system

Abstract

Provide a system. A method for receiving information input by a user; a means for obtaining the content of the information from a designated source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; a means for generating a report of the judgment result and its basis and providing it to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] Currently, fake news and fake videos using AI, as well as the spread of rumors on social media, are becoming widespread around the world, making it extremely difficult to determine the reliability of information. In this situation, there is a need for a system that allows users to quickly and accurately determine the veracity of information. Conventional methods require time-consuming manual verification and can be inefficient. It is also difficult to clearly communicate accurate judgments and their rationale to users. This calls for an automatic and efficient judgment method based on reliable information sources. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means.

[0006] First, it provides a means for receiving information entered by a user. Next, it provides a natural language processing means for retrieving the content of the information from a specified information source, processing the retrieved information, and extracting assertions and important keywords. This provides a means for comparing the extracted information with a reliable database to determine its reliability. Finally, it provides a means for generating a report that generates the determination result and its rationale and provides it to the user.

[0007] Specifically, the natural language processing means performs tokenization, stemming, and part-of-speech tagging to optimally extract the main points of the information. The reliability assessment means uses an algorithm that checks the consistency with information sources in a trusted database to provide an accurate reliability score. This allows users to quickly and accurately determine the authenticity of information and clearly understand the basis for that determination.

[0008] The following are definitions of important terms contained in the claims.

[0009] ---

[0010] "User" refers to any person or entity that uses the system to input information to determine its authenticity.

[0011] "Information" refers to content whose authenticity is to be determined, such as data, text, and links obtained from social media, news sites, and other media.

[0012] The "receiving means" refers to hardware and software components for acquiring information input by a user within the system and passing it on to the next processing step.

[0013] "Means of Acquisition" refers to scraping or API-based techniques to extract the required text or data from designated sources.

[0014] "Natural language processing means" refers to means for extracting key points and keywords from input information using natural language processing techniques such as tokenization (word division), stemming (word root extraction), and part-of-speech tagging.

[0015] "Means for determining reliability" refers to algorithms or technologies that compare extracted information with sources in a trusted database and determine the reliability of the information based on the degree of match.

[0016] "Means for creating a report" refers to a system component that generates a document or graphical format to present the judgment results and their rationale to the user in an easy-to-understand manner.

[0017] "Database" refers to an electronic data storage system for collecting and recording reliable information sources.

[0018] "Tokenization" refers to a natural language processing technique that breaks up large pieces of text into individual words or phrases.

[0019] "Stemming" refers to a natural language processing technique that extracts the roots of words and puts them into a unified form.

[0020] "Part-of-speech tagging" refers to a natural language processing technique that assigns each word a corresponding part of speech (verb, noun, adjective, etc.).

[0021] "Consistency checking algorithm" refers to a mathematical model or computational method that compares extracted information with information in a trusted database and evaluates their similarity or consistency. [Brief explanation of the drawings]

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

[0023] 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.

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

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

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

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

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0037] 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.

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

[0039] 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.

[0040] 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.

[0041] 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.

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

[0043] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[0044] System configuration

[0045] User terminal

[0046] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server.

[0047] server

[0048] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[0049] 1. URL analysis

[0050] Scrape the web page from the URL you receive and extract the article text.

[0051] 2. Natural Language Processing (NLP)

[0052] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[0053] 3. Reliability assessment

[0054] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[0055] 4. Report Generation

[0056] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[0057] Database

[0058] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0059] Program processing

[0060] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[0061] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[0062] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0063] Specific examples

[0064] Example: Scrutinizing news articles circulating on social media

[0065] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[0066] The server first receives the URL and then scrapes the web page to extract the article text.

[0067] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[0068] The extracted information is compared with a reliable database to assess the degree of match.

[0069] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0070] A report is generated and sent to the user terminal.

[0071] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[0075] Step 2:

[0076] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[0077] Step 3:

[0078] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[0079] Step 4:

[0080] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[0081] Step 5:

[0082] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques. This involves the following specific steps:

[0083] Tokenization: Breaking text into individual words or phrases.

[0084] Stemming: Extracting the roots of words.

[0085] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[0086] Step 6:

[0087] Server: Extracts important keywords and phrases from the NLP analysis results. Using algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency), it identifies words and phrases with high importance within the text.

[0088] Step 7:

[0089] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[0090] Step 8:

[0091] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[0092] Step 9:

[0093] Server: Consolidates the verdict and supporting information and generates a report, showing a confidence score as the verdict and including a list of trusted sources referenced as supporting information.

[0094] Step 10:

[0095] Server: Sends the generated report to the user's device. For example, it passes the report data to the device in JSON or HTML format.

[0096] Step 11:

[0097] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[0098] Step 12:

[0099] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[0100] Example 1

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

[0102] In today's information society, there is a huge amount of information on the Internet whose authenticity is unknown, and users are required to quickly and accurately judge it. However, it is not easy for users to verify the reliability of information themselves, as it requires a great deal of time and specialized knowledge. Furthermore, the spread of unreliable information could lead to social confusion and misunderstanding. Therefore, a system is needed that can efficiently and accurately determine the reliability of information provided by users and provide the results to users.

[0103] 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.

[0104] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; means for generating a report to provide to the user with the determination result and its basis; means for the user to input a news article URL and send the input URL; means for the server to scrape the web page from the URL and extract the article text; and means for the server to compare the extracted keywords or phrases with data in the trusted database and evaluate the degree of match, thereby enabling users to easily confirm the reliability of information.

[0105] "User" is a person or device that inputs information into the system and receives the results.

[0106] "Terminal" means a device through which a user inputs information and communicates with the system.

[0107] A "server" is a computer system that plays a key role in analyzing the information it receives and determining its trustworthiness.

[0108] "Database" means a storage medium within a system that contains data collected from reliable sources.

[0109] A "URL" is an address that identifies a web page on the Internet.

[0110] A "web page" is a page of information accessible via the Internet.

[0111] "Scraping" is a technique for automatically extracting specific data from web pages.

[0112] An "article" is a document or piece of textual information posted on a web page.

[0113] "Natural language processing" is a technology that analyzes text data and understands or processes its content.

[0114] "Tokenization" is the process of dividing text into units of words and phrases.

[0115] "Stemming" is the process of extracting the roots of words.

[0116] "Part-of-speech tagging" is the process of tagging each word in a text with a part of speech.

[0117] "Keywords" are words or phrases in a piece of text that are considered particularly important.

[0118] "Reliability" is a criterion for assessing whether information is trustworthy or not.

[0119] An "algorithm" is a formalized procedure or computational method for solving a particular problem.

[0120] "Report" means a report that includes the reliability assessment results and their rationale.

[0121] The present invention relates to a system for efficiently and accurately determining the reliability of information provided by a user and providing the result to the user. An embodiment of the system will be specifically described below.

[0122] This system mainly consists of a user terminal, a server, and a database.

[0123] User terminal

[0124] A user terminal is a device through which a user inputs information. Examples include a PC or smartphone. The user uses a browser to input the URL of a social networking site or news article and presses the send button. The user terminal provides an interface for sending the input URL to the server. An example of a prompt sentence is "Please paste the URL of the news article into the input field."

[0125] server

[0126] The server is responsible for analyzing the received information and determining its reliability. Specifically, it uses the following hardware and software:

[0127] URL analysis

[0128] The server uses libraries such as BeautifulSoup to scrape the web page from the received URL and extract the article text, allowing you to retrieve the required information from the specified source.

[0129] Natural Language Processing (NLP)

[0130] The server performs natural language processing on the extracted text, using libraries such as NLTK and Spacy to:

[0131] Tokenization: The process of dividing text into units of words and phrases.

[0132] Stemming: The process of extracting the roots of words.

[0133] Part-of-speech tagging: The process of tagging each word in a text with its part of speech.

[0134] This allows the system to extract the text's assertive parts and important keywords.

[0135] Reliability determination

[0136] The server compares the extracted keywords and phrases with sources in a reliable database. Specific algorithms and similarity calculations are used for the comparison. For example, the server evaluates the degree of similarity with sources included in Google Scholar or reliable news sites. Based on the results of this evaluation, the server assigns a numerical rating to the reliability of the text.

[0137] Report Generation

[0138] The server generates a report that integrates the results of the reliability evaluation and their rationale, and provides it to the user. The report includes the reliability value, matching sources, and keyword analysis results. The generated report is sent to the user's device.

[0139] Database

[0140] The database contains data collected from trusted sources, including trusted news sites, academic papers, etc. The server consults this database when determining the trustworthiness of information.

[0141] Specific examples

[0142] Consider a scenario where a user copies and pastes the URL of a news article they found on a social networking site into an input field on the system and presses the send button.

[0143] 1. Enter the URL on the user's device and send it

[0144] The user enters the URL of a news article and presses the send button.

[0145] 2. Receive URL and extract text on the server

[0146] The server scrapes the received URL and extracts the article text.

[0147] 3. Perform natural language processing (NLP) on the server

[0148] The server performs natural language processing on the extracted text to extract important keywords and assertive parts.

[0149] 4. The server determines the reliability

[0150] The server matches the extracted keywords with sources in a trusted database and evaluates the degree of match.

[0151] 5. Generate and send the report on the server

[0152] The server generates a report summarizing the evaluation results and their rationale and sends it to the user's terminal.

[0153] 6. Display the report on the user's device

[0154] The received report is displayed on the user's terminal, and the user can check the reliability of the news article and its basis.

[0155] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1:

[0158] Entering and submitting a URL

[0159] The user enters the URL of a news article into the system's input field using a computer or smartphone browser and presses the send button.

[0160] Input: News article URL

[0161] Output: A request to send the URL to the server

[0162] Specifically, users copy the URL of an article they find on a social networking site or news site, paste it into the system's input field, and then click the send button, which sends the URL to the server.

[0163] Step 2:

[0164] Receiving URLs and scraping web pages

[0165] The server receives the URL sent from the user terminal and extracts the content of the web page using a scraping tool (e.g., BeautifulSoup).

[0166] Input: URL sent by user

[0167] Output: Extracted article text

[0168] Specifically, the server checks whether the received URL is in a valid format, analyzes the HTML structure, and <article>Extracts article text from a specified selector, such as a tag.

[0169] Step 3:

[0170] Natural language processing of extracted text

[0171] The server analyzes the extracted article text using natural language processing technology.

[0172] Input: Article text extracted by scraping

[0173] Output: Tokenized text, stemmed word list, part-of-speech tagged text

[0174] Specifically, the server uses the NLTK and Spacy libraries to perform the following tasks:

[0175] Tokenization: Splitting text into words and phrases. For example, splitting "Climate change will become serious" into "Climate change", "but", and "become serious".

[0176] Stemming: Extracting the root of a word. Example: "become serious" becomes "serious."

[0177] Part-of-speech tagging: Assign a part-of-speech tag to each word, e.g., "climate change / noun", "ga / particle", "serious / adjective", "naru / verb".

[0178] Step 4:

[0179] Determining reliability

[0180] The server matches the extracted keywords and phrases with sources in a trusted database and evaluates the degree of match.

[0181] Input: A list of tokenized and stemmed keywords

[0182] Output: Numerical evaluation of reliability, matching result data

[0183] Specifically, the server evaluates the degree of match between keywords and phrases and reliable sources in the database (e.g., academic paper databases or reliable news sites). It then uses a similarity calculation algorithm to quantify the degree of match and makes a judgment, such as "if the degree of match is 80% or higher, the confidence level is 90%."

[0184] Step 5:

[0185] Generate and send reports

[0186] The server generates a report that integrates the results of the reliability judgment and the basis for it, and transmits it to the user terminal.

[0187] Input: Numerical evaluation of reliability, matching result data

[0188] Output: Generated report, request to send to user terminal

[0189] Specifically, the server creates a report based on the results of the reliability assessment. The report includes the reliability value, the matching results, and the extracted keywords. The report is then sent to the user's device.

[0190] Step 6:

[0191] Viewing Reports

[0192] The user terminal receives the report sent from the server and displays it to the user.

[0193] Input: Report sent from the server

[0194] Output: Report displayed in a user-readable format

[0195] Specifically, the user device displays the received report on a web page, where the user can check the reliability of the news article and its basis. For example, it may state that the reliability is 90% and that multiple reliable sources agree.

[0196] (Application example 1)

[0197] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0198] The Internet is overflowing with news articles and information, making it difficult to quickly and accurately assess their reliability. This puts users at risk of being misled by unreliable information and making incorrect decisions. Users of mobile devices, such as smartphones, lack easy ways to verify information's reliability. This invention aims to solve these problems and enable users to make decisions based on accurate information.

[0199] 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.

[0200] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a reliable database to determine its reliability; means for generating a report of the determination result and its basis and providing it to the user; means for having an interface applicable to a mobile device and operable directly by the user from the device; and means for having an application for analyzing the URL of a news article to determine its reliability and providing the result. This allows users to easily check the reliability of news articles on their mobile devices such as smartphones.

[0201] "Means for receiving information entered by the user" refers to the interface or form that allows a user to enter information such as the URL of a news article using a device such as a smartphone or PC.

[0202] "Means of obtaining the content of the information from a specified source" refers to a method of extracting article text from a web page on the Internet using web scraping technology, etc., based on the URL entered by the user.

[0203] "Natural language processing means for processing acquired information and extracting assertive parts and important keywords" refers to technology that applies natural language processing techniques such as tokenization, stemming, and part-of-speech tagging to acquired article text to extract important keywords and phrases and analyze the article content.

[0204] "Methods of determining reliability by comparing with a reliable database" refers to a method of determining the reliability of an article by comparing extracted keywords and phrases with a database containing reliable information sources and using an algorithm to evaluate the degree of match.

[0205] "Means for generating a report that provides the results of a reliability assessment and their rationale, and presents it to the user" refers to a technology that integrates the results of a reliability assessment and their rationale, generates a report that can be presented to the user in an easy-to-understand manner, and sends it to the user's terminal.

[0206] "Means that have an interface applicable to mobile devices and allow users to operate directly from the device" refers to a user interface that can be operated on mobile devices such as smartphones and tablets. This is implemented as a smartphone application, allowing users to easily operate it.

[0207] "Means having an application that analyzes the URL of a news article, determines its reliability, and provides the results" refers to an application that has the function of automatically analyzing the URL of a news article entered by a user, determining its reliability, and providing the results to the user.

[0208] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[0209] A user terminal is a device into which a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a smartphone or PC browser. The user uses a smartphone application to input the URL of a news article and presses the send button. This sends the URL to the server.

[0210] The server plays the primary role of analyzing the received information and determining its reliability. Specifically, it first scrapes the web page from the received URL and extracts the article text. This is done using a Python library called "BeautifulSoup." Next, natural language processing techniques are used on this text to perform tokenization, stemming, and part-of-speech tagging. In this case, a natural language processing library called "spaCy" is used. This allows important keywords and phrases to be extracted.

[0211] The server then compares the extracted keywords and phrases with information sources in a trusted database and evaluates the degree of match. The trusted database stores reliable news sites, academic papers, etc. The server then uses this information to determine the reliability of the information. For example, if a claim in the text matches multiple reliable sources in the database, the server evaluates the reliability of the claim.

[0212] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0213] As a concrete example, let's say a user enters the URL "https: / / example.com / news-article". The News Verifier application scrapes the article from this URL, extracts the text, and analyzes it. As a result of the analysis, certain keywords are extracted and compared with trusted database information. If the result is 80% confidence, the following report will be generated:

[0214] Example prompt sentence:

[0215] Check the authenticity of this URL with the News Verifier app: https: / / example.com / news-article

[0216] Generated response:

[0217] Confidence: 80%

[0218] Evidence: 80% match with trusted sources in our database. This article can be treated as relatively reliable information.

[0219] This allows users to easily determine the reliability of news articles for themselves.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] A user opens the News Verifier app, enters the URL of a news article, and presses the send button. The user's device sends this URL to the server. The input is the URL entered by the user, and the output is the URL sent to the server. Specific operations include the user entering the URL from the application screen on their smartphone.

[0223] Step 2:

[0224] The server uses the received URL to scrape the web page. Here, we use "BeautifulSoup." The input is the URL sent in step 1, and the output is the article text of the web page. The specific operation involves the server accessing the specified URL and extracting the article text from the HTML code.

[0225] Step 3:

[0226] The server performs natural language processing on the extracted article text. Here, it uses "spaCy" to perform tokenization, stemming, and part-of-speech tagging. The input is the article text extracted in step 2, and the output is a list of important keywords and phrases. Specifically, it splits the text into individual words, returns it to its original form, and tags it for parts of speech.

[0227] Step 4:

[0228] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. It uses a proprietary algorithm. The input is the list of keywords and phrases obtained in step 3, and the output is a score evaluating the degree of match. Specifically, it retrieves relevant information from the database, compares it with the keywords and phrases, and calculates the degree of match.

