system

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting information on the Internet in real time; AI means for analyzing spelling errors, grammatical errors, and reputation information of web sites; means for determining whether a web site is a secure web site or a potentially fraudulent web site; means for scanning a URL to be accessed by a user in real time and issuing a warning to the user if the web site is a potentially fraudulent web site; and means for navigating to a secure official web site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the number of fraudulent websites on the Internet has increased, and the damage caused by users accessing these sites has become a serious problem. Typical phishing and spoofed websites are sophisticated in appearance, making them difficult for users to identify. Furthermore, conventional blacklists and whitelists require periodic manual updates, which means they lack real-time capabilities and are unable to keep up with the latest fraudulent websites. Furthermore, there is no mechanism in place to guide individual users to safe sites. Therefore, a system that can effectively and quickly detect fraudulent websites and protect users is needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes a means for collecting data on the Internet in real time, an AI means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, and a means for determining whether a website is safe or potentially fraudulent based on the analysis results. The system also includes a means for scanning URLs that users attempt to access in real time, warning users if a website is potentially fraudulent, and directing users to a safe, official website. The system also includes a database management means for regularly updating whitelists and blacklists, enabling it to respond to the latest fraudulent websites. Furthermore, the system includes a means for monitoring newly emerging websites and updating data in real time, allowing it to constantly provide emergency protection based on the latest information.

[0006] "Means for collecting data on the Internet in real time" refers to the technical means for continuously monitoring the latest website information and domain registration data on the Internet and immediately incorporating it into the system.

[0007] "Spelling error" refers to a situation in which a word is not spelled correctly, and is a criterion for detecting unnatural typos and typos.

[0008] "Grammar errors" refer to sentences or phrases that do not follow grammatical rules, and are a criterion for identifying unnatural expressions that differ from natural language expressions.

[0009] "Reputation data" is information that includes past user ratings and reviews of a particular website, as well as reports from third-party organizations.

[0010] "AI means" refers to the use of artificial intelligence techniques to analyze data and learn and recognize specific patterns and characteristics.

[0011] "Analysis results" refers to the evaluation scores and risk assessment information generated after the AI ​​means analyzes the collected data.

[0012] A "safe website" is a website that is deemed to be highly reliable and safe for users to use.

[0013] A "potentially fraudulent website" is a website that is deemed to be high risk and likely to defraud users.

[0014] "User warning measures" are technical measures that display a warning message to a user when the user attempts to access a potentially fraudulent website.

[0015] "Means of redirecting users to safe official websites" refers to technical measures to present users with reliable official websites instead of the fraudulent sites they are attempting to access.

[0016] "Database management means" refers to the database and its management system required to maintain and update whitelists and blacklists.

[0017] "Means for monitoring emerging websites" means technological means for continuously monitoring emerging websites on the Internet and updating the data in real time. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0040] System Configuration

[0041] 1. Server

[0042] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[0043] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[0044] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[0045] 2. Terminal

[0046] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[0047] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[0048] 3. Users

[0049] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[0050] Specific program processing explanation

[0051] After the system is launched, the server first collects whitelists and blacklists from the Internet, as well as data on a wide variety of websites. This collected data is then analyzed by AI to evaluate the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites are determined, as are potentially fraudulent websites, and the blacklist or whitelist is updated accordingly.

[0052] When a user attempts to access a specific website, the device sends the URL to the server. The server scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and provides the URL of a safe, official website. The user can then confirm the warning and be directed to the safe website, thereby protecting them from fraud.

[0053] Specific examples

[0054] For example, consider a case where a user searches for "online bank" on the web and attempts to access a fake bank site. When the user enters the URL "fake-bank.example" and sends it to the server, the server analyzes the URL using AI. Based on spelling mistakes in the URL (e.g., 'bank' becomes 'bannk'), grammatical errors, and past reputation data, the AI ​​determines that the site is high risk. As a result, the server immediately issues a warning to the device, displays a message saying "This site may be fraudulent," and provides a link to a safe, official bank site (e.g., 'official-bank.com'). The user confirms this and proceeds to the official website to avoid becoming a victim of fraud.

[0055] Thus, the system of the present invention has specific means and actions to detect fraudulent sites in real time and protect users.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[0059] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[0060] Step 2:

[0061] The server passes the collected data to an AI analysis engine for initial analysis.

[0062] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[0063] Step 3:

[0064] The server updates the whitelist and blacklist based on the results of the initial analysis.

[0065] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[0066] Step 4:

[0067] The server monitors the Internet for new websites in real time.

[0068] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[0069] Step 5:

[0070] A user attempts to access a particular website.

[0071] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[0072] Step 6:

[0073] The server scans and determines in real time the URLs that users attempt to access.

[0074] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[0075] Step 7:

[0076] If the server determines that the site is fraudulent, it will issue a warning to the user.

[0077] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[0078] Step 8:

[0079] Provide users with a link to a secure official website.

[0080] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[0081] Step 9:

[0082] The user visits the official secure website provided.

[0083] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[0084] In this way, the system goes through a series of steps to detect fraudulent sites in real time and direct users to safe sites.

[0085] Example 1

[0086] 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."

[0087] In recent years, the increase in fraudulent and phishing sites on the Internet has made it difficult for users to browse the web safely. To prevent users from accessing these fraudulent sites, real-time reliability assessment and immediate warnings are necessary. However, conventional methods often suffer from delays in data analysis and updates, making it difficult to provide immediate protection to users. The present invention aims to solve these problems and enable users to use the Internet safely.

[0088] 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.

[0089] In this invention, the server includes means for collecting the latest data from the Internet in real time, means for analyzing websites for spelling mistakes, grammatical errors, and past reputation data using an AI model based on the collected data, means for evaluating the safety of websites based on the analysis results and updating blacklists and whitelists in real time, means for scanning URLs that users attempt to access in real time, warning users if there is a possibility of fraud and directing them to a safer official website, and means for transmitting URLs entered by users from their terminals to the server in real time, thereby enabling users to check the safety of websites in real time, prevent access to fraudulent sites, and be quickly directed to safe official websites.

[0090] "The latest data on the Internet" refers to the latest information, such as newly published websites on the Internet, updated information on existing websites, and related evaluation data.

[0091] "Real-time collection means" refers to technologies and methods for obtaining data on the Internet instantly and without delay.

[0092] A "spelling error" refers to an error that involves different characters that are not spelled correctly.

[0093] A "grammatical error" refers to a situation in which the grammatical rules or sentence structure in a sentence are incorrect.

[0094] "Historical Reputation Data" refers to previous user reviews, ratings, feedback, and credibility information about a website.

[0095] An "AI model" refers to an algorithm or system that uses machine learning, deep learning, etc. to analyze data and make predictions, classifications, and evaluations.

[0096] "Means of analysis" refers to techniques and methods for examining collected data and evaluating and classifying it based on certain criteria.

[0097] A "blacklist" refers to a list of websites that are rated as unsafe and potentially fraudulent.

[0098] A "whitelist" refers to a list of websites that are rated as highly secure and trustworthy.

[0099] "Real-time update methods" refers to the technology and methods used to instantly update information in databases and lists and keep them up to date.

[0100] "URL is an abbreviation for Uniform Resource Locator, and refers to the address used to indicate resources on the web (pages, images, videos, etc.).

[0101] "Scanning methods" refers to technologies and methods used to investigate and analyze the content of specific URLs or websites and assess their reliability and safety.

[0102] "Warning measures" refers to techniques or methods used to inform users when certain actions involve risks.

[0103] "Means of directing users to official websites" refers to techniques and methods that provide appropriate links and information to direct users to trusted and safe sites.

[0104] "Terminal" refers to a device that a user uses to access the Internet, and specifically includes a PC, smartphone, tablet, etc.

[0105] This invention is a system that collects, analyzes, and evaluates data on the Internet in real time. It aims to issue a warning when a user attempts to access a fraudulent website and guide the user to a safe, official website. A specific embodiment of this system is described below.

[0106] Server Roles

[0107] 1. Data collection

[0108] The server collects the latest data from the Internet using web scraping tools (e.g., BeautifulSoup, Scrapy) and Web APIs (e.g., Google Safe Browsing API).

[0109] What it does: It uses BeautifulSoup to get the HTML data of a specific webpage, extracts the URL of the new website and the existing reputation data, and calls the Google Safe Browsing API to get a list of dangerous websites.

[0110] 2. Data Analysis

[0111] The collected data is analyzed using natural language processing tools (e.g., spaCy, NLTK) and machine learning models (e.g., TensorFlow, PyTorch).

[0112] What it does: It uses spaCy to tokenize the text content of websites and detect spelling and grammatical errors, and then uses a TensorFlow model to calculate a risk score from the analysis results and assess their trustworthiness.

[0113] 3. Blacklist and Whitelist Management

[0114] Based on the analysis results, the server rates websites and updates the blacklist and whitelist in real time.

[0115] What it does: Accesses a PostgreSQL database and records the evaluation results. It adds websites that are likely to be fraudulent to a blacklist and websites that are deemed safe to a whitelist.

[0116] Device Role

[0117] 1. Sending the URL

[0118] The URL the user attempts to access is sent from the device to the server in real time.

[0119] Specific behavior: When a user enters a URL into a web browser, the device generates an HTTP request containing that information and sends it to the server.

[0120] User Roles

[0121] 1. Web Browsing

[0122] When a user browses the internet using a browser, the URLs they try to access are sent via their device to a server where they are scanned and analyzed.

[0123] Specific operation: When the user enters the URL they want to access and presses the Enter key, the URL is immediately sent from the device to the server.

[0124] Specific examples

[0125] For example, consider a user searching for "online banking" and attempting to access a fake banking site.

[0126] When a user enters the URL "fake-bank.example", this information is sent from the device to the server.

[0127] The server analyzes the URL in real time using AI, for example by looking for misspellings (such as "bannk") and past reputation data to determine whether it is high risk.

[0128] If the server determines that the URL is dangerous, it will send a warning message to the device saying, "This site may be fraudulent," and provide a link to the safe official website ("official-bank.com").

[0129] Users can avoid becoming a victim of fraud by checking the warning and going to the official website.

[0130] Prompt Sentence Examples

[0131] "When a user searches for an online bank and tries to access a fraudulent site like 'fake-bank.example', explain how this system will detect the site as fraudulent and issue a warning."