[0229] Step 5:

[0230] The server generates a report that integrates the reliability judgment results and their rationale based on the matching evaluation score. The input is the matching evaluation score obtained in step 4, and the output is a report to be provided to the user. Specifically, the server creates a reliability value and rationale text based on the evaluation score, and compiles these into a single report.

[0231] Step 6:

[0232] The generated report is sent to the user's terminal, where the user views it. The input is the report generated in step 5, and the output is the report displayed on the user's terminal. Specific operations include the server sending the report to the user's terminal and the user viewing the report on the screen of their smartphone.

[0233] 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.

[0234] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, an emotion engine, and a database.

[0235] System configuration

[0236] User terminal

[0237] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server. If necessary, the input data can also be sent to an emotion engine for analyzing the user's emotions.

[0238] server

[0239] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[0240] 1. URL analysis

[0241] Scrape the web page from the URL you receive and extract the article text.

[0242] 2. Natural Language Processing (NLP)

[0243] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[0244] 3. Reliability assessment

[0245] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[0246] 4. Report Generation

[0247] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[0248] Emotion Engine

[0249] The emotion engine analyzes the information and behavioral patterns entered by the user and has the function of recognizing the user's emotions. Specifically, it has the following functions:

[0250] 1. Emotion analysis

[0251] The system analyzes the text and behavioral data entered by the user and determines their emotions using natural language processing technology.

[0252] 2. Relevance Analysis

[0253] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[0254] 3. Report customization

[0255] Customize the results and report content based on the recognized user sentiment.

[0256] Database

[0257] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0258] Program processing

[0259] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[0260] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[0261] Furthermore, an emotion engine analyzes the text entered by the user to determine their emotion. For example, it recognizes various emotions such as positive, negative, surprise, and anger. Based on the recognized emotion, the report content is customized. In the case of a positive emotion, parts with many highly reliable sources are highlighted, while in the case of a negative emotion, uncertain elements are explained in detail.

[0262] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0263] Specific examples

[0264] Example: Scrutinizing news articles circulating on social media

[0265] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[0266] The server first receives the URL and then scrapes the web page to extract the article text.

[0267] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[0268] The extracted information is compared with a reliable database to assess the degree of match.

[0269] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0270] Furthermore, the emotion engine analyzes the data entered by the user and determines the user's emotion.

[0271] Moderately customize report content based on user sentiment.

[0272] A report is generated and sent to the user terminal.

[0273] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information. It also recognizes users' emotions and customizes the report content, enabling more personalized information provision.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[0277] Step 2:

[0278] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[0279] Step 3:

[0280] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[0281] Step 4:

[0282] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[0283] Step 5:

[0284] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques, specifically by following these steps:

[0285] Tokenization: Breaking text into individual words or phrases.

[0286] Stemming: Extracting the roots of words.

[0287] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[0288] Step 6:

[0289] Server: Extracts important keywords and phrases from the NLP analysis results, for example, using Term Frequency-Inverse Document Frequency (TF-IDF) to identify important words and phrases in the text.

[0290] Step 7:

[0291] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[0292] Step 8:

[0293] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[0294] Step 9:

[0295] Server: Integrates the judgement results and evidence information and generates a report, which includes a confidence score and a list of referenced sources.

[0296] Step 10:

[0297] Server: Sends the text entered by the user to the emotion engine for sentiment analysis, for example, analyzing user comments and reactions to articles.

[0298] Step 11:

[0299] Server: The emotion engine determines the user's emotions. For example, it uses natural language processing technology to recognize positive, negative, and other emotions.

[0300] Step 12:

[0301] Server: Customize report content based on perceived user sentiment, e.g., detailing low-trust sources if negative sentiment is detected.

[0302] Step 13:

[0303] Server: Sends customized reports to user devices.

[0304] Step 14:

[0305] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[0306] Step 15:

[0307] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[0308] Example 2

[0309] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] As the amount of information on the Internet increases, users need to be able to quickly and accurately evaluate the reliability of the information they see. However, current systems are insufficient in assessing reliability, which puts users at risk of believing incorrect information. Furthermore, information provided does not take into account the user's emotions, resulting in a lack of improvement in the user experience.

[0311] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving information input by a user, means for acquiring the content of the information from a specified information source, language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable storage device to determine its reliability, means for analyzing the information and behavioral patterns input by the user and determining the user's emotions, and means for generating a determination result and its basis and creating a report to be provided to the user. This allows the user to quickly and accurately evaluate the reliability of information, and also makes it possible to provide a personalized report based on the user's emotions.

[0312] The "means for receiving information input by the user" is a function for receiving URLs and text data provided by the user via the input interface.

[0313] "Means of obtaining the content of the information from the specified source" refers to the function of obtaining the target web page or data from the input URL or specified source using scraping or other techniques.

[0314] "Language processing means for processing acquired information and extracting assertive parts and important keywords" refers to a function that applies natural language processing technology to the extracted text, performing tokenization, stemming, and part-of-speech tagging to extract assertive parts and important keywords.

[0315] "Means for comparing extracted information with trusted storage to determine trustworthiness" refers to a function that uses algorithms and databases to compare extracted keywords and phrases with trusted sources, evaluate the degree of match, and determine the trustworthiness of the information.

[0316] "Means for analyzing information entered by the user and behavioral patterns to determine the user's emotions" refers to a function that analyzes the user's input and behavioral data and uses natural language processing technology to recognize the user's emotions.

[0317] "Means for generating a report that generates a judgment result and its basis and provides it to the user" is a function that compiles an evaluation of the reliability of information and its basis, and generates a report that can be provided to the user in an easy-to-understand manner.

[0318] The present invention is a system that automatically determines the reliability of information entered by a user and provides the user with an appropriate report. This system is composed of a user terminal, a server, an emotion analysis engine, and a database.

[0319] User terminal

[0320] A user terminal is a device through which a user inputs information via input means. For example, a news article or a URL of a social networking site can be entered from the browser of a personal computer or smartphone. The user terminal provides an interface for sending the entered information to a server. It can also send data to an emotion engine to analyze the user's emotions as needed.

[0321] server

[0322] The server performs the primary function of analyzing the received information and determining its reliability. Specifically, it uses the following software and technologies:

[0323] 1. Web page scraping

[0324] The server scrapes the web page from the URL received from the user and extracts the article text, using software such as "BeautifulSoup" or "Scrapy."

[0325] 2. Natural Language Processing (NLP)

[0326] The server tokenizes the extracted article text using NLTK and spaCy, and performs stemming and part-of-speech tagging to extract important keywords and phrases.

[0327] 3. Reliability assessment

[0328] The server uses ElasticSearch (registered trademark) to compare extracted keywords and phrases with reliable information sources and evaluate the degree of match. Based on the evaluation results, the server determines the reliability.

[0329] 4. Report Generation

[0330] The server aggregates the results and rationale of the confidence assessment and generates a report to provide to the user.

[0331] Emotion Engine

[0332] The emotion engine has the function of analyzing the information and behavioral patterns entered by the user and recognizing the user's emotions. Specifically, it uses the following technologies:

[0333] 1. Emotion analysis

[0334] The emotion engine uses IBM Watson (registered trademark) NLU and Google Cloud Natural Language API to recognize emotions from the user's input text.

[0335] 2. Relevance Analysis

[0336] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[0337] 3. Report customization

[0338] Customize the report content based on the sentiment analysis results, for example highlighting reliable sources for positive sentiment and providing detailed explanations of uncertainties for negative sentiment.

[0339] Database

[0340] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0341] Specific examples

[0342] When a user pastes the URL of a news article into the system's input field and presses the send button, the following steps occur:

[0343] The server receives the URL and scrapes the web page to extract the article text.

[0344] The server uses natural language processing technology to analyze the text and extract important keywords and key points.

[0345] The server compares the extracted information with a trusted database and evaluates the degree of match.

[0346] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0347] The emotion engine analyzes the user's input data and determines the user's emotion.

[0348] The report content is appropriately customized based on the user's sentiment.

[0349] Finally, the report is sent to the user terminal and the user confirms it.

[0350] Prompt Sentence Examples

[0351] By entering the URL of an article along with the prompt "Please rate the credibility of this news article:", the system performs text analysis, credibility rating, sentiment analysis, etc. on the article and provides the results.

[0352] This allows users to easily check the reliability of news articles and social media information, enabling them to make appropriate decisions. Furthermore, the report content is customized based on the user's emotions, achieving more personalized information provision.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1:

[0355] User

[0356] The user enters the URL of a news article into the input field of the device and presses the send button. The user opens a browser, copies the URL of an article of interest from a social networking site or news site, and pastes it into the input field of the system. Then, the user clicks the send button. As a result, the input information is the URL of the news article, and the output is an HTTP request.

[0357] Step 2:

[0358] Terminal

[0359] The device sends the URL entered by the user to the server. When the user presses the send button, the device's browser generates an HTTP request and sends it to the specified server. The input in this step is the news article URL and the HTTP request, and the output is the URL sent to the server.

[0360] Step 3:

[0361] server

[0362] The server scrapes the web page from the received URL and extracts the article text. The server uses "BeautifulSoup" or "Scrapy" to get the web page content from the URL and extract the article text. The input in this step is the URL and the output is the extracted article text.

[0363] Step 4:

[0364] server

[0365] The server applies natural language processing to the extracted article text. Using NLTK or spaCy, the server tokenizes the article text and performs stemming and part-of-speech tagging. This allows important keywords and phrases to be extracted. The input for this step is the article text, and the output is keywords and phrases.

[0366] Step 5:

[0367] server

[0368] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. The server uses Elasticsearch to compare the keywords and phrases with trusted sources in the database and calculate the degree of match. The input for this step is the keywords and phrases and the database information, and the output is the result of the match assessment.

[0369] Step 6:

[0370] Emotion Engine

[0371] The emotion engine analyzes the information and behavioral patterns entered by the user to determine the user's emotion. The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from the user's input text. The input for this step is the user's input data, and the output is the recognized emotion.

[0372] Step 7:

[0373] server

[0374] The server generates a report based on the reliability evaluation results and sentiment analysis results. The server integrates the reliability evaluation results with the emotion engine analysis results and creates a report for the user. The report contains the reliability value and its rationale. The inputs to this step are the reliability evaluation results and sentiment analysis results, and the output is a report.

[0375] Step 8:

[0376] Emotion Engine

[0377] The emotion engine customizes the report content based on the user's emotion. The emotion engine adjusts the report depending on the user's emotion. For example, a positive emotion might highlight areas of high confidence, while a negative emotion might provide more detailed explanations of uncertainties. The inputs to this step are the report and the user's emotion, and the output is a customized report.

[0378] Step 9:

[0379] server

[0380] The server sends the generated report to the user terminal. The server returns the generated report to the user terminal as an HTTP response. The report is then displayed in the browser on the user terminal. The input of this step is the report, and the output is the sent report.

[0381] Step 10:

[0382] Terminal

[0383] The terminal displays the received report. The browser on the user's terminal analyzes the contents of the report received from the server and displays it on the screen. The user can check this and understand the reliability of the article and its basis. The input of this step is the report, and the output is the displayed report.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] Internet advertisements often contain information of questionable reliability, making it difficult for users to judge such information. This problem is particularly serious in today's world, where fraud and fake news are easily spread. The present invention aims to solve this problem by providing a system that efficiently and accurately judges the reliability of advertising information viewed by users on the Internet and presents the results and evidence.

[0387] The identification process by the identification 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 means for receiving information input by a user, means for acquiring the content of the information from a specified information source, natural language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable database to determine its reliability, means for generating a report to provide the user with the determination result and its basis, and means for determining the reliability of advertisements in real time and providing the determination result. This allows users to accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[0388] A "user" is a person or entity that utilizes the system to enter and receive information.

[0389] "Information" refers to data such as URLs and text data entered by users, and the contents of web pages retrieved by the system.

[0390] A "designated source" is a pre-defined source, such as a website or database, that is referenced for assessing trustworthiness.

[0391] "Natural language processing" is a technology that analyzes input text data using algorithms such as tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases.

[0392] "Reliability" is a numerical value or index that evaluates the degree to which extracted information matches a reliable database.

[0393] A "database" is a collection of information that stores data collected from reliable sources.

[0394] "Report creation" refers to the creation of documents and presentation formats that integrate the results and their rationale for analysis and evaluation results in order to provide them to users.

[0395] "Advertising" means information or content displayed on the Internet for the purpose of promoting a product or service.

[0396] A "URL" is a string of characters that specifies the location of a web page and points to an Internet resource such as an advertisement or news article.

[0397] "Analysis" is the process of breaking down acquired information using algorithms and methods to understand and evaluate its structure and content.

[0398] The "judgment result" is an evaluation or judgment obtained as a result of the analysis and reliability judgment.

[0399] "Real-time" refers to the time characteristics of a process in which information is obtained and evaluated immediately.

[0400] Overall system configuration

[0401] The present invention provides a system that efficiently and accurately determines the reliability of information entered by a user and presents the results and the basis for the determination. The system is composed of a user terminal, a server, and multiple components. The system will be described in detail below.

[0402] User terminal

[0403] The user terminal is a device through which the user inputs information and receives the judgment results. For example, the user inputs the URL of the advertisement page using a smartphone or a PC.

[0404] server

[0405] The server is the central information processing center. It provides the following main functions:

[0406] Data Acquisition Method

[0407] It scrapes web pages from URLs entered by users and extracts the text from those pages. The hardware used is a high-performance server, and the software uses the widely used Python requests and BeautifulSoup libraries.

[0408] Natural language processing tools

[0409] The acquired text is then subjected to natural language processing (NLP) to extract important keywords and phrases. This processing is performed using a NLP library such as NLTK, which performs tokenization, stemming, and part-of-speech tagging.

[0410] Reliability determination means

[0411] The extracted keywords and phrases are compared with a reliable database to evaluate their reliability based on the degree of match. This process uses a database collected from pre-prepared, reliable sources. The algorithm used is a match check algorithm.

[0412] Emotion analysis means

[0413] The system analyzes user sentiment based on the captured text. For sentiment analysis, it uses the VADER Sentiment library, which calculates a positive, negative, or neutral sentiment score and reflects it in the report.

[0414] Report Creation Method

[0415] The results of the confidence assessment and sentiment analysis are integrated to generate a report and provided to the user using a custom Python script.

[0416] Specific examples

[0417] For example, the following shows the process when a user views an advertisement on a smartphone and enters the URL of the advertisement page.

[0418] A user enters the URL of an ad page in an application on their smartphone. When they press the submit button, the URL is sent to the server, which then scrapes the web page from the URL to extract text. Next, natural language processing is performed on the extracted text to analyze important keywords and phrases. This is then compared with a trusted database to assess its trustworthiness. Sentiment analysis is also performed in parallel, and a report based on the user's sentiment is generated. Finally, a trustworthiness report is displayed on the user's device, allowing the user to review the results.

[0419] Prompt Sentence Examples

[0420] The user inputs the URL of a news article. The system scrapes the web page from the input URL and extracts the text. It then uses natural language processing technology to extract important keywords from that text. It then compares the reliability of the article against a trusted database and generates a report based on the results. Finally, the generated report is sent to the user's device and displayed.

[0421] According to this invention, users can accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[0422] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0423] Step 1:

[0424] The user inputs the URL of the advertisement page using a smartphone application. The input URL is sent from the user's device to the server. Input: URL of advertisement page, Output: URL sent to the server.

[0425] Step 2:

[0426] The server scrapes the web page from the received URL and extracts the text from that page. Specifically, it retrieves the web page using Python's requests library and extracts the text from the HTML using the BeautifulSoup library. Input: URL of the ad page, Output: Text of the ad page.

[0427] Step 3:

[0428] The server performs natural language processing on the extracted text. It uses Python's NLTK library to perform tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases. Input: text from the ad page. Output: important keywords and phrases.

[0429] Step 4:

[0430] The server checks the extracted keywords and phrases against a trusted database, calculates the degree of match, and determines the confidence score. A specific algorithm is used to evaluate whether there is a match. Input: extracted keywords and phrases, database. Output: confidence score.

[0431] Step 5:

[0432] The server performs sentiment analysis based on the extracted text. It uses the VADER Sentiment library to calculate a positive, negative, or neutral sentiment score. Input: text of the ad page. Output: sentiment score.

[0433] Step 6:

[0434] The server integrates the results of the confidence evaluation and sentiment analysis to generate a report. The report includes confidence scores, sentiment scores, and their rationales. Input: confidence scores, sentiment scores. Output: confidence report.

[0435] Step 7:

[0436] The generated report is sent from the server to the user's device. The user can check the report on their smartphone and evaluate the reliability of the advertising information. Input: Reliability report, Output: Report displayed on the user's device.

[0437] In this way, by performing each step sequentially, the user can efficiently and accurately evaluate the reliability of the advertising information viewed on the Internet, and can use it with peace of mind.

[0438] 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.

[0439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] 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.

[0441] [Second embodiment]

[0442] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0454] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[0455] System configuration

[0456] User terminal

[0457] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server.

[0458] server

[0459] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[0460] 1. URL analysis

[0461] Scrape the web page from the URL you receive and extract the article text.

[0462] 2. Natural Language Processing (NLP)

[0463] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[0464] 3. Reliability assessment

[0465] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[0466] 4. Report Generation

[0467] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[0468] Database

[0469] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0470] Program processing

[0471] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[0472] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[0473] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0474] Specific examples

[0475] Example: Scrutinizing news articles circulating on social media

[0476] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[0477] The server first receives the URL and then scrapes the web page to extract the article text.

[0478] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[0479] The extracted information is compared with a reliable database to assess the degree of match.

[0480] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0481] A report is generated and sent to the user terminal.

[0482] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information.

[0483] The processing flow will be explained below.

[0484] Step 1:

[0485] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[0486] Step 2:

[0487] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[0488] Step 3:

[0489] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[0490] Step 4:

[0491] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[0492] Step 5:

[0493] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques. This involves the following specific steps:

[0494] Tokenization: Breaking text into individual words or phrases.

[0495] Stemming: Extracting the roots of words.

[0496] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[0497] Step 6:

[0498] Server: Extracts important keywords and phrases from the NLP analysis results. Using algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency), it identifies words and phrases with high importance within the text.

[0499] Step 7:

[0500] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[0501] Step 8:

[0502] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[0503] Step 9:

[0504] Server: Consolidates the verdict and supporting information and generates a report, showing a confidence score as the verdict and including a list of trusted sources referenced as supporting information.

[0505] Step 10:

[0506] Server: Sends the generated report to the user's device. For example, it passes the report data to the device in JSON or HTML format.

[0507] Step 11:

[0508] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[0509] Step 12:

[0510] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[0511] Example 1

[0512] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0513] In today's information society, there is a huge amount of information on the Internet whose authenticity is unknown, and users are required to quickly and accurately judge it. However, it is not easy for users to verify the reliability of information themselves, as it requires a great deal of time and specialized knowledge. Furthermore, the spread of unreliable information could lead to social confusion and misunderstanding. Therefore, a system is needed that can efficiently and accurately determine the reliability of information provided by users and provide the results to users.

[0514] 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.

[0515] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; means for generating a report to provide to the user with the determination result and its basis; means for the user to input a news article URL and send the input URL; means for the server to scrape the web page from the URL and extract the article text; and means for the server to compare the extracted keywords or phrases with data in the trusted database and evaluate the degree of match, thereby enabling users to easily confirm the reliability of information.

[0516] "User" is a person or device that inputs information into the system and receives the results.

[0517] "Terminal" means a device through which a user inputs information and communicates with the system.

[0518] A "server" is a computer system that plays a key role in analyzing the information it receives and determining its trustworthiness.

[0519] "Database" means a storage medium within a system that contains data collected from reliable sources.

[0520] A "URL" is an address that identifies a web page on the Internet.

[0521] A "web page" is a page of information accessible via the Internet.

[0522] "Scraping" is a technique for automatically extracting specific data from web pages.

[0523] An "article" is a document or piece of textual information posted on a web page.

[0524] "Natural language processing" is a technology that analyzes text data and understands or processes its content.

[0525] "Tokenization" is the process of dividing text into units of words and phrases.

[0526] "Stemming" is the process of extracting the roots of words.

[0527] "Part-of-speech tagging" is the process of tagging each word in a text with a part of speech.

[0528] "Keywords" are words or phrases in a piece of text that are considered particularly important.

[0529] "Reliability" is a criterion for assessing whether information is trustworthy or not.

[0530] An "algorithm" is a formalized procedure or computational method for solving a particular problem.

[0531] "Report" means a report that includes the reliability assessment results and their rationale.

[0532] The present invention relates to a system for efficiently and accurately determining the reliability of information provided by a user and providing the result to the user. An embodiment of the system will be specifically described below.

[0533] This system mainly consists of a user terminal, a server, and a database.

[0534] User terminal

[0535] A user terminal is a device through which a user inputs information. Examples include a PC or smartphone. The user uses a browser to input the URL of a social networking site or news article and presses the send button. The user terminal provides an interface for sending the input URL to the server. An example of a prompt sentence is "Please paste the URL of the news article into the input field."

[0536] server

[0537] The server is responsible for analyzing the received information and determining its reliability. Specifically, it uses the following hardware and software:

[0538] URL analysis

[0539] The server uses libraries such as BeautifulSoup to scrape the web page from the received URL and extract the article text, allowing you to retrieve the required information from the specified source.

[0540] Natural Language Processing (NLP)

[0541] The server performs natural language processing on the extracted text, using libraries such as NLTK and Spacy to:

[0542] Tokenization: The process of dividing text into units of words and phrases.

[0543] Stemming: The process of extracting the roots of words.

[0544] Part-of-speech tagging: The process of tagging each word in a text with its part of speech.

[0545] This allows the system to extract the text's assertive parts and important keywords.

[0546] Reliability determination

[0547] The server then matches the extracted keywords and phrases with sources in a trusted database. Specific algorithms and similarity calculations are used for matching. For example, the server evaluates the degree of similarity with sources included in Google Scholar or trusted news sites. Based on the results of this evaluation, the server assigns a numerical rating to the reliability of the text.

[0548] Report Generation

[0549] The server generates a report that integrates the results of the reliability evaluation and their rationale, and provides it to the user. The report includes the reliability value, matching sources, and keyword analysis results. The generated report is sent to the user's device.

[0550] Database

[0551] The database contains data collected from trusted sources, including trusted news sites, academic papers, etc. The server consults this database when determining the trustworthiness of information.

[0552] Specific examples

[0553] Consider a scenario where a user copies and pastes the URL of a news article they found on a social networking site into an input field on the system and presses the send button.

[0554] 1. Enter the URL on the user's device and send it

[0555] The user enters the URL of a news article and presses the send button.

[0556] 2. Receive URL and extract text on the server

[0557] The server scrapes the received URL and extracts the article text.

[0558] 3. Perform natural language processing (NLP) on the server

[0559] The server performs natural language processing on the extracted text to extract important keywords and assertive parts.

[0560] 4. The server determines the reliability

[0561] The server matches the extracted keywords with sources in a trusted database and evaluates the degree of match.

[0562] 5. Generate and send the report on the server

[0563] The server generates a report summarizing the evaluation results and their rationale and sends it to the user's terminal.

[0564] 6. Display the report on the user's device

[0565] The received report is displayed on the user's terminal, and the user can check the reliability of the news article and its basis.

[0566] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions.

[0567] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0568] Step 1:

[0569] Entering and submitting a URL

[0570] The user enters the URL of a news article into the system's input field using a computer or smartphone browser and presses the send button.

[0571] Input: News article URL

[0572] Output: A request to send the URL to the server

[0573] Specifically, users copy the URL of an article they find on a social networking site or news site, paste it into the system's input field, and then click the send button, which sends the URL to the server.

[0574] Step 2:

[0575] Receiving URLs and scraping web pages

[0576] The server receives the URL sent from the user terminal and extracts the content of the web page using a scraping tool (e.g., BeautifulSoup).

[0577] Input: URL sent by user

[0578] Output: Extracted article text

[0579] Specifically, the server checks whether the received URL is in a valid format, analyzes the HTML structure, and <article>Extracts article text from a specified selector, such as a tag.

[0580] Step 3:

[0581] Natural language processing of extracted text

[0582] The server analyzes the extracted article text using natural language processing technology.

[0583] Input: Article text extracted by scraping

[0584] Output: Tokenized text, stemmed word list, part-of-speech tagged text

[0585] Specifically, the server uses the NLTK and Spacy libraries to perform the following tasks:

[0586] Tokenization: Splitting text into words and phrases. For example, splitting "Climate change will become serious" into "Climate change", "but", and "become serious".

[0587] Stemming: Extracting the root of a word. Example: "become serious" becomes "serious."

[0588] Part-of-speech tagging: Assign a part-of-speech tag to each word, e.g., "climate change / noun", "ga / particle", "serious / adjective", "naru / verb".

[0589] Step 4:

[0590] Determining reliability

[0591] The server matches the extracted keywords and phrases with sources in a trusted database and evaluates the degree of match.

[0592] Input: A list of tokenized and stemmed keywords

[0593] Output: Numerical evaluation of reliability, matching result data

[0594] Specifically, the server evaluates the degree of match between keywords and phrases and reliable sources in the database (e.g., academic paper databases or reliable news sites). It then uses a similarity calculation algorithm to quantify the degree of match and makes a judgment, such as "if the degree of match is 80% or higher, the confidence level is 90%."

[0595] Step 5:

[0596] Generate and send reports

[0597] The server generates a report that integrates the results of the reliability judgment and the basis for it, and transmits it to the user terminal.

[0598] Input: Numerical evaluation of reliability, matching result data

[0599] Output: Generated report, request to send to user terminal

[0600] Specifically, the server creates a report based on the results of the reliability assessment. The report includes the reliability value, the matching results, and the extracted keywords. The report is then sent to the user's device.

[0601] Step 6:

[0602] Viewing Reports

[0603] The user terminal receives the report sent from the server and displays it to the user.

[0604] Input: Report sent from the server

[0605] Output: Report displayed in a user-readable format

[0606] Specifically, the user device displays the received report on a web page, where the user can check the reliability of the news article and its basis. For example, it may state that the reliability is 90% and that multiple reliable sources agree.

[0607] (Application example 1)

[0608] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0609] The Internet is overflowing with news articles and information, making it difficult to quickly and accurately assess their reliability. This puts users at risk of being misled by unreliable information and making incorrect decisions. Users of mobile devices, such as smartphones, lack easy ways to verify information's reliability. This invention aims to solve these problems and enable users to make decisions based on accurate information.

[0610] 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.

[0611] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a reliable database to determine its reliability; means for generating a report of the determination result and its basis and providing it to the user; means for having an interface applicable to a mobile device and operable directly by the user from the device; and means for having an application for analyzing the URL of a news article to determine its reliability and providing the result. This allows users to easily check the reliability of news articles on their mobile devices such as smartphones.

[0612] "Means for receiving information entered by the user" refers to the interface or form that allows a user to enter information such as the URL of a news article using a device such as a smartphone or PC.

[0613] "Means of obtaining the content of the information from a specified source" refers to a method of extracting article text from a web page on the Internet using web scraping technology, etc., based on the URL entered by the user.

[0614] "Natural language processing means for processing acquired information and extracting assertive parts and important keywords" refers to technology that applies natural language processing techniques such as tokenization, stemming, and part-of-speech tagging to acquired article text to extract important keywords and phrases and analyze the article content.

[0615] "Methods of determining reliability by comparing with a reliable database" refers to a method of determining the reliability of an article by comparing extracted keywords and phrases with a database containing reliable information sources and using an algorithm to evaluate the degree of match.

[0616] "Means for generating a report that provides the results of a reliability assessment and their rationale, and presents it to the user" refers to a technology that integrates the results of a reliability assessment and their rationale, generates a report that can be presented to the user in an easy-to-understand manner, and sends it to the user's terminal.

[0617] "Means that have an interface applicable to mobile devices and allow users to operate directly from the device" refers to a user interface that can be operated on mobile devices such as smartphones and tablets. This is implemented as a smartphone application, allowing users to easily operate it.

[0618] "Means having an application that analyzes the URL of a news article, determines its reliability, and provides the results" refers to an application that has the function of automatically analyzing the URL of a news article entered by a user, determining its reliability, and providing the results to the user.

[0619] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[0620] A user terminal is a device into which a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a smartphone or PC browser. The user uses a smartphone application to input the URL of a news article and presses the send button. This sends the URL to the server.

[0621] The server plays the primary role of analyzing the received information and determining its reliability. Specifically, it first scrapes the web page from the received URL and extracts the article text. This is done using a Python library called "BeautifulSoup." Next, natural language processing techniques are used on this text to perform tokenization, stemming, and part-of-speech tagging. In this case, a natural language processing library called "spaCy" is used. This allows important keywords and phrases to be extracted.

[0622] The server then compares the extracted keywords and phrases with information sources in a trusted database and evaluates the degree of match. The trusted database stores reliable news sites, academic papers, etc. The server then uses this information to determine the reliability of the information. For example, if a claim in the text matches multiple reliable sources in the database, the server evaluates the reliability of the claim.

[0623] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0624] As a concrete example, let's say a user enters the URL "https: / / example.com / news-article". The News Verifier application scrapes the article from this URL, extracts the text, and analyzes it. As a result of the analysis, certain keywords are extracted and compared with trusted database information. If the result is 80% confidence, the following report will be generated:

[0625] Example prompt sentence:

[0626] Check the authenticity of this URL with the News Verifier app: https: / / example.com / news-article

[0627] Generated response:

[0628] Confidence: 80%

[0629] Evidence: 80% match with trusted sources in our database. This article can be treated as relatively reliable information.

[0630] This allows users to easily determine the reliability of news articles for themselves.

[0631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0632] Step 1:

[0633] A user opens the News Verifier app, enters the URL of a news article, and presses the send button. The user's device sends this URL to the server. The input is the URL entered by the user, and the output is the URL sent to the server. Specific operations include the user entering the URL from the application screen on their smartphone.

[0634] Step 2:

[0635] The server uses the received URL to scrape the web page. Here, we use "BeautifulSoup." The input is the URL sent in step 1, and the output is the article text of the web page. The specific operation involves the server accessing the specified URL and extracting the article text from the HTML code.

[0636] Step 3:

[0637] The server performs natural language processing on the extracted article text. Here, it uses "spaCy" to perform tokenization, stemming, and part-of-speech tagging. The input is the article text extracted in step 2, and the output is a list of important keywords and phrases. Specifically, it splits the text into individual words, returns it to its original form, and tags it for parts of speech.

[0638] Step 4:

[0639] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. It uses a proprietary algorithm. The input is the list of keywords and phrases obtained in step 3, and the output is a score evaluating the degree of match. Specifically, it retrieves relevant information from the database, compares it with the keywords and phrases, and calculates the degree of match.

[0640] Step 5:

[0641] The server generates a report that integrates the reliability judgment results and their rationale based on the matching evaluation score. The input is the matching evaluation score obtained in step 4, and the output is a report to be provided to the user. Specifically, the server creates a reliability value and rationale text based on the evaluation score, and compiles these into a single report.

[0642] Step 6:

[0643] The generated report is sent to the user's terminal, where the user views it. The input is the report generated in step 5, and the output is the report displayed on the user's terminal. Specific operations include the server sending the report to the user's terminal and the user viewing the report on the screen of their smartphone.

[0644] 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.

[0645] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, an emotion engine, and a database.

[0646] System configuration

[0647] User terminal

[0648] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server. If necessary, the input data can also be sent to an emotion engine for analyzing the user's emotions.

[0649] server

[0650] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[0651] 1. URL analysis

[0652] Scrape the web page from the URL you receive and extract the article text.

[0653] 2. Natural Language Processing (NLP)

[0654] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[0655] 3. Reliability assessment

[0656] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[0657] 4. Report Generation

[0658] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[0659] Emotion Engine

[0660] The emotion engine analyzes the information and behavioral patterns entered by the user and has the function of recognizing the user's emotions. Specifically, it has the following functions:

[0661] 1. Emotion analysis

[0662] The system analyzes the text and behavioral data entered by the user and determines their emotions using natural language processing technology.

[0663] 2. Relevance Analysis

[0664] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[0665] 3. Report customization

[0666] Customize the results and report content based on the recognized user sentiment.

[0667] Database

[0668] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0669] Program processing

[0670] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[0671] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[0672] Furthermore, an emotion engine analyzes the text entered by the user to determine their emotion. For example, it recognizes various emotions such as positive, negative, surprise, and anger. Based on the recognized emotion, the report content is customized. In the case of a positive emotion, parts with many highly reliable sources are highlighted, while in the case of a negative emotion, uncertain elements are explained in detail.

[0673] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0674] Specific examples

[0675] Example: Scrutinizing news articles circulating on social media

[0676] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[0677] The server first receives the URL and then scrapes the web page to extract the article text.

[0678] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[0679] The extracted information is compared with a reliable database to assess the degree of match.

[0680] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0681] Furthermore, the emotion engine analyzes the data entered by the user and determines the user's emotion.

[0682] Moderately customize report content based on user sentiment.

[0683] A report is generated and sent to the user terminal.

[0684] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information. It also recognizes users' emotions and customizes the report content, enabling more personalized information provision.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[0688] Step 2:

[0689] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[0690] Step 3:

[0691] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[0692] Step 4:

[0693] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[0694] Step 5:

[0695] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques, specifically by following these steps:

[0696] Tokenization: Breaking text into individual words or phrases.

[0697] Stemming: Extracting the roots of words.