[0132] This invention collects data on the Internet in real time and analyzes it instantly to prevent users from accessing fraudulent sites and provide a safer web experience.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] The server collects the latest data from the Internet.

[0136] Specific operations and inputs / outputs:

[0137] Input: List of web page URLs, API endpoints.

[0138] Processing: Use BeautifulSoup to get the HTML data of the specified web page, and use Scrapy to extract the necessary information (e.g., URLs and reputation data of new websites). Also, obtain dangerous site information from the Google Safe Browsing API.

[0139] Output: Extracted website information and dangerous site list.

[0140] Step 2:

[0141] The server analyzes the collected data.

[0142] Specific operations and inputs / outputs:

[0143] Input: HTML data, website URL list, dangerous site information.

[0144] Processing: Uses spaCy to tokenize text content and detect spelling and grammar errors. Uses a TensorFlow model to calculate a website reputation score.

[0145] Output: Analysis results (spelling mistakes, grammar errors, evaluation score).

[0146] Step 3:

[0147] The server manages the blacklist and whitelist.

[0148] Specific operations and inputs / outputs:

[0149] Input: Analysis results (rating score), website URL list.

[0150] Action: Access the PostgreSQL database and update the blacklist and whitelist based on the evaluation results. Add high-risk sites to the blacklist and safe sites to the whitelist.

[0151] Output: Updated blacklists and whitelists.

[0152] Step 4:

[0153] The device sends the URL entered by the user to the server.

[0154] Specific operations and inputs / outputs:

[0155] Input: The URL entered by the user.

[0156] Processing: After the user enters a URL into the browser, the device generates an HTTP request and sends the URL information to the server.

[0157] Output: Data sent to the server (URL).

[0158] Step 5:

[0159] The server scans and evaluates the URLs it receives.

[0160] Specific operations and inputs / outputs:

[0161] Input: URL sent from device, blacklist, whitelist.

[0162] Processing: The server searches the received URL in the form of a database query to see if it is included in the blacklist or whitelist. If it is not included, a new real-time analysis is performed using AI.

[0163] Output: URL evaluation result (safe, dangerous, etc.).

[0164] Step 6:

[0165] The device will display a warning to the user.

[0166] Specific operations and inputs / outputs:

[0167] Input: URL evaluation results received from the server.

[0168] Action: For URLs that are determined to be high risk, the device will generate a browser pop-up with the message "This site may be fraudulent" and provide a link to the official, safe site.

[0169] Output: A warning message and a safe link to display to the user.

[0170] (Application example 1)

[0171] 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."

[0172] When using the Internet, the risk of users accessing fraudulent websites is increasing. However, current security systems lack the ability to issue real-time warnings the moment a user attempts to access a fraudulent website and quickly guide the user to a safe site. As a result, cases of users accessing fraudulent websites continue to increase. Furthermore, the lack of consistency in warnings, especially for users using visual devices, makes it difficult to immediately prevent access to fraudulent websites.

[0173] 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.

[0174] In this invention, the server includes means for collecting data on the Internet in real time, means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and warning the user if the URL is potentially fraudulent, means for directing the user to a safe, official website, and means for displaying a warning on the user's visual device in real time. This allows the user to be informed of the risk of encountering a fraudulent website in real time while using the Internet, and to be promptly directed to a safe website by receiving a warning via the visual device, thereby preventing fraud.

[0175] "Means for collecting data on the Internet" refers to devices or software that have the ability to obtain data from a wide variety of websites on the Internet in real time.

[0176] "AI methods for analyzing website spelling mistakes, grammatical errors, and reputation data" refers to artificial intelligence technology that analyzes the content of websites based on collected data and uses specific algorithms to evaluate spelling mistakes, grammatical errors, and past reputation information.

[0177] "Means for determining whether a website is safe or potentially fraudulent based on the results of analysis" refers to devices or software that have the ability to evaluate and classify whether a specific website is safe or potentially fraudulent based on the results of AI analysis.

[0178] "Means for scanning URLs that users attempt to access in real time and issuing a warning to users if there is a possibility of fraud" refers to devices or software that instantly analyzes the URL entered by the user and displays a warning to the user if the URL is determined to pose a risk of fraud.

[0179] "Means for redirecting users to a safe official website" refers to devices or software that have the function of redirecting users who attempt to access a potentially fraudulent website to a pre-defined safe official website.

[0180] "Means for displaying a real-time warning to a user's visual device" refers to devices or software that have the ability to instantly display a warning to a user about accessing a fraudulent website on a visual device such as smart glasses or a head-mounted display.

[0181] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0182] server

[0183] The server is responsible for the following functions:

[0184] 1. Data Collection:

[0185] The server collects data from a wide variety of websites on the Internet in real time. It also continuously monitors new websites and incorporates this data into the system. This ensures that the whitelist and blacklist databases are always kept up to date.

[0186] 2. Data Analysis:

[0187] The server uses AI to analyze the collected data, including spelling mistakes, grammatical errors, and reputation data of websites. The AI ​​model used is RiskAssessmentModel. The server also analyzes past evaluation data to assess the risk of each website.

[0188] 3. Judgment and Updates:

[0189] Based on the analysis results, it determines which websites are safe and which may be fraudulent. The results are immediately reflected in the database, and blacklists and whitelists are updated.