[0698] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[0699] Step 6:

[0700] Server: Extracts important keywords and phrases from the NLP analysis results, for example, using Term Frequency-Inverse Document Frequency (TF-IDF) to identify important words and phrases in the text.

[0701] Step 7:

[0702] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[0703] Step 8:

[0704] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[0705] Step 9:

[0706] Server: Integrates the judgement results and evidence information and generates a report, which includes a confidence score and a list of referenced sources.

[0707] Step 10:

[0708] Server: Sends the text entered by the user to the emotion engine for sentiment analysis, for example, analyzing user comments and reactions to articles.

[0709] Step 11:

[0710] Server: The emotion engine determines the user's emotions. For example, it uses natural language processing technology to recognize positive, negative, and other emotions.

[0711] Step 12:

[0712] Server: Customize report content based on perceived user sentiment, e.g., detailing low-trust sources if negative sentiment is detected.

[0713] Step 13:

[0714] Server: Sends customized reports to user devices.

[0715] Step 14:

[0716] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[0717] Step 15:

[0718] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[0719] Example 2

[0720] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0721] As the amount of information on the Internet increases, users need to be able to quickly and accurately evaluate the reliability of the information they see. However, current systems are insufficient in assessing reliability, which puts users at risk of believing incorrect information. Furthermore, information provided does not take into account the user's emotions, resulting in a lack of improvement in the user experience.

[0722] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving information input by a user, means for acquiring the content of the information from a specified information source, language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable storage device to determine its reliability, means for analyzing the information and behavioral patterns input by the user and determining the user's emotions, and means for generating a determination result and its basis and creating a report to be provided to the user. This allows the user to quickly and accurately evaluate the reliability of information, and also makes it possible to provide a personalized report based on the user's emotions.

[0723] The "means for receiving information input by the user" is a function for receiving URLs and text data provided by the user via the input interface.

[0724] "Means of obtaining the content of the information from the specified source" refers to the function of obtaining the target web page or data from the input URL or specified source using scraping or other techniques.

[0725] "Language processing means for processing acquired information and extracting assertive parts and important keywords" refers to a function that applies natural language processing technology to the extracted text, performing tokenization, stemming, and part-of-speech tagging to extract assertive parts and important keywords.

[0726] "Means for comparing extracted information with trusted storage to determine trustworthiness" refers to a function that uses algorithms and databases to compare extracted keywords and phrases with trusted sources, evaluate the degree of match, and determine the trustworthiness of the information.

[0727] "Means for analyzing information entered by the user and behavioral patterns to determine the user's emotions" refers to a function that analyzes the user's input and behavioral data and uses natural language processing technology to recognize the user's emotions.

[0728] "Means for generating a report that generates a judgment result and its basis and provides it to the user" is a function that compiles an evaluation of the reliability of information and its basis, and generates a report that can be provided to the user in an easy-to-understand manner.

[0729] The present invention is a system that automatically determines the reliability of information entered by a user and provides the user with an appropriate report. This system is composed of a user terminal, a server, an emotion analysis engine, and a database.

[0730] User terminal

[0731] A user terminal is a device through which a user inputs information via input means. For example, a news article or a URL of a social networking site can be entered from the browser of a personal computer or smartphone. The user terminal provides an interface for sending the entered information to a server. It can also send data to an emotion engine to analyze the user's emotions as needed.

[0732] server

[0733] The server performs the primary function of analyzing the received information and determining its reliability. Specifically, it uses the following software and technologies:

[0734] 1. Web page scraping

[0735] The server scrapes the web page from the URL received from the user and extracts the article text, using software such as "BeautifulSoup" or "Scrapy."

[0736] 2. Natural Language Processing (NLP)

[0737] The server tokenizes the extracted article text using NLTK and spaCy, and performs stemming and part-of-speech tagging to extract important keywords and phrases.

[0738] 3. Reliability assessment

[0739] The server uses Elasticsearch to compare extracted keywords and phrases with trusted sources and evaluates the degree of match. Based on the results, it determines the trustworthiness of the information.

[0740] 4. Report Generation

[0741] The server aggregates the results and rationale of the confidence assessment and generates a report to provide to the user.

[0742] Emotion Engine

[0743] The emotion engine has the function of analyzing the information and behavioral patterns entered by the user and recognizing the user's emotions. Specifically, it uses the following technologies:

[0744] 1. Emotion analysis

[0745] The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from user input text.

[0746] 2. Relevance Analysis

[0747] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[0748] 3. Report customization

[0749] Customize the report content based on the sentiment analysis results, for example highlighting reliable sources for positive sentiment and providing detailed explanations of uncertainties for negative sentiment.

[0750] Database

[0751] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0752] Specific examples

[0753] When a user pastes the URL of a news article into the system's input field and presses the send button, the following steps occur:

[0754] The server receives the URL and scrapes the web page to extract the article text.

[0755] The server uses natural language processing technology to analyze the text and extract important keywords and key points.

[0756] The server compares the extracted information with a trusted database and evaluates the degree of match.

[0757] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0758] The emotion engine analyzes the user's input data and determines the user's emotion.

[0759] The report content is appropriately customized based on the user's sentiment.

[0760] Finally, the report is sent to the user terminal and the user confirms it.

[0761] Prompt Sentence Examples

[0762] By entering the URL of an article along with the prompt "Please rate the credibility of this news article:", the system performs text analysis, credibility rating, sentiment analysis, etc. on the article and provides the results.

[0763] This allows users to easily check the reliability of news articles and social media information, enabling them to make appropriate decisions. Furthermore, the report content is customized based on the user's emotions, achieving more personalized information provision.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Step 1:

[0766] User

[0767] The user enters the URL of a news article into the input field of the device and presses the send button. The user opens a browser, copies the URL of an article of interest from a social networking site or news site, and pastes it into the input field of the system. Then, the user clicks the send button. As a result, the input information is the URL of the news article, and the output is an HTTP request.

[0768] Step 2:

[0769] Terminal

[0770] The device sends the URL entered by the user to the server. When the user presses the send button, the device's browser generates an HTTP request and sends it to the specified server. The input in this step is the news article URL and the HTTP request, and the output is the URL sent to the server.

[0771] Step 3:

[0772] server

[0773] The server scrapes the web page from the received URL and extracts the article text. The server uses "BeautifulSoup" or "Scrapy" to get the web page content from the URL and extract the article text. The input in this step is the URL and the output is the extracted article text.

[0774] Step 4:

[0775] server

[0776] The server applies natural language processing to the extracted article text. Using NLTK or spaCy, the server tokenizes the article text and performs stemming and part-of-speech tagging. This allows important keywords and phrases to be extracted. The input for this step is the article text, and the output is keywords and phrases.

[0777] Step 5:

[0778] server

[0779] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. The server uses Elasticsearch to compare the keywords and phrases with trusted sources in the database and calculate the degree of match. The input for this step is the keywords and phrases and the database information, and the output is the result of the match assessment.

[0780] Step 6:

[0781] Emotion Engine

[0782] The emotion engine analyzes the information and behavioral patterns entered by the user to determine the user's emotion. The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from the user's input text. The input for this step is the user's input data, and the output is the recognized emotion.

[0783] Step 7:

[0784] server

[0785] The server generates a report based on the reliability evaluation results and sentiment analysis results. The server integrates the reliability evaluation results with the emotion engine analysis results and creates a report for the user. The report contains the reliability value and its rationale. The inputs to this step are the reliability evaluation results and sentiment analysis results, and the output is a report.

[0786] Step 8:

[0787] Emotion Engine

[0788] The emotion engine customizes the report content based on the user's emotion. The emotion engine adjusts the report depending on the user's emotion. For example, a positive emotion might highlight areas of high confidence, while a negative emotion might provide more detailed explanations of uncertainties. The inputs to this step are the report and the user's emotion, and the output is a customized report.

[0789] Step 9:

[0790] server

[0791] The server sends the generated report to the user terminal. The server returns the generated report to the user terminal as an HTTP response. The report is then displayed in the browser on the user terminal. The input of this step is the report, and the output is the sent report.

[0792] Step 10:

[0793] Terminal

[0794] The terminal displays the received report. The browser on the user's terminal analyzes the contents of the report received from the server and displays it on the screen. The user can check this and understand the reliability of the article and its basis. The input of this step is the report, and the output is the displayed report.

[0795] (Application example 2)

[0796] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0797] Internet advertisements often contain information of questionable reliability, making it difficult for users to judge such information. This problem is particularly serious in today's world, where fraud and fake news are easily spread. The present invention aims to solve this problem by providing a system that efficiently and accurately judges the reliability of advertising information viewed by users on the Internet and presents the results and evidence.

[0798] The identification process by the identification 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 means for receiving information input by a user, means for acquiring the content of the information from a specified information source, natural language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable database to determine its reliability, means for generating a report to provide the user with the determination result and its basis, and means for determining the reliability of advertisements in real time and providing the determination result. This allows users to accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[0799] A "user" is a person or entity that utilizes the system to enter and receive information.

[0800] "Information" refers to data such as URLs and text data entered by users, and the contents of web pages retrieved by the system.

[0801] A "designated source" is a pre-defined source, such as a website or database, that is referenced for assessing trustworthiness.

[0802] "Natural language processing" is a technology that analyzes input text data using algorithms such as tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases.

[0803] "Reliability" is a numerical value or index that evaluates the degree to which extracted information matches a reliable database.

[0804] A "database" is a collection of information that stores data collected from reliable sources.

[0805] "Report creation" refers to the creation of documents and presentation formats that integrate the results and their rationale for analysis and evaluation results in order to provide them to users.

[0806] "Advertising" means information or content displayed on the Internet for the purpose of promoting a product or service.

[0807] A "URL" is a string of characters that specifies the location of a web page and points to an Internet resource such as an advertisement or news article.

[0808] "Analysis" is the process of breaking down acquired information using algorithms and methods to understand and evaluate its structure and content.

[0809] The "judgment result" is an evaluation or judgment obtained as a result of the analysis and reliability judgment.

[0810] "Real-time" refers to the time characteristics of a process in which information is obtained and evaluated immediately.

[0811] Overall system configuration

[0812] The present invention provides a system that efficiently and accurately determines the reliability of information entered by a user and presents the results and the basis for the determination. The system is composed of a user terminal, a server, and multiple components. The system will be described in detail below.

[0813] User terminal

[0814] The user terminal is a device through which the user inputs information and receives the judgment results. For example, the user inputs the URL of the advertisement page using a smartphone or a PC.

[0815] server

[0816] The server is the central information processing center. It provides the following main functions:

[0817] Data Acquisition Method

[0818] It scrapes web pages from URLs entered by users and extracts the text from those pages. The hardware used is a high-performance server, and the software uses the widely used Python requests and BeautifulSoup libraries.

[0819] Natural language processing tools

[0820] The acquired text is then subjected to natural language processing (NLP) to extract important keywords and phrases. This processing is performed using a NLP library such as NLTK, which performs tokenization, stemming, and part-of-speech tagging.

[0821] Reliability determination means

[0822] The extracted keywords and phrases are compared with a reliable database to evaluate their reliability based on the degree of match. This process uses a database collected from pre-prepared, reliable sources. The algorithm used is a match check algorithm.

[0823] Emotion analysis means

[0824] The system analyzes user sentiment based on the captured text. For sentiment analysis, it uses the VADER Sentiment library, which calculates a positive, negative, or neutral sentiment score and reflects it in the report.

[0825] Report Creation Method

[0826] The results of the confidence assessment and sentiment analysis are integrated to generate a report and provided to the user using a custom Python script.

[0827] Specific examples

[0828] For example, the following shows the process when a user views an advertisement on a smartphone and enters the URL of the advertisement page.

[0829] A user enters the URL of an ad page in an application on their smartphone. When they press the submit button, the URL is sent to the server, which then scrapes the web page from the URL to extract text. Next, natural language processing is performed on the extracted text to analyze important keywords and phrases. This is then compared with a trusted database to assess its trustworthiness. Sentiment analysis is also performed in parallel, and a report based on the user's sentiment is generated. Finally, a trustworthiness report is displayed on the user's device, allowing the user to review the results.

[0830] Prompt Sentence Examples

[0831] The user inputs the URL of a news article. The system scrapes the web page from the input URL and extracts the text. It then uses natural language processing technology to extract important keywords from that text. It then compares the reliability of the article against a trusted database and generates a report based on the results. Finally, the generated report is sent to the user's device and displayed.

[0832] According to this invention, users can accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[0833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0834] Step 1:

[0835] The user inputs the URL of the advertisement page using a smartphone application. The input URL is sent from the user's device to the server. Input: URL of advertisement page, Output: URL sent to the server.

[0836] Step 2:

[0837] The server scrapes the web page from the received URL and extracts the text from that page. Specifically, it retrieves the web page using Python's requests library and extracts the text from the HTML using the BeautifulSoup library. Input: URL of the ad page, Output: Text of the ad page.

[0838] Step 3:

[0839] The server performs natural language processing on the extracted text. It uses Python's NLTK library to perform tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases. Input: text from the ad page. Output: important keywords and phrases.

[0840] Step 4:

[0841] The server checks the extracted keywords and phrases against a trusted database, calculates the degree of match, and determines the confidence score. A specific algorithm is used to evaluate whether there is a match. Input: extracted keywords and phrases, database. Output: confidence score.

[0842] Step 5:

[0843] The server performs sentiment analysis based on the extracted text. It uses the VADER Sentiment library to calculate a positive, negative, or neutral sentiment score. Input: text of the ad page. Output: sentiment score.

[0844] Step 6:

[0845] The server integrates the results of the confidence evaluation and sentiment analysis to generate a report. The report includes confidence scores, sentiment scores, and their rationales. Input: confidence scores, sentiment scores. Output: confidence report.

[0846] Step 7:

[0847] The generated report is sent from the server to the user's device. The user can check the report on their smartphone and evaluate the reliability of the advertising information. Input: Reliability report, Output: Report displayed on the user's device.

[0848] In this way, by performing each step sequentially, the user can efficiently and accurately evaluate the reliability of the advertising information viewed on the Internet, and can use it with peace of mind.

[0849] 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.

[0850] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] 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.

[0852] [Third embodiment]

[0853] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0854] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0855] 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).

[0856] 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.

[0857] 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.

[0858] 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).

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0865] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[0866] System configuration

[0867] User terminal

[0868] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server.

[0869] server

[0870] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[0871] 1. URL analysis

[0872] Scrape the web page from the URL you receive and extract the article text.

[0873] 2. Natural Language Processing (NLP)

[0874] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[0875] 3. Reliability assessment

[0876] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[0877] 4. Report Generation

[0878] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[0879] Database

[0880] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[0881] Program processing

[0882] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[0883] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[0884] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[0885] Specific examples

[0886] Example: Scrutinizing news articles circulating on social media

[0887] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[0888] The server first receives the URL and then scrapes the web page to extract the article text.

[0889] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[0890] The extracted information is compared with a reliable database to assess the degree of match.

[0891] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[0892] A report is generated and sent to the user terminal.

[0893] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[0897] Step 2:

[0898] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[0899] Step 3:

[0900] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[0901] Step 4:

[0902] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[0903] Step 5:

[0904] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques. This involves the following specific steps:

[0905] Tokenization: Breaking text into individual words or phrases.

[0906] Stemming: Extracting the roots of words.

[0907] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[0908] Step 6:

[0909] Server: Extracts important keywords and phrases from the NLP analysis results. Using algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency), it identifies words and phrases with high importance within the text.

[0910] Step 7:

[0911] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[0912] Step 8:

[0913] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[0914] Step 9:

[0915] Server: Consolidates the verdict and supporting information and generates a report, showing a confidence score as the verdict and including a list of trusted sources referenced as supporting information.

[0916] Step 10:

[0917] Server: Sends the generated report to the user's device. For example, it passes the report data to the device in JSON or HTML format.

[0918] Step 11:

[0919] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[0920] Step 12:

[0921] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[0922] Example 1

[0923] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0924] In today's information society, there is a huge amount of information on the Internet whose authenticity is unknown, and users are required to quickly and accurately judge it. However, it is not easy for users to verify the reliability of information themselves, as it requires a great deal of time and specialized knowledge. Furthermore, the spread of unreliable information could lead to social confusion and misunderstanding. Therefore, a system is needed that can efficiently and accurately determine the reliability of information provided by users and provide the results to users.

[0925] 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.

[0926] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; means for generating a report to provide to the user with the determination result and its basis; means for the user to input a news article URL and send the input URL; means for the server to scrape the web page from the URL and extract the article text; and means for the server to compare the extracted keywords or phrases with data in the trusted database and evaluate the degree of match, thereby enabling users to easily confirm the reliability of information.

[0927] "User" is a person or device that inputs information into the system and receives the results.

[0928] "Terminal" means a device through which a user inputs information and communicates with the system.

[0929] A "server" is a computer system that plays a key role in analyzing the information it receives and determining its trustworthiness.

[0930] "Database" means a storage medium within a system that contains data collected from reliable sources.

[0931] A "URL" is an address that identifies a web page on the Internet.

[0932] A "web page" is a page of information accessible via the Internet.

[0933] "Scraping" is a technique for automatically extracting specific data from web pages.

[0934] An "article" is a document or piece of textual information posted on a web page.

[0935] "Natural language processing" is a technology that analyzes text data and understands or processes its content.

[0936] "Tokenization" is the process of dividing text into units of words and phrases.

[0937] "Stemming" is the process of extracting the roots of words.

[0938] "Part-of-speech tagging" is the process of tagging each word in a text with a part of speech.

[0939] "Keywords" are words or phrases in a piece of text that are considered particularly important.

[0940] "Reliability" is a criterion for assessing whether information is trustworthy or not.

[0941] An "algorithm" is a formalized procedure or computational method for solving a particular problem.

[0942] "Report" means a report that includes the reliability assessment results and their rationale.

[0943] The present invention relates to a system for efficiently and accurately determining the reliability of information provided by a user and providing the result to the user. An embodiment of the system will be specifically described below.

[0944] This system mainly consists of a user terminal, a server, and a database.

[0945] User terminal

[0946] A user terminal is a device through which a user inputs information. Examples include a PC or smartphone. The user uses a browser to input the URL of a social networking site or news article and presses the send button. The user terminal provides an interface for sending the input URL to the server. An example of a prompt sentence is "Please paste the URL of the news article into the input field."

[0947] server

[0948] The server is responsible for analyzing the received information and determining its reliability. Specifically, it uses the following hardware and software:

[0949] URL analysis

[0950] The server uses libraries such as BeautifulSoup to scrape the web page from the received URL and extract the article text, allowing you to retrieve the required information from the specified source.

[0951] Natural Language Processing (NLP)

[0952] The server performs natural language processing on the extracted text, using libraries such as NLTK and Spacy to:

[0953] Tokenization: The process of dividing text into units of words and phrases.

[0954] Stemming: The process of extracting the roots of words.

[0955] Part-of-speech tagging: The process of tagging each word in a text with its part of speech.

[0956] This allows the system to extract the text's assertive parts and important keywords.

[0957] Reliability determination

[0958] The server then matches the extracted keywords and phrases with sources in a trusted database. Specific algorithms and similarity calculations are used for matching. For example, the server evaluates the degree of similarity with sources included in Google Scholar or trusted news sites. Based on the results of this evaluation, the server assigns a numerical rating to the reliability of the text.

[0959] Report Generation

[0960] The server generates a report that integrates the results of the reliability evaluation and their rationale, and provides it to the user. The report includes the reliability value, matching sources, and keyword analysis results. The generated report is sent to the user's device.

[0961] Database

[0962] The database contains data collected from trusted sources, including trusted news sites, academic papers, etc. The server consults this database when determining the trustworthiness of information.

[0963] Specific examples

[0964] Consider a scenario where a user copies and pastes the URL of a news article they found on a social networking site into an input field on the system and presses the send button.

[0965] 1. Enter the URL on the user's device and send it

[0966] The user enters the URL of a news article and presses the send button.

[0967] 2. Receive URL and extract text on the server

[0968] The server scrapes the received URL and extracts the article text.

[0969] 3. Perform natural language processing (NLP) on the server

[0970] The server performs natural language processing on the extracted text to extract important keywords and assertive parts.

[0971] 4. The server determines the reliability

[0972] The server matches the extracted keywords with sources in a trusted database and evaluates the degree of match.

[0973] 5. Generate and send the report on the server

[0974] The server generates a report summarizing the evaluation results and their rationale and sends it to the user's terminal.

[0975] 6. Display the report on the user's device

[0976] The received report is displayed on the user's terminal, and the user can check the reliability of the news article and its basis.

[0977] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions.

[0978] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0979] Step 1:

[0980] Entering and submitting a URL

[0981] The user enters the URL of a news article into the system's input field using a computer or smartphone browser and presses the send button.

[0982] Input: News article URL

[0983] Output: A request to send the URL to the server

[0984] Specifically, users copy the URL of an article they find on a social networking site or news site, paste it into the system's input field, and then click the send button, which sends the URL to the server.

[0985] Step 2:

[0986] Receiving URLs and scraping web pages

[0987] The server receives the URL sent from the user terminal and extracts the content of the web page using a scraping tool (e.g., BeautifulSoup).

[0988] Input: URL sent by user

[0989] Output: Extracted article text

[0990] Specifically, the server checks whether the received URL is in a valid format, analyzes the HTML structure, and <article>Extracts article text from a specified selector, such as a tag.

[0991] Step 3:

[0992] Natural language processing of extracted text

[0993] The server analyzes the extracted article text using natural language processing technology.

[0994] Input: Article text extracted by scraping

[0995] Output: Tokenized text, stemmed word list, part-of-speech tagged text

[0996] Specifically, the server uses the NLTK and Spacy libraries to perform the following tasks:

[0997] Tokenization: Splitting text into words and phrases. For example, splitting "Climate change will become serious" into "Climate change", "but", and "become serious".

[0998] Stemming: Extracting the root of a word. Example: "become serious" becomes "serious."

[0999] Part-of-speech tagging: Assign a part-of-speech tag to each word, e.g., "climate change / noun", "ga / particle", "serious / adjective", "naru / verb".

[1000] Step 4:

[1001] Determining reliability

[1002] The server matches the extracted keywords and phrases with sources in a trusted database and evaluates the degree of match.

[1003] Input: A list of tokenized and stemmed keywords

[1004] Output: Numerical evaluation of reliability, matching result data

[1005] Specifically, the server evaluates the degree of match between keywords and phrases and reliable sources in the database (e.g., academic paper databases or reliable news sites). It then uses a similarity calculation algorithm to quantify the degree of match and makes a judgment, such as "if the degree of match is 80% or higher, the confidence level is 90%."

[1006] Step 5:

[1007] Generate and send reports

[1008] The server generates a report that integrates the results of the reliability judgment and the basis for it, and transmits it to the user terminal.

[1009] Input: Numerical evaluation of reliability, matching result data

[1010] Output: Generated report, request to send to user terminal

[1011] Specifically, the server creates a report based on the results of the reliability assessment. The report includes the reliability value, the matching results, and the extracted keywords. The report is then sent to the user's device.

[1012] Step 6:

[1013] Viewing Reports

[1014] The user terminal receives the report sent from the server and displays it to the user.

[1015] Input: Report sent from the server

[1016] Output: Report displayed in a user-readable format

[1017] Specifically, the user device displays the received report on a web page, where the user can check the reliability of the news article and its basis. For example, it may state that the reliability is 90% and that multiple reliable sources agree.

[1018] (Application example 1)

[1019] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1020] The Internet is overflowing with news articles and information, making it difficult to quickly and accurately assess their reliability. This puts users at risk of being misled by unreliable information and making incorrect decisions. Users of mobile devices, such as smartphones, lack easy ways to verify information's reliability. This invention aims to solve these problems and enable users to make decisions based on accurate information.

[1021] 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.

[1022] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a reliable database to determine its reliability; means for generating a report of the determination result and its basis and providing it to the user; means for having an interface applicable to a mobile device and operable directly by the user from the device; and means for having an application for analyzing the URL of a news article to determine its reliability and providing the result. This allows users to easily check the reliability of news articles on their mobile devices such as smartphones.

[1023] "Means for receiving information entered by the user" refers to the interface or form that allows a user to enter information such as the URL of a news article using a device such as a smartphone or PC.

[1024] "Means of obtaining the content of the information from a specified source" refers to a method of extracting article text from a web page on the Internet using web scraping technology, etc., based on the URL entered by the user.

[1025] "Natural language processing means for processing acquired information and extracting assertive parts and important keywords" refers to technology that applies natural language processing techniques such as tokenization, stemming, and part-of-speech tagging to acquired article text to extract important keywords and phrases and analyze the article content.

[1026] "Methods of determining reliability by comparing with a reliable database" refers to a method of determining the reliability of an article by comparing extracted keywords and phrases with a database containing reliable information sources and using an algorithm to evaluate the degree of match.

[1027] "Means for generating a report that provides the results of a reliability assessment and their rationale, and presents it to the user" refers to a technology that integrates the results of a reliability assessment and their rationale, generates a report that can be presented to the user in an easy-to-understand manner, and sends it to the user's terminal.

[1028] "Means that have an interface applicable to mobile devices and allow users to operate directly from the device" refers to a user interface that can be operated on mobile devices such as smartphones and tablets. This is implemented as a smartphone application, allowing users to easily operate it.

[1029] "Means having an application that analyzes the URL of a news article, determines its reliability, and provides the results" refers to an application that has the function of automatically analyzing the URL of a news article entered by a user, determining its reliability, and providing the results to the user.

[1030] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[1031] A user terminal is a device into which a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a smartphone or PC browser. The user uses a smartphone application to input the URL of a news article and presses the send button. This sends the URL to the server.

[1032] The server plays the primary role of analyzing the received information and determining its reliability. Specifically, it first scrapes the web page from the received URL and extracts the article text. This is done using a Python library called "BeautifulSoup." Next, natural language processing techniques are used on this text to perform tokenization, stemming, and part-of-speech tagging. In this case, a natural language processing library called "spaCy" is used. This allows important keywords and phrases to be extracted.

[1033] The server then compares the extracted keywords and phrases with information sources in a trusted database and evaluates the degree of match. The trusted database stores reliable news sites, academic papers, etc. The server then uses this information to determine the reliability of the information. For example, if a claim in the text matches multiple reliable sources in the database, the server evaluates the reliability of the claim.

[1034] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[1035] As a concrete example, let's say a user enters the URL "https: / / example.com / news-article". The News Verifier application scrapes the article from this URL, extracts the text, and analyzes it. As a result of the analysis, certain keywords are extracted and compared with trusted database information. If the result is 80% confidence, the following report will be generated:

[1036] Example prompt sentence:

[1037] Check the authenticity of this URL with the News Verifier app: https: / / example.com / news-article

[1038] Generated response:

[1039] Confidence: 80%

[1040] Evidence: 80% match with trusted sources in our database. This article can be treated as relatively reliable information.

[1041] This allows users to easily determine the reliability of news articles for themselves.

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] A user opens the News Verifier app, enters the URL of a news article, and presses the send button. The user's device sends this URL to the server. The input is the URL entered by the user, and the output is the URL sent to the server. Specific operations include the user entering the URL from the application screen on their smartphone.

[1045] Step 2:

[1046] The server uses the received URL to scrape the web page. Here, we use "BeautifulSoup." The input is the URL sent in step 1, and the output is the article text of the web page. The specific operation involves the server accessing the specified URL and extracting the article text from the HTML code.

[1047] Step 3:

[1048] The server performs natural language processing on the extracted article text. Here, it uses "spaCy" to perform tokenization, stemming, and part-of-speech tagging. The input is the article text extracted in step 2, and the output is a list of important keywords and phrases. Specifically, it splits the text into individual words, returns it to its original form, and tags it for parts of speech.

[1049] Step 4:

[1050] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. It uses a proprietary algorithm. The input is the list of keywords and phrases obtained in step 3, and the output is a score evaluating the degree of match. Specifically, it retrieves relevant information from the database, compares it with the keywords and phrases, and calculates the degree of match.

[1051] Step 5:

[1052] The server generates a report that integrates the reliability judgment results and their rationale based on the matching evaluation score. The input is the matching evaluation score obtained in step 4, and the output is a report to be provided to the user. Specifically, the server creates a reliability value and rationale text based on the evaluation score, and compiles these into a single report.

[1053] Step 6:

[1054] The generated report is sent to the user's terminal, where the user views it. The input is the report generated in step 5, and the output is the report displayed on the user's terminal. Specific operations include the server sending the report to the user's terminal and the user viewing the report on the screen of their smartphone.

[1055] 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.

[1056] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, an emotion engine, and a database.

[1057] System configuration

[1058] User terminal

[1059] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server. If necessary, the input data can also be sent to an emotion engine for analyzing the user's emotions.

[1060] server

[1061] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[1062] 1. URL analysis

[1063] Scrape the web page from the URL you receive and extract the article text.

[1064] 2. Natural Language Processing (NLP)

[1065] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[1066] 3. Reliability assessment

[1067] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[1068] 4. Report Generation

[1069] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[1070] Emotion Engine

[1071] The emotion engine analyzes the information and behavioral patterns entered by the user and has the function of recognizing the user's emotions. Specifically, it has the following functions:

[1072] 1. Emotion analysis

[1073] The system analyzes the text and behavioral data entered by the user and determines their emotions using natural language processing technology.

[1074] 2. Relevance Analysis

[1075] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[1076] 3. Report customization

[1077] Customize the results and report content based on the recognized user sentiment.

[1078] Database

[1079] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[1080] Program processing

[1081] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[1082] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[1083] Furthermore, an emotion engine analyzes the text entered by the user to determine their emotion. For example, it recognizes various emotions such as positive, negative, surprise, and anger. Based on the recognized emotion, the report content is customized. In the case of a positive emotion, parts with many highly reliable sources are highlighted, while in the case of a negative emotion, uncertain elements are explained in detail.

[1084] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[1085] Specific examples

[1086] Example: Scrutinizing news articles circulating on social media

[1087] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[1088] The server first receives the URL and then scrapes the web page to extract the article text.

[1089] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[1090] The extracted information is compared with a reliable database to assess the degree of match.

[1091] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[1092] Furthermore, the emotion engine analyzes the data entered by the user and determines the user's emotion.

[1093] Moderately customize report content based on user sentiment.

[1094] A report is generated and sent to the user terminal.

[1095] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information. It also recognizes users' emotions and customizes the report content, enabling more personalized information provision.

[1096] The processing flow will be explained below.

[1097] Step 1:

[1098] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[1099] Step 2:

[1100] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[1101] Step 3:

[1102] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[1103] Step 4:

[1104] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[1105] Step 5:

[1106] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques, specifically by following these steps:

[1107] Tokenization: Breaking text into individual words or phrases.

[1108] Stemming: Extracting the roots of words.

[1109] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[1110] Step 6:

[1111] Server: Extracts important keywords and phrases from the NLP analysis results, for example, using Term Frequency-Inverse Document Frequency (TF-IDF) to identify important words and phrases in the text.

[1112] Step 7:

[1113] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[1114] Step 8:

[1115] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[1116] Step 9:

[1117] Server: Integrates the judgement results and evidence information and generates a report, which includes a confidence score and a list of referenced sources.

[1118] Step 10:

[1119] Server: Sends the text entered by the user to the emotion engine for sentiment analysis, for example, analyzing user comments and reactions to articles.

[1120] Step 11:

[1121] Server: The emotion engine determines the user's emotions. For example, it uses natural language processing technology to recognize positive, negative, and other emotions.

[1122] Step 12:

[1123] Server: Customize report content based on perceived user sentiment, e.g., detailing low-trust sources if negative sentiment is detected.

[1124] Step 13:

[1125] Server: Sends customized reports to user devices.

[1126] Step 14:

[1127] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[1128] Step 15:

[1129] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[1130] Example 2

[1131] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1132] As the amount of information on the Internet increases, users need to be able to quickly and accurately evaluate the reliability of the information they see. However, current systems are insufficient in assessing reliability, which puts users at risk of believing incorrect information. Furthermore, information provided does not take into account the user's emotions, resulting in a lack of improvement in the user experience.

[1133] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving information input by a user, means for acquiring the content of the information from a specified information source, language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable storage device to determine its reliability, means for analyzing the information and behavioral patterns input by the user and determining the user's emotions, and means for generating a determination result and its basis and creating a report to be provided to the user. This allows the user to quickly and accurately evaluate the reliability of information, and also makes it possible to provide a personalized report based on the user's emotions.

[1134] The "means for receiving information input by the user" is a function for receiving URLs and text data provided by the user via the input interface.

[1135] "Means of obtaining the content of the information from the specified source" refers to the function of obtaining the target web page or data from the input URL or specified source using scraping or other techniques.

[1136] "Language processing means for processing acquired information and extracting assertive parts and important keywords" refers to a function that applies natural language processing technology to the extracted text, performing tokenization, stemming, and part-of-speech tagging to extract assertive parts and important keywords.

[1137] "Means for comparing extracted information with trusted storage to determine trustworthiness" refers to a function that uses algorithms and databases to compare extracted keywords and phrases with trusted sources, evaluate the degree of match, and determine the trustworthiness of the information.

[1138] "Means for analyzing information entered by the user and behavioral patterns to determine the user's emotions" refers to a function that analyzes the user's input and behavioral data and uses natural language processing technology to recognize the user's emotions.

[1139] "Means for generating a report that generates a judgment result and its basis and provides it to the user" is a function that compiles an evaluation of the reliability of information and its basis, and generates a report that can be provided to the user in an easy-to-understand manner.