[0190] Terminal

[0191] The terminal is responsible for the following functions:

[0192] 1. Real-time scanning:

[0193] The URL that the user is trying to access is sent to the server in real time, and the device immediately relays the user's access request to the server and receives the scan results.

[0194] 2. Warnings and Guidance:

[0195] If a potential scam is detected, the device will immediately display a warning to the user and provide a URL to a safe, official website, which will be displayed via a visual device (e.g., smart glasses).

[0196] user

[0197] Users use a web browser to browse the Internet. When a user attempts to access a particular website, the URL is sent via the device to a server where it is scanned and analyzed.

[0198] Specific processing and implementation method

[0199] The server collects whitelists, blacklists, and website data from the internet in real time. This data is analyzed by an AI model (RiskAssessmentModel), which evaluates spelling mistakes, grammatical errors, and past reputation data to determine the risk of a fraudulent website.

[0200] When a user tries to access "fake-bank.example" using smart glasses, the URL is sent to the server. If the server analyzes the URL and determines it to be high-risk, it immediately displays a warning on the smart glasses and provides a link to the official, secure website ("official-bank.com").

[0201] For example, a sample prompt might look like this:

[0202] "The website I'm trying to access has the URL "fake-bank.example". Please provide the data needed to assess whether this website is a scam. The required data elements are:

[0203] Misspellings in the URL

[0204] Past reputation data

[0205] Grammar error on website"

[0206] As a result, this system determines the risk of fraudulent websites in real time when using the Internet, and provides users with immediate warnings via visual devices, quickly directing them to safe websites and preventing them from becoming victims of fraud.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] (Data Collection): The server collects data from a wide variety of websites on the Internet in real time. It constantly monitors the appearance of new websites and imports them into a database. The input is the URL of the website on the Internet, and the output is the collected website data. Data processing involves analyzing and saving the necessary metadata and HTML code.

[0210] Step 2:

[0211] (Data analysis): The server uses the collected data to analyze the website's spelling mistakes, grammatical errors, and reputation data using an AI model called RiskAssessmentModel. The input is the collected website data, and the output is a risk assessment score based on the analysis results. As a data calculation, the AI ​​model evaluates the collected data and generates a risk score.

[0212] Step 3:

[0213] (Risk Assessment): The server assesses the risk of each website based on the analysis results. It categorizes websites as safe or potentially fraudulent, and updates the information in the database. The input is the risk assessment score, and the output is the updated whitelist and blacklist. Data processing involves categorizing websites as safe or dangerous based on the risk score.

[0214] Step 4:

[0215] (URL Scan): When a user attempts to access a specific URL through a web browser, the device sends the URL to the server in real time. The input is the URL the user attempts to access, and the output is the URL sent to the server. Specifically, the device immediately relays the URL to the server.

[0216] Step 5:

[0217] (Risk assessment): The server scans the transmitted URL in real time and assesses its risk based on blacklists and whitelists. The input is the URL transmitted from the terminal, and the output is the assessment result (risk score). Specifically, the server checks whether it corresponds to the blacklist.

[0218] Step 6:

[0219] (Warning): If the fraud risk is determined to be high, the server issues a warning to the user's visual device (such as smart glasses) via the terminal. The input is the risk assessment result, and the output is a warning message. Specifically, the server transfers the warning message and the URL of a safe official website to the terminal.

[0220] Step 7:

[0221] (User Guidance): The device displays a real-time warning on the user's visual device and provides a link to the official secure website. The input is the warning message from the server and the URL of the official website, and the output is the warning message displayed on the visual device. Specifically, the device displays the warning message and the link to the official website on the visual device's display.

[0222] These steps enable the system to assess the risk of fraudulent sites in real time, warn users through visual devices, and quickly direct them to safe websites.

[0223] 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.

[0224] This system collects and analyzes internet data in real time, warns users when they attempt to access fraudulent websites, and guides them to safe, official websites. It also recognizes users' emotions and adaptively adjusts the warnings and guidance. This system is comprised of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[0225] System Configuration

[0226] 1. Server

[0227] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[0228] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[0229] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[0230] It is equipped with an emotion engine that recognizes the user's emotions. The emotion engine has the ability to analyze data such as the user's facial expressions and voice to determine their emotional state.

[0231] 2. Terminal

[0232] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[0233] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[0234] 3. Users

[0235] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[0236] Emotional state is recognized by the emotion engine, which uses the user's facial and voice data to determine emotions in real time.

[0237] Specific program processing explanation

[0238] After the system is launched, the server first collects whitelists and blacklists, as well as data on various websites from the Internet. This collected data is then analyzed by an AI analysis engine, which evaluates the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites and potentially fraudulent websites are determined, and the blacklist or whitelist is updated accordingly.

[0239] When a user attempts to access a specific website, the device sends the URL to the server. The server then scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and suggests the URL of a safe, official website. Furthermore, the emotion engine analyzes the user's emotional state and adaptively adjusts the warning message based on that emotion. This adjustment allows the user to receive the warning in a more understandable format.