[1140] The present invention is a system that automatically determines the reliability of information entered by a user and provides the user with an appropriate report. This system is composed of a user terminal, a server, an emotion analysis engine, and a database.

[1141] User terminal

[1142] A user terminal is a device through which a user inputs information via input means. For example, a news article or a URL of a social networking site can be entered from the browser of a personal computer or smartphone. The user terminal provides an interface for sending the entered information to a server. It can also send data to an emotion engine to analyze the user's emotions as needed.

[1143] server

[1144] The server performs the primary function of analyzing the received information and determining its reliability. Specifically, it uses the following software and technologies:

[1145] 1. Web page scraping

[1146] The server scrapes the web page from the URL received from the user and extracts the article text, using software such as "BeautifulSoup" or "Scrapy."

[1147] 2. Natural Language Processing (NLP)

[1148] The server tokenizes the extracted article text using NLTK and spaCy, and performs stemming and part-of-speech tagging to extract important keywords and phrases.

[1149] 3. Reliability assessment

[1150] The server uses Elasticsearch to compare extracted keywords and phrases with trusted sources and evaluates the degree of match. Based on the results, it determines the trustworthiness of the information.

[1151] 4. Report Generation

[1152] The server aggregates the results and rationale of the confidence assessment and generates a report to provide to the user.

[1153] Emotion Engine

[1154] The emotion engine has the function of analyzing the information and behavioral patterns entered by the user and recognizing the user's emotions. Specifically, it uses the following technologies:

[1155] 1. Emotion analysis

[1156] The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from user input text.

[1157] 2. Relevance Analysis

[1158] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[1159] 3. Report customization

[1160] Customize the report content based on the sentiment analysis results, for example highlighting reliable sources for positive sentiment and providing detailed explanations of uncertainties for negative sentiment.

[1161] Database

[1162] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[1163] Specific examples

[1164] When a user pastes the URL of a news article into the system's input field and presses the send button, the following steps occur:

[1165] The server receives the URL and scrapes the web page to extract the article text.

[1166] The server uses natural language processing technology to analyze the text and extract important keywords and key points.

[1167] The server compares the extracted information with a trusted database and evaluates the degree of match.

[1168] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[1169] The emotion engine analyzes the user's input data and determines the user's emotion.

[1170] The report content is appropriately customized based on the user's sentiment.

[1171] Finally, the report is sent to the user terminal and the user confirms it.

[1172] Prompt Sentence Examples

[1173] By entering the URL of an article along with the prompt "Please rate the credibility of this news article:", the system performs text analysis, credibility rating, sentiment analysis, etc. on the article and provides the results.

[1174] This allows users to easily check the reliability of news articles and social media information, enabling them to make appropriate decisions. Furthermore, the report content is customized based on the user's emotions, achieving more personalized information provision.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] User

[1178] The user enters the URL of a news article into the input field of the device and presses the send button. The user opens a browser, copies the URL of an article of interest from a social networking site or news site, and pastes it into the input field of the system. Then, the user clicks the send button. As a result, the input information is the URL of the news article, and the output is an HTTP request.

[1179] Step 2:

[1180] Terminal

[1181] The device sends the URL entered by the user to the server. When the user presses the send button, the device's browser generates an HTTP request and sends it to the specified server. The input in this step is the news article URL and the HTTP request, and the output is the URL sent to the server.

[1182] Step 3:

[1183] server

[1184] The server scrapes the web page from the received URL and extracts the article text. The server uses "BeautifulSoup" or "Scrapy" to get the web page content from the URL and extract the article text. The input in this step is the URL and the output is the extracted article text.

[1185] Step 4:

[1186] server

[1187] The server applies natural language processing to the extracted article text. Using NLTK or spaCy, the server tokenizes the article text and performs stemming and part-of-speech tagging. This allows important keywords and phrases to be extracted. The input for this step is the article text, and the output is keywords and phrases.

[1188] Step 5:

[1189] server

[1190] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. The server uses Elasticsearch to compare the keywords and phrases with trusted sources in the database and calculate the degree of match. The input for this step is the keywords and phrases and the database information, and the output is the result of the match assessment.

[1191] Step 6:

[1192] Emotion Engine

[1193] The emotion engine analyzes the information and behavioral patterns entered by the user to determine the user's emotion. The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from the user's input text. The input for this step is the user's input data, and the output is the recognized emotion.

[1194] Step 7:

[1195] server

[1196] The server generates a report based on the reliability evaluation results and sentiment analysis results. The server integrates the reliability evaluation results with the emotion engine analysis results and creates a report for the user. The report contains the reliability value and its rationale. The inputs to this step are the reliability evaluation results and sentiment analysis results, and the output is a report.

[1197] Step 8:

[1198] Emotion Engine

[1199] The emotion engine customizes the report content based on the user's emotion. The emotion engine adjusts the report depending on the user's emotion. For example, a positive emotion might highlight areas of high confidence, while a negative emotion might provide more detailed explanations of uncertainties. The inputs to this step are the report and the user's emotion, and the output is a customized report.

[1200] Step 9:

[1201] server

[1202] The server sends the generated report to the user terminal. The server returns the generated report to the user terminal as an HTTP response. The report is then displayed in the browser on the user terminal. The input of this step is the report, and the output is the sent report.

[1203] Step 10:

[1204] Terminal

[1205] The terminal displays the received report. The browser on the user's terminal analyzes the contents of the report received from the server and displays it on the screen. The user can check this and understand the reliability of the article and its basis. The input of this step is the report, and the output is the displayed report.

[1206] (Application example 2)

[1207] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1208] Internet advertisements often contain information of questionable reliability, making it difficult for users to judge such information. This problem is particularly serious in today's world, where fraud and fake news are easily spread. The present invention aims to solve this problem by providing a system that efficiently and accurately judges the reliability of advertising information viewed by users on the Internet and presents the results and evidence.

[1209] The identification process by the identification 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 means for receiving information input by a user, means for acquiring the content of the information from a specified information source, natural language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable database to determine its reliability, means for generating a report to provide the user with the determination result and its basis, and means for determining the reliability of advertisements in real time and providing the determination result. This allows users to accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[1210] A "user" is a person or entity that utilizes the system to enter and receive information.

[1211] "Information" refers to data such as URLs and text data entered by users, and the contents of web pages retrieved by the system.

[1212] A "designated source" is a pre-defined source, such as a website or database, that is referenced for assessing trustworthiness.

[1213] "Natural language processing" is a technology that analyzes input text data using algorithms such as tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases.

[1214] "Reliability" is a numerical value or index that evaluates the degree to which extracted information matches a reliable database.

[1215] A "database" is a collection of information that stores data collected from reliable sources.

[1216] "Report creation" refers to the creation of documents and presentation formats that integrate the results and their rationale for analysis and evaluation results in order to provide them to users.

[1217] "Advertising" means information or content displayed on the Internet for the purpose of promoting a product or service.

[1218] A "URL" is a string of characters that specifies the location of a web page and points to an Internet resource such as an advertisement or news article.

[1219] "Analysis" is the process of breaking down acquired information using algorithms and methods to understand and evaluate its structure and content.

[1220] The "judgment result" is an evaluation or judgment obtained as a result of the analysis and reliability judgment.

[1221] "Real-time" refers to the time characteristics of a process in which information is obtained and evaluated immediately.

[1222] Overall system configuration

[1223] The present invention provides a system that efficiently and accurately determines the reliability of information entered by a user and presents the results and the basis for the determination. The system is composed of a user terminal, a server, and multiple components. The system will be described in detail below.

[1224] User terminal

[1225] The user terminal is a device through which the user inputs information and receives the judgment results. For example, the user inputs the URL of the advertisement page using a smartphone or a PC.

[1226] server

[1227] The server is the central information processing center. It provides the following main functions:

[1228] Data Acquisition Method

[1229] It scrapes web pages from URLs entered by users and extracts the text from those pages. The hardware used is a high-performance server, and the software uses the widely used Python requests and BeautifulSoup libraries.

[1230] Natural language processing tools

[1231] The acquired text is then subjected to natural language processing (NLP) to extract important keywords and phrases. This processing is performed using a NLP library such as NLTK, which performs tokenization, stemming, and part-of-speech tagging.

[1232] Reliability determination means

[1233] The extracted keywords and phrases are compared with a reliable database to evaluate their reliability based on the degree of match. This process uses a database collected from pre-prepared, reliable sources. The algorithm used is a match check algorithm.

[1234] Emotion analysis means

[1235] The system analyzes user sentiment based on the captured text. For sentiment analysis, it uses the VADER Sentiment library, which calculates a positive, negative, or neutral sentiment score and reflects it in the report.

[1236] Report Creation Method

[1237] The results of the confidence assessment and sentiment analysis are integrated to generate a report and provided to the user using a custom Python script.

[1238] Specific examples

[1239] For example, the following shows the process when a user views an advertisement on a smartphone and enters the URL of the advertisement page.

[1240] A user enters the URL of an ad page in an application on their smartphone. When they press the submit button, the URL is sent to the server, which then scrapes the web page from the URL to extract text. Next, natural language processing is performed on the extracted text to analyze important keywords and phrases. This is then compared with a trusted database to assess its trustworthiness. Sentiment analysis is also performed in parallel, and a report based on the user's sentiment is generated. Finally, a trustworthiness report is displayed on the user's device, allowing the user to review the results.

[1241] Prompt Sentence Examples

[1242] The user inputs the URL of a news article. The system scrapes the web page from the input URL and extracts the text. It then uses natural language processing technology to extract important keywords from that text. It then compares the reliability of the article against a trusted database and generates a report based on the results. Finally, the generated report is sent to the user's device and displayed.

[1243] According to this invention, users can accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[1244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] The user inputs the URL of the advertisement page using a smartphone application. The input URL is sent from the user's device to the server. Input: URL of advertisement page, Output: URL sent to the server.

[1247] Step 2:

[1248] The server scrapes the web page from the received URL and extracts the text from that page. Specifically, it retrieves the web page using Python's requests library and extracts the text from the HTML using the BeautifulSoup library. Input: URL of the ad page, Output: Text of the ad page.

[1249] Step 3:

[1250] The server performs natural language processing on the extracted text. It uses Python's NLTK library to perform tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases. Input: text from the ad page. Output: important keywords and phrases.

[1251] Step 4:

[1252] The server checks the extracted keywords and phrases against a trusted database, calculates the degree of match, and determines the confidence score. A specific algorithm is used to evaluate whether there is a match. Input: extracted keywords and phrases, database. Output: confidence score.

[1253] Step 5:

[1254] The server performs sentiment analysis based on the extracted text. It uses the VADER Sentiment library to calculate a positive, negative, or neutral sentiment score. Input: text of the ad page. Output: sentiment score.

[1255] Step 6:

[1256] The server integrates the results of the confidence evaluation and sentiment analysis to generate a report. The report includes confidence scores, sentiment scores, and their rationales. Input: confidence scores, sentiment scores. Output: confidence report.

[1257] Step 7:

[1258] The generated report is sent from the server to the user's device. The user can check the report on their smartphone and evaluate the reliability of the advertising information. Input: Reliability report, Output: Report displayed on the user's device.

[1259] In this way, by performing each step sequentially, the user can efficiently and accurately evaluate the reliability of the advertising information viewed on the Internet, and can use it with peace of mind.

[1260] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1261] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1262] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1266] 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).

[1267] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1272] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1273] 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.

[1274] 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.

[1275] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1276] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[1278] System configuration

[1279] User terminal

[1280] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server.

[1281] server

[1282] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[1283] 1. URL analysis

[1284] Scrape the web page from the URL you receive and extract the article text.

[1285] 2. Natural Language Processing (NLP)

[1286] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[1287] 3. Reliability assessment

[1288] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[1289] 4. Report Generation

[1290] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[1291] Database

[1292] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[1293] Program processing

[1294] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[1295] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[1296] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[1297] Specific examples

[1298] Example: Scrutinizing news articles circulating on social media

[1299] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[1300] The server first receives the URL and then scrapes the web page to extract the article text.

[1301] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[1302] The extracted information is compared with a reliable database to assess the degree of match.

[1303] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[1304] A report is generated and sent to the user terminal.

[1305] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information.

[1306] The processing flow will be explained below.

[1307] Step 1:

[1308] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[1309] Step 2:

[1310] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[1311] Step 3:

[1312] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[1313] Step 4:

[1314] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[1315] Step 5:

[1316] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques. This involves the following specific steps:

[1317] Tokenization: Breaking text into individual words or phrases.

[1318] Stemming: Extracting the roots of words.

[1319] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[1320] Step 6:

[1321] Server: Extracts important keywords and phrases from the NLP analysis results. Using algorithms such as TF-IDF (Term Frequency-Inverse Document Frequency), it identifies words and phrases with high importance within the text.

[1322] Step 7:

[1323] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[1324] Step 8:

[1325] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[1326] Step 9:

[1327] Server: Consolidates the verdict and supporting information and generates a report, showing a confidence score as the verdict and including a list of trusted sources referenced as supporting information.

[1328] Step 10:

[1329] Server: Sends the generated report to the user's device. For example, it passes the report data to the device in JSON or HTML format.

[1330] Step 11:

[1331] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[1332] Step 12:

[1333] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[1334] Example 1

[1335] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1336] In today's information society, there is a huge amount of information on the Internet whose authenticity is unknown, and users are required to quickly and accurately judge it. However, it is not easy for users to verify the reliability of information themselves, as it requires a great deal of time and specialized knowledge. Furthermore, the spread of unreliable information could lead to social confusion and misunderstanding. Therefore, a system is needed that can efficiently and accurately determine the reliability of information provided by users and provide the results to users.

[1337] 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.

[1338] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; means for generating a report to provide to the user with the determination result and its basis; means for the user to input a news article URL and send the input URL; means for the server to scrape the web page from the URL and extract the article text; and means for the server to compare the extracted keywords or phrases with data in the trusted database and evaluate the degree of match, thereby enabling users to easily confirm the reliability of information.

[1339] "User" is a person or device that inputs information into the system and receives the results.

[1340] "Terminal" means a device through which a user inputs information and communicates with the system.

[1341] A "server" is a computer system that plays a key role in analyzing the information it receives and determining its trustworthiness.

[1342] "Database" means a storage medium within a system that contains data collected from reliable sources.

[1343] A "URL" is an address that identifies a web page on the Internet.

[1344] A "web page" is a page of information accessible via the Internet.

[1345] "Scraping" is a technique for automatically extracting specific data from web pages.

[1346] An "article" is a document or piece of textual information posted on a web page.

[1347] "Natural language processing" is a technology that analyzes text data and understands or processes its content.

[1348] "Tokenization" is the process of dividing text into units of words and phrases.

[1349] "Stemming" is the process of extracting the roots of words.

[1350] "Part-of-speech tagging" is the process of tagging each word in a text with a part of speech.

[1351] "Keywords" are words or phrases in a piece of text that are considered particularly important.

[1352] "Reliability" is a criterion for assessing whether information is trustworthy or not.

[1353] An "algorithm" is a formalized procedure or computational method for solving a particular problem.

[1354] "Report" means a report that includes the reliability assessment results and their rationale.

[1355] The present invention relates to a system for efficiently and accurately determining the reliability of information provided by a user and providing the result to the user. An embodiment of the system will be specifically described below.

[1356] This system mainly consists of a user terminal, a server, and a database.

[1357] User terminal

[1358] A user terminal is a device through which a user inputs information. Examples include a PC or smartphone. The user uses a browser to input the URL of a social networking site or news article and presses the send button. The user terminal provides an interface for sending the input URL to the server. An example of a prompt sentence is "Please paste the URL of the news article into the input field."

[1359] server

[1360] The server is responsible for analyzing the received information and determining its reliability. Specifically, it uses the following hardware and software:

[1361] URL analysis

[1362] The server uses libraries such as BeautifulSoup to scrape the web page from the received URL and extract the article text, allowing you to retrieve the required information from the specified source.

[1363] Natural Language Processing (NLP)

[1364] The server performs natural language processing on the extracted text, using libraries such as NLTK and Spacy to:

[1365] Tokenization: The process of dividing text into units of words and phrases.

[1366] Stemming: The process of extracting the roots of words.

[1367] Part-of-speech tagging: The process of tagging each word in a text with its part of speech.

[1368] This allows the system to extract the text's assertive parts and important keywords.

[1369] Reliability determination

[1370] The server then matches the extracted keywords and phrases with sources in a trusted database. Specific algorithms and similarity calculations are used for matching. For example, the server evaluates the degree of similarity with sources included in Google Scholar or trusted news sites. Based on the results of this evaluation, the server assigns a numerical rating to the reliability of the text.

[1371] Report Generation

[1372] The server generates a report that integrates the results of the reliability evaluation and their rationale, and provides it to the user. The report includes the reliability value, matching sources, and keyword analysis results. The generated report is sent to the user's device.

[1373] Database

[1374] The database contains data collected from trusted sources, including trusted news sites, academic papers, etc. The server consults this database when determining the trustworthiness of information.

[1375] Specific examples

[1376] Consider a scenario where a user copies and pastes the URL of a news article they found on a social networking site into an input field on the system and presses the send button.

[1377] 1. Enter the URL on the user's device and send it

[1378] The user enters the URL of a news article and presses the send button.

[1379] 2. Receive URL and extract text on the server

[1380] The server scrapes the received URL and extracts the article text.

[1381] 3. Perform natural language processing (NLP) on the server

[1382] The server performs natural language processing on the extracted text to extract important keywords and assertive parts.

[1383] 4. The server determines the reliability

[1384] The server matches the extracted keywords with sources in a trusted database and evaluates the degree of match.

[1385] 5. Generate and send the report on the server

[1386] The server generates a report summarizing the evaluation results and their rationale and sends it to the user's terminal.

[1387] 6. Display the report on the user's device

[1388] The received report is displayed on the user's terminal, and the user can check the reliability of the news article and its basis.

[1389] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions.

[1390] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1391] Step 1:

[1392] Entering and submitting a URL

[1393] The user enters the URL of a news article into the system's input field using a computer or smartphone browser and presses the send button.

[1394] Input: News article URL

[1395] Output: A request to send the URL to the server

[1396] Specifically, users copy the URL of an article they find on a social networking site or news site, paste it into the system's input field, and then click the send button, which sends the URL to the server.

[1397] Step 2:

[1398] Receiving URLs and scraping web pages

[1399] The server receives the URL sent from the user terminal and extracts the content of the web page using a scraping tool (e.g., BeautifulSoup).

[1400] Input: URL sent by user

[1401] Output: Extracted article text

[1402] Specifically, the server checks whether the received URL is in a valid format, analyzes the HTML structure, and <article>Extracts article text from a specified selector, such as a tag.

[1403] Step 3:

[1404] Natural language processing of extracted text

[1405] The server analyzes the extracted article text using natural language processing technology.

[1406] Input: Article text extracted by scraping

[1407] Output: Tokenized text, stemmed word list, part-of-speech tagged text

[1408] Specifically, the server uses the NLTK and Spacy libraries to perform the following tasks:

[1409] Tokenization: Splitting text into words and phrases. For example, splitting "Climate change will become serious" into "Climate change", "but", and "become serious".

[1410] Stemming: Extracting the root of a word. Example: "become serious" becomes "serious."

[1411] Part-of-speech tagging: Assign a part-of-speech tag to each word, e.g., "climate change / noun", "ga / particle", "serious / adjective", "naru / verb".

[1412] Step 4:

[1413] Determining reliability

[1414] The server matches the extracted keywords and phrases with sources in a trusted database and evaluates the degree of match.

[1415] Input: A list of tokenized and stemmed keywords

[1416] Output: Numerical evaluation of reliability, matching result data

[1417] Specifically, the server evaluates the degree of match between keywords and phrases and reliable sources in the database (e.g., academic paper databases or reliable news sites). It then uses a similarity calculation algorithm to quantify the degree of match and makes a judgment, such as "if the degree of match is 80% or higher, the confidence level is 90%."

[1418] Step 5:

[1419] Generate and send reports

[1420] The server generates a report that integrates the results of the reliability judgment and the basis for it, and transmits it to the user terminal.

[1421] Input: Numerical evaluation of reliability, matching result data

[1422] Output: Generated report, request to send to user terminal

[1423] Specifically, the server creates a report based on the results of the reliability assessment. The report includes the reliability value, the matching results, and the extracted keywords. The report is then sent to the user's device.

[1424] Step 6:

[1425] Viewing Reports

[1426] The user terminal receives the report sent from the server and displays it to the user.

[1427] Input: Report sent from the server

[1428] Output: Report displayed in a user-readable format

[1429] Specifically, the user device displays the received report on a web page, where the user can check the reliability of the news article and its basis. For example, it may state that the reliability is 90% and that multiple reliable sources agree.

[1430] (Application example 1)

[1431] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1432] The Internet is overflowing with news articles and information, making it difficult to quickly and accurately assess their reliability. This puts users at risk of being misled by unreliable information and making incorrect decisions. Users of mobile devices, such as smartphones, lack easy ways to verify information's reliability. This invention aims to solve these problems and enable users to make decisions based on accurate information.

[1433] 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.

[1434] In this invention, the server includes: means for receiving information input by a user; means for acquiring the content of the information from a specified information source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a reliable database to determine its reliability; means for generating a report of the determination result and its basis and providing it to the user; means for having an interface applicable to a mobile device and operable directly by the user from the device; and means for having an application for analyzing the URL of a news article to determine its reliability and providing the result. This allows users to easily check the reliability of news articles on their mobile devices such as smartphones.

[1435] "Means for receiving information entered by the user" refers to the interface or form that allows a user to enter information such as the URL of a news article using a device such as a smartphone or PC.

[1436] "Means of obtaining the content of the information from a specified source" refers to a method of extracting article text from a web page on the Internet using web scraping technology, etc., based on the URL entered by the user.

[1437] "Natural language processing means for processing acquired information and extracting assertive parts and important keywords" refers to technology that applies natural language processing techniques such as tokenization, stemming, and part-of-speech tagging to acquired article text to extract important keywords and phrases and analyze the article content.

[1438] "Methods of determining reliability by comparing with a reliable database" refers to a method of determining the reliability of an article by comparing extracted keywords and phrases with a database containing reliable information sources and using an algorithm to evaluate the degree of match.

[1439] "Means for generating a report that provides the results of a reliability assessment and their rationale, and presents it to the user" refers to a technology that integrates the results of a reliability assessment and their rationale, generates a report that can be presented to the user in an easy-to-understand manner, and sends it to the user's terminal.

[1440] "Means that have an interface applicable to mobile devices and allow users to operate directly from the device" refers to a user interface that can be operated on mobile devices such as smartphones and tablets. This is implemented as a smartphone application, allowing users to easily operate it.

[1441] "Means having an application that analyzes the URL of a news article, determines its reliability, and provides the results" refers to an application that has the function of automatically analyzing the URL of a news article entered by a user, determining its reliability, and providing the results to the user.

[1442] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, and a database.

[1443] A user terminal is a device into which a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a smartphone or PC browser. The user uses a smartphone application to input the URL of a news article and presses the send button. This sends the URL to the server.

[1444] The server plays the primary role of analyzing the received information and determining its reliability. Specifically, it first scrapes the web page from the received URL and extracts the article text. This is done using a Python library called "BeautifulSoup." Next, natural language processing techniques are used on this text to perform tokenization, stemming, and part-of-speech tagging. In this case, a natural language processing library called "spaCy" is used. This allows important keywords and phrases to be extracted.

[1445] The server then compares the extracted keywords and phrases with information sources in a trusted database and evaluates the degree of match. The trusted database stores reliable news sites, academic papers, etc. The server then uses this information to determine the reliability of the information. For example, if a claim in the text matches multiple reliable sources in the database, the server evaluates the reliability of the claim.

[1446] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[1447] As a concrete example, let's say a user enters the URL "https: / / example.com / news-article". The News Verifier application scrapes the article from this URL, extracts the text, and analyzes it. As a result of the analysis, certain keywords are extracted and compared with trusted database information. If the result is 80% confidence, the following report will be generated:

[1448] Example prompt sentence:

[1449] Check the authenticity of this URL with the News Verifier app: https: / / example.com / news-article

[1450] Generated response:

[1451] Confidence: 80%

[1452] Evidence: 80% match with trusted sources in our database. This article can be treated as relatively reliable information.

[1453] This allows users to easily determine the reliability of news articles for themselves.

[1454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1455] Step 1:

[1456] A user opens the News Verifier app, enters the URL of a news article, and presses the send button. The user's device sends this URL to the server. The input is the URL entered by the user, and the output is the URL sent to the server. Specific operations include the user entering the URL from the application screen on their smartphone.

[1457] Step 2:

[1458] The server uses the received URL to scrape the web page. Here, we use "BeautifulSoup." The input is the URL sent in step 1, and the output is the article text of the web page. The specific operation involves the server accessing the specified URL and extracting the article text from the HTML code.

[1459] Step 3:

[1460] The server performs natural language processing on the extracted article text. Here, it uses "spaCy" to perform tokenization, stemming, and part-of-speech tagging. The input is the article text extracted in step 2, and the output is a list of important keywords and phrases. Specifically, it splits the text into individual words, returns it to its original form, and tags it for parts of speech.

[1461] Step 4:

[1462] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. It uses a proprietary algorithm. The input is the list of keywords and phrases obtained in step 3, and the output is a score evaluating the degree of match. Specifically, it retrieves relevant information from the database, compares it with the keywords and phrases, and calculates the degree of match.

[1463] Step 5:

[1464] The server generates a report that integrates the reliability judgment results and their rationale based on the matching evaluation score. The input is the matching evaluation score obtained in step 4, and the output is a report to be provided to the user. Specifically, the server creates a reliability value and rationale text based on the evaluation score, and compiles these into a single report.

[1465] Step 6:

[1466] The generated report is sent to the user's terminal, where the user views it. The input is the report generated in step 5, and the output is the report displayed on the user's terminal. Specific operations include the server sending the report to the user's terminal and the user viewing the report on the screen of their smartphone.

[1467] 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.

[1468] The present invention relates to a system that efficiently and accurately judges the reliability of information entered by a user and presents the results and the reasons for the judgement. This system is composed of a user terminal, a server, an emotion engine, and a database.

[1469] System configuration

[1470] User terminal

[1471] A user terminal is a device where a user inputs information through an input means. For example, a user can input the URL of a social networking site or news article from a browser on a PC or smartphone. The user terminal provides an interface for sending the input information to a server. If necessary, the input data can also be sent to an emotion engine for analyzing the user's emotions.

[1472] server

[1473] The server plays the main role of analyzing the received information and determining its reliability. Specifically, it has the following functions:

[1474] 1. URL analysis

[1475] Scrape the web page from the URL you receive and extract the article text.

[1476] 2. Natural Language Processing (NLP)

[1477] The scraped text is summarized using natural language processing techniques such as tokenization, stemming, and part-of-speech tagging, and important keywords are extracted.

[1478] 3. Reliability assessment

[1479] The extracted keywords and phrases are then compared against a trusted database and an algorithm is run to assess the degree of match.

[1480] 4. Report Generation

[1481] A report is generated that integrates the judgment results and their rationale and is provided to the user.

[1482] Emotion Engine

[1483] The emotion engine analyzes the information and behavioral patterns entered by the user and has the function of recognizing the user's emotions. Specifically, it has the following functions:

[1484] 1. Emotion analysis

[1485] The system analyzes the text and behavioral data entered by the user and determines their emotions using natural language processing technology.

[1486] 2. Relevance Analysis

[1487] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[1488] 3. Report customization

[1489] Customize the results and report content based on the recognized user sentiment.

[1490] Database

[1491] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[1492] Program processing

[1493] When a user enters the URL of a news article on their device and presses the send button, the URL is sent to the server. The server first scrapes the web page from the received URL to extract the article text. It then uses natural language processing techniques to tokenize, stem, and tag parts of speech on this text to extract important keywords and phrases.

[1494] The server then compares the extracted keywords and phrases with trusted sources in its database and evaluates the degree of match. For example, if a claim in the text matches multiple trusted sources in the database, the claim is rated as highly trustworthy.

[1495] Furthermore, an emotion engine analyzes the text entered by the user to determine their emotion. For example, it recognizes various emotions such as positive, negative, surprise, and anger. Based on the recognized emotion, the report content is customized. In the case of a positive emotion, parts with many highly reliable sources are highlighted, while in the case of a negative emotion, uncertain elements are explained in detail.

[1496] Finally, the server generates a report that integrates the results of the assessment and their rationale, and sends it to the user's device. This report is displayed on the user's device, allowing the user to check the article's reliability and its rationale.

[1497] Specific examples

[1498] Example: Scrutinizing news articles circulating on social media

[1499] Users copy and paste the URL of a news article they found on a social networking site into the system's input field, and when they press the send button, the URL is sent to the server.

[1500] The server first receives the URL and then scrapes the web page to extract the article text.

[1501] Next, natural language processing techniques are used to analyze the text and extract key keywords and argumentative parts.

[1502] The extracted information is compared with a reliable database to assess the degree of match.

[1503] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[1504] Furthermore, the emotion engine analyzes the data entered by the user and determines the user's emotion.

[1505] Moderately customize report content based on user sentiment.

[1506] A report is generated and sent to the user terminal.

[1507] This system allows users to easily check the reliability of news articles and social media information and make appropriate decisions based on that information. It also recognizes users' emotions and customizes the report content, enabling more personalized information provision.

[1508] The processing flow will be explained below.

[1509] Step 1:

[1510] User: Enters information obtained from a social networking site or news site into the device's input field. For example, copies and enters the URL of a news article.

[1511] Step 2:

[1512] Terminal: Sends the URL entered by the user to the server, and data is sent to the server via an HTTP request.

[1513] Step 3:

[1514] Server: Scrape the web page from the received URL, for example by parsing the HTML page using a library such as BeautifulSoup or Scrapy and extracting the article text.

[1515] Step 4:

[1516] Server: Clean the scraped text data. For example, use regular expressions to remove unnecessary HTML tags and advertisements to obtain pure text.

[1517] Step 5:

[1518] Server: Analyzes the cleaned text using Natural Language Processing (NLP) techniques, specifically by following these steps:

[1519] Tokenization: Breaking text into individual words or phrases.

[1520] Stemming: Extracting the roots of words.

[1521] Part-of-speech tagging: Assigning a part of speech (verb, noun, etc.) to each word.

[1522] Step 6:

[1523] Server: Extracts important keywords and phrases from the NLP analysis results, for example, using Term Frequency-Inverse Document Frequency (TF-IDF) to identify important words and phrases in the text.

[1524] Step 7:

[1525] Server: Checks the extracted keywords and phrases against a trusted database to determine their trustworthiness, for example by running an algorithm that checks for matches with sources in the database.

[1526] Step 8:

[1527] Server: Calculates a confidence score based on the degree of match and generates a judgment result. For example, it calculates a specific confidence score such as "The confidence level of this article is 90%."

[1528] Step 9:

[1529] Server: Integrates the judgement results and evidence information and generates a report, which includes a confidence score and a list of referenced sources.

[1530] Step 10:

[1531] Server: Sends the text entered by the user to the emotion engine for sentiment analysis, for example, analyzing user comments and reactions to articles.

[1532] Step 11:

[1533] Server: The emotion engine determines the user's emotions. For example, it uses natural language processing technology to recognize positive, negative, and other emotions.

[1534] Step 12:

[1535] Server: Customize report content based on perceived user sentiment, e.g., detailing low-trust sources if negative sentiment is detected.

[1536] Step 13:

[1537] Server: Sends customized reports to user devices.

[1538] Step 14:

[1539] On the device: The received report is displayed in a user interface, with the news article's reliability score and its rationale clearly presented.

[1540] Step 15:

[1541] Users: Review the analysis results and rationale displayed, and can request more information if needed, for example by clicking on links to the sources of the rationale.

[1542] Example 2

[1543] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1544] As the amount of information on the Internet increases, users need to be able to quickly and accurately evaluate the reliability of the information they see. However, current systems are insufficient in assessing reliability, which puts users at risk of believing incorrect information. Furthermore, information provided does not take into account the user's emotions, resulting in a lack of improvement in the user experience.

[1545] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving information input by a user, means for acquiring the content of the information from a specified information source, language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable storage device to determine its reliability, means for analyzing the information and behavioral patterns input by the user and determining the user's emotions, and means for generating a determination result and its basis and creating a report to be provided to the user. This allows the user to quickly and accurately evaluate the reliability of information, and also makes it possible to provide a personalized report based on the user's emotions.

[1546] The "means for receiving information input by the user" is a function for receiving URLs and text data provided by the user via the input interface.

[1547] "Means of obtaining the content of the information from the specified source" refers to the function of obtaining the target web page or data from the input URL or specified source using scraping or other techniques.

[1548] "Language processing means for processing acquired information and extracting assertive parts and important keywords" refers to a function that applies natural language processing technology to the extracted text, performing tokenization, stemming, and part-of-speech tagging to extract assertive parts and important keywords.

[1549] "Means for comparing extracted information with trusted storage to determine trustworthiness" refers to a function that uses algorithms and databases to compare extracted keywords and phrases with trusted sources, evaluate the degree of match, and determine the trustworthiness of the information.

[1550] "Means for analyzing information entered by the user and behavioral patterns to determine the user's emotions" refers to a function that analyzes the user's input and behavioral data and uses natural language processing technology to recognize the user's emotions.