[0240] Specific examples

[0241] For example, consider a case where a user searches for "online bank" and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example," is sent to the server, where the server analyzes the URL using AI. Based on spelling errors (e.g., 'bank' replaced with 'bannk'), grammatical errors, and past reputation data, the AI ​​determines the site to be high risk. As a result, the server immediately issues a warning to the device, displays a message stating, "This site may be fraudulent," and provides a link to the safe, official bank site (e.g., 'official-bank.com'). At the same time, the emotion engine analyzes the user's facial expressions and voice. For example, if the user appears anxious, the server adjusts the warning message to include more detailed and clearer explanations.

[0242] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[0246] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[0247] Step 2:

[0248] The server passes the collected data to an AI analysis engine for initial analysis.

[0249] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[0250] Step 3:

[0251] The server updates the whitelist and blacklist based on the results of the initial analysis.

[0252] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[0253] Step 4:

[0254] The server monitors the Internet for new websites in real time.

[0255] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[0256] Step 5:

[0257] A user attempts to access a particular website.

[0258] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[0259] Step 6:

[0260] The server scans and determines in real time the URLs that users attempt to access.

[0261] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[0262] Step 7:

[0263] If the server determines that the site is fraudulent, it will issue a warning to the user.

[0264] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[0265] Step 8:

[0266] The terminal transmits the user's emotional state to the emotion engine.

[0267] Specific operation: The device uses the built-in camera and microphone to collect the user's facial expressions and voice, and sends that data to the emotion engine.

[0268] Step 9:

[0269] The emotion engine analyzes the user's emotional state.

[0270] Specific behavior: The emotion engine analyzes collected facial and voice data to determine the user's emotional state, such as anxious, confused, or calm.

[0271] Step 10:

[0272] The server adjusts the warning message based on the analysis results from the emotion engine.

[0273] Specific operation: The server adaptively changes the content and display method of the warning message according to the user's emotional state obtained from the emotion engine. For example, if the user is anxious, it displays a more detailed and reassuring message.

[0274] Step 11:

[0275] The server presents the user with a link to the official secure website.

[0276] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[0277] Step 12:

[0278] The user visits the official secure website provided.

[0279] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[0280] In this way, the system detects fraudulent sites in real time through a series of steps, provides adaptive warning messages based on user sentiment, and guides users to safe sites.

[0281] Example 2

[0282] 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."

[0283] Many websites on the Internet are fraudulent or malicious, and accessing them poses a risk to users. This risk is particularly pronounced when users access sites that handle important information. Furthermore, issuing uniform warning messages makes it difficult to encourage users to take appropriate action and fails to take into account the individual emotional state of each user. Therefore, there is a need for a system that can detect fraudulent websites in real time and adaptively adjust warning messages according to the user's emotional state.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0285] In this invention, the server includes means for collecting data on the Internet in real time, AI means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and issuing a warning to the user if the URL is potentially fraudulent, means for directing the user to a safe official website, and emotion recognition means for analyzing the user's emotional state and adjusting the warning message. This allows users to be directed to safe websites in real time and receive appropriate warning messages according to their individual emotional state.

[0286] "Means for collecting data on the Internet in real time" refers to a device or method that constantly monitors websites and evaluation data on the Internet and instantly retrieves the latest information.

[0287] A "spelling error" refers to a word that is not spelled correctly, often due to a typing error or a lack of understanding of the language.

[0288] A "grammatical error" refers to an expression or structure that violates the grammatical rules of the language being used, and often makes it difficult for the reader to understand.

[0289] "Reputation data" refers to past user feedback and evaluation information about a website and its operator, and is used as an indicator of reliability and safety.

[0290] "AI means" refers to algorithms or devices that use artificial intelligence techniques to analyze data and recognize various patterns and features.

[0291] "Website assessment means" refers to a process or device that determines whether a website is safe or potentially fraudulent based on the analyzed data.

[0292] "Real-time URL scanning means" refers to a method or device that instantly inspects the URL of a website a user is attempting to access and assesses its safety.

[0293] "Means for issuing a warning" refers to a method or device that displays a message to a user urging them not to take a particular action.

[0294] "Means for guiding users to a safe official website" refers to a method or device for navigating users to a safe and reliable official website.

[0295] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice data to determine their emotional state at any given time.

[0296] "Database management means" refers to a method or device for efficiently managing and updating data such as whitelists and blacklists.

[0297] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, guides the user to a safe, official website, and recognizes the user's emotions to adaptively adjust the warning and guidance. The system is composed of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[0298] Hardware and software used

[0299] 1. Server

[0300] As a means of collecting data, we use data streaming platforms such as Apache Kafka to collect the latest data from the Internet.

[0301] Deep learning frameworks such as TensorFlow and PyTorch are used as AI analysis tools.

[0302] A database management system (MySQL, PostgreSQL, etc.) is used as the database management method.

[0303] To recognize emotions, the system uses Microsoft Azure's Emotion API and Google Cloud Vision to analyze the user's facial expressions and voice data.

[0304] 2. Terminal

[0305] A browser extension is used to send URLs. The device captures the user's access request and sends it to the server in real time.

[0306] To issue a warning, JavaScript is used to display a warning message to the user.

[0307] Example

[0308] For example, consider the case where a user searches for "online bank" on the web and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example.com," is sent to the server via the terminal. The server analyzes this URL using an AI analysis engine. The analysis evaluates spelling and grammatical errors contained in the URL, as well as past reputation data. Based on this evaluation, the server determines that "fake-bank.example.com" is high risk. After the judgment is made, the server updates the database and adds "fake-bank.example.com" to the blacklist.