[1551] "Means for generating a report that generates a judgment result and its basis and provides it to the user" is a function that compiles an evaluation of the reliability of information and its basis, and generates a report that can be provided to the user in an easy-to-understand manner.

[1552] The present invention is a system that automatically determines the reliability of information entered by a user and provides the user with an appropriate report. This system is composed of a user terminal, a server, an emotion analysis engine, and a database.

[1553] User terminal

[1554] A user terminal is a device through which a user inputs information via input means. For example, a news article or a URL of a social networking site can be entered from the browser of a personal computer or smartphone. The user terminal provides an interface for sending the entered information to a server. It can also send data to an emotion engine to analyze the user's emotions as needed.

[1555] server

[1556] The server performs the primary function of analyzing the received information and determining its reliability. Specifically, it uses the following software and technologies:

[1557] 1. Web page scraping

[1558] The server scrapes the web page from the URL received from the user and extracts the article text, using software such as "BeautifulSoup" or "Scrapy."

[1559] 2. Natural Language Processing (NLP)

[1560] The server tokenizes the extracted article text using NLTK and spaCy, and performs stemming and part-of-speech tagging to extract important keywords and phrases.

[1561] 3. Reliability assessment

[1562] The server uses Elasticsearch to compare extracted keywords and phrases with trusted sources and evaluates the degree of match. Based on the results, it determines the trustworthiness of the information.

[1563] 4. Report Generation

[1564] The server aggregates the results and rationale of the confidence assessment and generates a report to provide to the user.

[1565] Emotion Engine

[1566] The emotion engine has the function of analyzing the information and behavioral patterns entered by the user and recognizing the user's emotions. Specifically, it uses the following technologies:

[1567] 1. Emotion analysis

[1568] The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from user input text.

[1569] 2. Relevance Analysis

[1570] Analyze the relationship between the sentiment analysis results and the article's main points and important keywords.

[1571] 3. Report customization

[1572] Customize the report content based on the sentiment analysis results, for example highlighting reliable sources for positive sentiment and providing detailed explanations of uncertainties for negative sentiment.

[1573] Database

[1574] The database contains data collected from trusted sources, such as trusted news sites and academic papers, and the server consults this database when determining the trustworthiness of information.

[1575] Specific examples

[1576] When a user pastes the URL of a news article into the system's input field and presses the send button, the following steps occur:

[1577] The server receives the URL and scrapes the web page to extract the article text.

[1578] The server uses natural language processing technology to analyze the text and extract important keywords and key points.

[1579] The server compares the extracted information with a trusted database and evaluates the degree of match.

[1580] Based on the evaluation results, a report will be compiled stating the article's reliability (e.g., 90% reliable) and the basis for that reliability (e.g., similar claims have been confirmed in multiple reliable sources).

[1581] The emotion engine analyzes the user's input data and determines the user's emotion.

[1582] The report content is appropriately customized based on the user's sentiment.

[1583] Finally, the report is sent to the user terminal and the user confirms it.

[1584] Prompt Sentence Examples

[1585] By entering the URL of an article along with the prompt "Please rate the credibility of this news article:", the system performs text analysis, credibility rating, sentiment analysis, etc. on the article and provides the results.

[1586] This allows users to easily check the reliability of news articles and social media information, enabling them to make appropriate decisions. Furthermore, the report content is customized based on the user's emotions, achieving more personalized information provision.

[1587] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] User

[1590] The user enters the URL of a news article into the input field of the device and presses the send button. The user opens a browser, copies the URL of an article of interest from a social networking site or news site, and pastes it into the input field of the system. Then, the user clicks the send button. As a result, the input information is the URL of the news article, and the output is an HTTP request.

[1591] Step 2:

[1592] Terminal

[1593] The device sends the URL entered by the user to the server. When the user presses the send button, the device's browser generates an HTTP request and sends it to the specified server. The input in this step is the news article URL and the HTTP request, and the output is the URL sent to the server.

[1594] Step 3:

[1595] server

[1596] The server scrapes the web page from the received URL and extracts the article text. The server uses "BeautifulSoup" or "Scrapy" to get the web page content from the URL and extract the article text. The input in this step is the URL and the output is the extracted article text.

[1597] Step 4:

[1598] server

[1599] The server applies natural language processing to the extracted article text. Using NLTK or spaCy, the server tokenizes the article text and performs stemming and part-of-speech tagging. This allows important keywords and phrases to be extracted. The input for this step is the article text, and the output is keywords and phrases.

[1600] Step 5:

[1601] server

[1602] The server compares the extracted keywords and phrases with a trusted database to assess the degree of match. The server uses Elasticsearch to compare the keywords and phrases with trusted sources in the database and calculate the degree of match. The input for this step is the keywords and phrases and the database information, and the output is the result of the match assessment.

[1603] Step 6:

[1604] Emotion Engine

[1605] The emotion engine analyzes the information and behavioral patterns entered by the user to determine the user's emotion. The emotion engine uses IBM Watson NLU and Google Cloud Natural Language API to recognize emotions from the user's input text. The input for this step is the user's input data, and the output is the recognized emotion.

[1606] Step 7:

[1607] server

[1608] The server generates a report based on the reliability evaluation results and sentiment analysis results. The server integrates the reliability evaluation results with the emotion engine analysis results and creates a report for the user. The report contains the reliability value and its rationale. The inputs to this step are the reliability evaluation results and sentiment analysis results, and the output is a report.

[1609] Step 8:

[1610] Emotion Engine

[1611] The emotion engine customizes the report content based on the user's emotion. The emotion engine adjusts the report depending on the user's emotion. For example, a positive emotion might highlight areas of high confidence, while a negative emotion might provide more detailed explanations of uncertainties. The inputs to this step are the report and the user's emotion, and the output is a customized report.

[1612] Step 9:

[1613] server

[1614] The server sends the generated report to the user terminal. The server returns the generated report to the user terminal as an HTTP response. The report is then displayed in the browser on the user terminal. The input of this step is the report, and the output is the sent report.

[1615] Step 10:

[1616] Terminal

[1617] The terminal displays the received report. The browser on the user's terminal analyzes the contents of the report received from the server and displays it on the screen. The user can check this and understand the reliability of the article and its basis. The input of this step is the report, and the output is the displayed report.

[1618] (Application example 2)

[1619] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] Internet advertisements often contain information of questionable reliability, making it difficult for users to judge such information. This problem is particularly serious in today's world, where fraud and fake news are easily spread. The present invention aims to solve this problem by providing a system that efficiently and accurately judges the reliability of advertising information viewed by users on the Internet and presents the results and evidence.

[1621] The identification process by the identification 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 means for receiving information input by a user, means for acquiring the content of the information from a specified information source, natural language processing means for processing the acquired information and extracting assertive parts and important keywords, means for comparing the extracted information with a reliable database to determine its reliability, means for generating a report to provide the user with the determination result and its basis, and means for determining the reliability of advertisements in real time and providing the determination result. This allows users to accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[1622] A "user" is a person or entity that utilizes the system to enter and receive information.

[1623] "Information" refers to data such as URLs and text data entered by users, and the contents of web pages retrieved by the system.

[1624] A "designated source" is a pre-defined source, such as a website or database, that is referenced for assessing trustworthiness.

[1625] "Natural language processing" is a technology that analyzes input text data using algorithms such as tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases.

[1626] "Reliability" is a numerical value or index that evaluates the degree to which extracted information matches a reliable database.

[1627] A "database" is a collection of information that stores data collected from reliable sources.

[1628] "Report creation" refers to the creation of documents and presentation formats that integrate the results and their rationale for analysis and evaluation results in order to provide them to users.

[1629] "Advertising" means information or content displayed on the Internet for the purpose of promoting a product or service.

[1630] A "URL" is a string of characters that specifies the location of a web page and points to an Internet resource such as an advertisement or news article.

[1631] "Analysis" is the process of breaking down acquired information using algorithms and methods to understand and evaluate its structure and content.

[1632] The "judgment result" is an evaluation or judgment obtained as a result of the analysis and reliability judgment.

[1633] "Real-time" refers to the time characteristics of a process in which information is obtained and evaluated immediately.

[1634] Overall system configuration

[1635] The present invention provides a system that efficiently and accurately determines the reliability of information entered by a user and presents the results and the basis for the determination. The system is composed of a user terminal, a server, and multiple components. The system will be described in detail below.

[1636] User terminal

[1637] The user terminal is a device through which the user inputs information and receives the judgment results. For example, the user inputs the URL of the advertisement page using a smartphone or a PC.

[1638] server

[1639] The server is the central information processing center. It provides the following main functions:

[1640] Data Acquisition Method

[1641] It scrapes web pages from URLs entered by users and extracts the text from those pages. The hardware used is a high-performance server, and the software uses the widely used Python requests and BeautifulSoup libraries.

[1642] Natural language processing tools

[1643] The acquired text is then subjected to natural language processing (NLP) to extract important keywords and phrases. This processing is performed using a NLP library such as NLTK, which performs tokenization, stemming, and part-of-speech tagging.

[1644] Reliability determination means

[1645] The extracted keywords and phrases are compared with a reliable database to evaluate their reliability based on the degree of match. This process uses a database collected from pre-prepared, reliable sources. The algorithm used is a match check algorithm.

[1646] Emotion analysis means

[1647] The system analyzes user sentiment based on the captured text. For sentiment analysis, it uses the VADER Sentiment library, which calculates a positive, negative, or neutral sentiment score and reflects it in the report.

[1648] Report Creation Method

[1649] The results of the confidence assessment and sentiment analysis are integrated to generate a report and provided to the user using a custom Python script.

[1650] Specific examples

[1651] For example, the following shows the process when a user views an advertisement on a smartphone and enters the URL of the advertisement page.

[1652] A user enters the URL of an ad page in an application on their smartphone. When they press the submit button, the URL is sent to the server, which then scrapes the web page from the URL to extract text. Next, natural language processing is performed on the extracted text to analyze important keywords and phrases. This is then compared with a trusted database to assess its trustworthiness. Sentiment analysis is also performed in parallel, and a report based on the user's sentiment is generated. Finally, a trustworthiness report is displayed on the user's device, allowing the user to review the results.

[1653] Prompt Sentence Examples

[1654] The user inputs the URL of a news article. The system scrapes the web page from the input URL and extracts the text. It then uses natural language processing technology to extract important keywords from that text. It then compares the reliability of the article against a trusted database and generates a report based on the results. Finally, the generated report is sent to the user's device and displayed.

[1655] According to this invention, users can accurately evaluate the reliability of advertising information they view on the Internet and use it with peace of mind.

[1656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1657] Step 1:

[1658] The user inputs the URL of the advertisement page using a smartphone application. The input URL is sent from the user's device to the server. Input: URL of advertisement page, Output: URL sent to the server.

[1659] Step 2:

[1660] The server scrapes the web page from the received URL and extracts the text from that page. Specifically, it retrieves the web page using Python's requests library and extracts the text from the HTML using the BeautifulSoup library. Input: URL of the ad page, Output: Text of the ad page.

[1661] Step 3:

[1662] The server performs natural language processing on the extracted text. It uses Python's NLTK library to perform tokenization, stemming, and part-of-speech tagging to extract important keywords and phrases. Input: text from the ad page. Output: important keywords and phrases.

[1663] Step 4:

[1664] The server checks the extracted keywords and phrases against a trusted database, calculates the degree of match, and determines the confidence score. A specific algorithm is used to evaluate whether there is a match. Input: extracted keywords and phrases, database. Output: confidence score.

[1665] Step 5:

[1666] The server performs sentiment analysis based on the extracted text. It uses the VADER Sentiment library to calculate a positive, negative, or neutral sentiment score. Input: text of the ad page. Output: sentiment score.

[1667] Step 6:

[1668] The server integrates the results of the confidence evaluation and sentiment analysis to generate a report. The report includes confidence scores, sentiment scores, and their rationales. Input: confidence scores, sentiment scores. Output: confidence report.

[1669] Step 7:

[1670] The generated report is sent from the server to the user's device. The user can check the report on their smartphone and evaluate the reliability of the advertising information. Input: Reliability report, Output: Report displayed on the user's device.

[1671] In this way, by performing each step sequentially, the user can efficiently and accurately evaluate the reliability of the advertising information viewed on the Internet, and can use it with peace of mind.

[1672] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1673] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1674] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1675] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1676] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1677] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1678] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1679] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1680] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1681] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1682] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1683] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1684] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1685] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1686] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1687] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1688] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1689] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1690] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1691] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1692] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1693] The following is further disclosed regarding the above embodiment.

[1694] (Claim 1)

[1695] means for receiving information input from a user;

[1696] a means for obtaining the content of the information from a designated source;

[1697] natural language processing means for processing the acquired information and extracting assertions and important keywords;

[1698] means for comparing the extracted information with a trusted database to determine its reliability;

[1699] a means for generating a report of the judgment result and its basis and providing it to a user;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, wherein the natural language processing means performs tokenization, stemming, and part-of-speech tagging.

[1703] (Claim 3)

[1704] 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources in a trusted database.

[1705] "Example 1"

[1706] (Claim 1)

[1707] means for receiving information input from a user;

[1708] a means for obtaining the content of the information from a designated source;

[1709] natural language processing means for processing the acquired information and extracting assertions and important keywords;

[1710] means for comparing the extracted information with a trusted database to determine its reliability;

[1711] a means for generating a report of the judgment result and its basis and providing it to a user;

[1712] a means for a user to input a URL of a news article and to transmit the input URL;

[1713] A means for the server to scrape web pages from URLs and extract article text;

[1714] a means for the server to match the extracted keywords or phrases with data in an authoritative database and evaluate the degree of match;

[1715] A system including:

[1716] (Claim 2)

[1717] 10. The system of claim 1, wherein the natural language processing means performs tokenization, stemming, and part-of-speech tagging.

[1718] (Claim 3)

[1719] 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources in a trusted database.

[1720] "Application Example 1"

[1721] (Claim 1)

[1722] means for receiving information input from a user;

[1723] a means for obtaining the content of the information from a designated source;

[1724] natural language processing means for processing the acquired information and extracting assertions and important keywords;

[1725] means for comparing the extracted information with a trusted database to determine its reliability;

[1726] a means for generating a report of the judgment result and its basis and providing it to a user;

[1727] means having an interface applicable to a mobile device and operable by a user directly from the device;

[1728] means for analyzing the URL of a news article to determine its reliability and providing a result;

[1729] A system including:

[1730] (Claim 2)

[1731] 10. The system of claim 1, wherein the natural language processing means performs tokenization, stemming, and part-of-speech tagging.

[1732] (Claim 3)

[1733] 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources in a trusted database.

[1734] "Example 2: Combining Emotion Engines"

[1735] (Claim 1)

[1736] means for receiving information input from a user;

[1737] a means for obtaining the content of the information from a designated source;

[1738] a language processing means for processing the acquired information and extracting assertions and important keywords;

[1739] means for checking the extracted information against a trusted storage device to determine its trustworthiness;

[1740] A means for analyzing information input by a user and behavioral patterns to determine the user's emotions;

[1741] a means for generating a report of the judgment result and its basis and providing it to a user;

[1742] A system including:

[1743] (Claim 2)

[1744] 10. The system of claim 1, wherein the language processing means performs tokenization, stemming, and part-of-speech tagging.

[1745] (Claim 3)

[1746] 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources of information in a trusted store.

[1747] "Application example 2 when combining emotion engines"

[1748] (Claim 1)

[1749] means for receiving information input from a user;

[1750] a means for obtaining the content of the information from a designated source;

[1751] natural language processing means for processing the acquired information and extracting assertions and important keywords;

[1752] means for comparing the extracted information with a trusted database to determine its reliability;

[1753] a means for generating a report of the judgment result and its basis and providing it to a user;

[1754] A means for determining the reliability of an advertisement in real time and providing the result of the determination;

[1755] A system including:

[1756] (Claim 2)

[1757] 10. The system of claim 1, wherein the natural language processing means performs tokenization, stemming, and part-of-speech tagging.

[1758] (Claim 3)

[1759] 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources in a trusted database.

[1760] (Claim 4)

[1761] 10. The system of claim 1, wherein the means for determining the authenticity of the advertisement in real time comprises a process for analyzing a URL of the advertisement being viewed by the user and assessing its authenticity. [Explanation of symbols]

[1762] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / article> < / url:> < / article> < / url:> < / article> < / url:> < / article>

Claims

1. means for receiving information input from a user; a means for obtaining the content of the information from a designated source; natural language processing means for processing the acquired information and extracting assertions and important keywords; means for comparing the extracted information with a trusted database to determine its reliability; a means for generating a report of the judgment result and its basis and providing it to a user; A system including:

2. The system of claim 1 , wherein the natural language processing means performs tokenization, stemming, and part-of-speech tagging.

3. 10. The system of claim 1, wherein the means for determining trustworthiness uses an algorithm that checks for consistency with sources in a trusted database.

Images