[0309] The server then immediately issues a warning to the user via their device. Specifically, it displays the warning message "This site may be fraudulent" along with a link to the safe official bank site, "official-bank.com." At the same time, the emotion engine analyzes the user's facial expressions and voice, and if the user appears anxious, it adjusts the warning message to include a more detailed and easy-to-understand explanation.

[0310] Prompt Sentence Examples

[0311] "When a user attempts to access a fake online banking site, the server analyzes the URL in real time and assesses its likelihood of being a fraudulent site. If it is likely to be fraudulent, the server displays a warning message through the device and provides a link to a safe website. In addition, an emotion engine analyzes the user's emotional state and adaptively adjusts the warning message."

[0312] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] The server uses a data streaming platform such as Apache Kafka to collect evaluation data and newly created website data from the Internet in real time. The input is various data on the Internet, and the output is that the data is imported into the server.

[0316] Step 2:

[0317] The server uses deep learning frameworks such as TensorFlow to analyze the data collected. The input is the collected website data, and the output is a risk assessment based on the website's spelling mistakes, grammatical errors, and past reputation data. Specifically, the analysis engine analyzes the text data of each website and extracts specific features.

[0318] Step 3:

[0319] Based on the analysis results, the server determines which websites are safe and which are potentially fraudulent, and updates the blacklist and whitelist using a MySQL database. The input is the risk assessment result obtained in step 2, and the output is the updated blacklist and whitelist. Specifically, it adds websites that are likely to be fraudulent to the blacklist and adds safe websites to the whitelist.

[0320] Step 4:

[0321] When a user tries to access a website in their browser, their device sends the URL to the server in real time. The input is the URL entered by the user, and the output is the URL sent to the server. Specifically, the browser extension activates, captures the URL, and relays it to the server.

[0322] Step 5:

[0323] The server scans the received URL with an AI analysis engine and performs a risk assessment by referencing blacklists and whitelists. The input is the URL the user is attempting to access, and the output is the risk assessment result for that URL. Specifically, the server compares the URL with a past database to determine whether the URL is likely to be fraudulent.

[0324] Step 6:

[0325] If the server determines that a site is at high risk of fraud, it displays a warning message to the user via the device. The input is the risk assessment result, and the output is the warning message to be displayed to the user. Specifically, it uses JavaScript to display the warning message "This site may be fraudulent" in the browser and provides a link to the official, safe website.

[0326] Step 7:

[0327] The server uses Microsoft Azure's Emotion API to analyze the user's emotional state and adaptively adjust the warning message. The input is the user's facial and voice data, and the output is the adjusted warning message. Specifically, the emotion engine analyzes the user's real-time facial expressions and voice, and if, for example, the user looks anxious, it adjusts the display of a warning message with a detailed explanation.

[0328] Through these steps, the system achieves fast and accurate detection of fraudulent websites and user protection.

[0329] (Application example 2)

[0330] 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."

[0331] Conventional Internet website security systems focus on detecting fraudulent websites, but do not adaptively adjust warning messages to take into account the user's emotional state. This results in problems such as users overlooking or finding the warning messages difficult to understand. Specifically, while the risk of accessing fraudulent websites is high, users are not adequately protected because notifications are not adaptively tailored to the user's emotions. There is also a need for a system that can monitor new fraudulent websites in real time and respond immediately whenever they appear.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0333] In this invention, the server includes means for collecting data on the Internet in real time, artificial intelligence means for analyzing spelling mistakes, grammatical errors, and reputation data of websites based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, and means for recognizing user emotions in real time and adaptively adjusting warning messages according to those emotions. This makes it possible to issue a warning in real time when a user attempts to access a fraudulent website, and by optimizing the warning message to suit the user's emotions, it becomes possible to easily understand and attract the user's attention and guide them to a safe official website.

[0334] "Real time" means processing and responding immediately at that moment.

[0335] "Internet data" refers to information found on websites, social media, news feeds, review sites, etc.

[0336] "Means of collection" refers to devices and programs used to acquire and store data on the Internet.

[0337] A "spelling error" is a misspelling of a word in a sentence.

[0338] A "grammatical error" is a structural or phrasal mistake in a sentence that violates the rules.

[0339] "Reputation Data" refers to user ratings and reviews of websites and services.

[0340] "Artificial intelligence means" refers to algorithms and systems for analyzing data, learning, making predictions, etc.

[0341] A "safe website" is a legitimate site that is free from fraud and malicious intent.

[0342] A "potentially fraudulent website" is a fraudulent site that exists to deceive users.

[0343] "Means for determining" refers to devices or programs that distinguish between safe and fraudulent based on the analysis results.

[0344] "Scanning" means checking data and URLs in real time.

[0345] "Means for issuing a warning" refers to a device or program for displaying a warning message to the user.

[0346] "Means for directing users to official websites" are devices or programs for redirecting users to safe official websites.

[0347] "Emotion" is a psychological state that is inferred based on the user's facial expressions and voice.

[0348] The "means for recognizing and adaptively adjusting the warning message in accordance with the emotion" refers to a device or program for changing the content of the warning based on the user's emotional state.

[0349] This invention is a system that allows users to enjoy a safe browsing experience on the Internet. The system of the present invention is composed of a server that collects and analyzes data on the Internet in real time, a user's terminal, and an emotion engine that recognizes the user's emotions and adjusts warning messages.

[0350] System configuration

[0351] 1. Server:

[0352] Data collection and analysis methods: Real-time website data is collected from the Internet and analyzed using artificial intelligence to identify spelling mistakes, grammatical errors, reputation data, etc. This analysis determines which websites are safe and which may be fraudulent. The server continuously monitors the collected data and regularly updates the whitelist and blacklist.

[0353] URL scanning method: The system receives the URL that the user is trying to access immediately and scans it against the blacklist and whitelist. If there is a possibility of fraud, a warning message is sent to the user's device.

[0354] Emotion Engine: Analyzes the user's facial and voice data to recognize their emotional state in real time, and adaptively adjusts warning messages based on their emotions.

[0355] 2. Terminal:

[0356] URL transmission and warning display method: The URL entered by the user is sent to the server in real time, and a warning message received from the server is displayed. It also guides the user to a safe official website.

[0357] Camera and microphone: Captures the user's facial expressions and voice and sends them to the server as data for analysis by the emotion engine.

[0358] Hardware and software used:

[0359] Hardware:

[0360] Smartphone: Camera, microphone, network communication function

[0361] software:

[0362] OpenCV: Face detection and image processing

[0363] dlib: Face landmark detection

[0364] Keras: Running an emotion recognition model

[0365] Flask: Building a web server and REST API

[0366] Examples:

[0367] For example, consider the case where a user attempts to access an online banking website. In this system, the URL entered by the user is first sent to the server in real time. The server then uses artificial intelligence to analyze the URL and determine the reliability of the website. If it determines that there is a high possibility of fraud, a warning message is immediately sent to the user's device, displaying a message such as, "This site may be fraudulent. Click here for the official website."

[0368] Furthermore, if the user looks anxious, the emotion engine will analyze that information in real time and display a more detailed and reassuring message, such as, "Don't worry. We'll safely redirect you to the official website."

[0369] Example prompt for a generative AI model:

[0370] "A user is attempting to access a banking website. Use emotion recognition through camera video analysis to assess fraud risk in real time and display an appropriate warning message."

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] When a user attempts to access a website, the device retrieves the entered URL.

[0374] Input: The URL that the user types into the browser.

[0375] Output: The input URL.

[0376] Specific operation: The smartphone browser receives the user's input and passes the URL data to the device's URL sending module.

[0377] Step 2:

[0378] The terminal sends the acquired URL to the server.

[0379] Input: The URL that the user types into the browser.

[0380] Output: The URL sent to the server.

[0381] Specific operation: Using the device's network communication function, the obtained URL is sent to the server as an HTTP request.

[0382] Step 3:

[0383] The server scans incoming URLs in real time and analyzes them by referencing blacklists and whitelists.

[0384] Input: The URL sent to the server.

[0385] Output: Site security assessment result (safe / scam).

[0386] How it works: The server retrieves URL data, checks blacklists and whitelists from a pre-collected database to assess fraud risk, and uses artificial intelligence to analyze spelling mistakes, grammatical errors, and reputation data to determine safety.

[0387] Step 4:

[0388] Based on the analysis results, the server generates a warning message if there is a possibility of fraud and sends it to the user's device.

[0389] Input: Site security assessment results.

[0390] Output: A warning message.

[0391] Specific operation: If the server determines that there is a high possibility of fraud based on the analysis results, it generates a warning message and sends it to the user's device as an HTTP response.

[0392] Step 5:

[0393] The device will display a warning message to the user and provide a link directing them to a secure official website.

[0394] Input: The warning message sent by the server.

[0395] Output: A warning message to be displayed to the user along with a link to the official website.

[0396] Specific operation: The device will display the received warning message as a pop-up or in-browser warning screen, and at the same time provide a link to the official website.

[0397] Step 6:

[0398] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to the emotion engine.

[0399] Input: User facial and voice data.

[0400] Output: Facial expression and speech data sent to the emotion engine.

[0401] Specific operation: The device starts input from the camera and microphone, captures the user's facial expressions and voice data, and sends it to the server.

[0402] Step 7:

[0403] The server uses an emotion engine to analyze the user's emotions and adaptively adjusts warning messages based on the results.

[0404] Input: Captured facial and speech data.

[0405] Output: Adjusted warning message.

[0406] How it works: The emotion engine analyzes the received facial and voice data to determine the user's emotional state, and generates adaptive warning messages based on the results.

[0407] Step 8:

[0408] The device will then re-display the adjusted warning message to the user.

[0409] Input: The adjusted warning message.

[0410] Output: An adaptive warning message that is displayed to the user.

[0411] Specific operation: The device updates the warning message again and displays an adaptively adjusted warning message to the user based on the results of sentiment analysis.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Second embodiment]

[0416] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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."

[0428] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0429] System Configuration

[0430] 1. Server

[0431] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[0432] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[0433] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[0434] 2. Terminal

[0435] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[0436] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[0437] 3. Users

[0438] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[0439] Specific program processing explanation

[0440] After the system is launched, the server first collects whitelists and blacklists from the Internet, as well as data on a wide variety of websites. This collected data is then analyzed by AI to evaluate the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites are determined, as are potentially fraudulent websites, and the blacklist or whitelist is updated accordingly.

[0441] When a user attempts to access a specific website, the device sends the URL to the server. The server scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and provides the URL of a safe, official website. The user can then confirm the warning and be directed to the safe website, thereby protecting them from fraud.

[0442] Specific examples

[0443] For example, consider a case where a user searches for "online bank" on the web and attempts to access a fake bank site. When the user enters the URL "fake-bank.example" and sends it to the server, the server analyzes the URL using AI. Based on spelling mistakes in the URL (e.g., 'bank' becomes 'bannk'), grammatical errors, and past reputation data, the AI ​​determines that the site is high risk. As a result, the server immediately issues a warning to the device, displays a message saying "This site may be fraudulent," and provides a link to a safe, official bank site (e.g., 'official-bank.com'). The user confirms this and proceeds to the official website to avoid becoming a victim of fraud.

[0444] Thus, the system of the present invention has specific means and actions to detect fraudulent sites in real time and protect users.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[0448] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[0449] Step 2:

[0450] The server passes the collected data to an AI analysis engine for initial analysis.

[0451] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[0452] Step 3:

[0453] The server updates the whitelist and blacklist based on the results of the initial analysis.

[0454] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[0455] Step 4:

[0456] The server monitors the Internet for new websites in real time.

[0457] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[0458] Step 5:

[0459] A user attempts to access a particular website.

[0460] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[0461] Step 6:

[0462] The server scans and determines in real time the URLs that users attempt to access.

[0463] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[0464] Step 7:

[0465] If the server determines that the site is fraudulent, it will issue a warning to the user.

[0466] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[0467] Step 8:

[0468] Provide users with a link to a secure official website.

[0469] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[0470] Step 9:

[0471] The user visits the official secure website provided.

[0472] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[0473] In this way, the system goes through a series of steps to detect fraudulent sites in real time and direct users to safe sites.

[0474] Example 1

[0475] 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."

[0476] In recent years, the increase in fraudulent and phishing sites on the Internet has made it difficult for users to browse the web safely. To prevent users from accessing these fraudulent sites, real-time reliability assessment and immediate warnings are necessary. However, conventional methods often suffer from delays in data analysis and updates, making it difficult to provide immediate protection to users. The present invention aims to solve these problems and enable users to use the Internet safely.

[0477] 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.

[0478] In this invention, the server includes means for collecting the latest data from the Internet in real time, means for analyzing websites for spelling mistakes, grammatical errors, and past reputation data using an AI model based on the collected data, means for evaluating the safety of websites based on the analysis results and updating blacklists and whitelists in real time, means for scanning URLs that users attempt to access in real time, warning users if there is a possibility of fraud and directing them to a safer official website, and means for transmitting URLs entered by users from their terminals to the server in real time, thereby enabling users to check the safety of websites in real time, prevent access to fraudulent sites, and be quickly directed to safe official websites.

[0479] "The latest data on the Internet" refers to the latest information, such as newly published websites on the Internet, updated information on existing websites, and related evaluation data.

[0480] "Real-time collection means" refers to technologies and methods for obtaining data on the Internet instantly and without delay.

[0481] A "spelling error" refers to an error that involves different characters that are not spelled correctly.

[0482] A "grammatical error" refers to a situation in which the grammatical rules or sentence structure in a sentence are incorrect.

[0483] "Historical Reputation Data" refers to previous user reviews, ratings, feedback, and credibility information about a website.

[0484] An "AI model" refers to an algorithm or system that uses machine learning, deep learning, etc. to analyze data and make predictions, classifications, and evaluations.

[0485] "Means of analysis" refers to techniques and methods for examining collected data and evaluating and classifying it based on certain criteria.

[0486] A "blacklist" refers to a list of websites that are rated as unsafe and potentially fraudulent.

[0487] A "whitelist" refers to a list of websites that are rated as highly secure and trustworthy.

[0488] "Real-time update methods" refers to the technology and methods used to instantly update information in databases and lists and keep them up to date.

[0489] "URL is an abbreviation for Uniform Resource Locator, and refers to the address used to indicate resources on the web (pages, images, videos, etc.).

[0490] "Scanning methods" refers to technologies and methods used to investigate and analyze the content of specific URLs or websites and assess their reliability and safety.

[0491] "Warning measures" refers to techniques or methods used to inform users when certain actions involve risks.

[0492] "Means of directing users to official websites" refers to techniques and methods that provide appropriate links and information to direct users to trusted and safe sites.

[0493] "Terminal" refers to a device that a user uses to access the Internet, and specifically includes a PC, smartphone, tablet, etc.

[0494] This invention is a system that collects, analyzes, and evaluates data on the Internet in real time. It aims to issue a warning when a user attempts to access a fraudulent website and guide the user to a safe, official website. A specific embodiment of this system is described below.

[0495] Server Roles

[0496] 1. Data collection

[0497] The server collects the latest data from the Internet using web scraping tools (e.g., BeautifulSoup, Scrapy) and Web APIs (e.g., Google Safe Browsing API).

[0498] What it does: It uses BeautifulSoup to get the HTML data of a specific webpage, extracts the URL of the new website and the existing reputation data, and calls the Google Safe Browsing API to get a list of dangerous websites.

[0499] 2. Data Analysis

[0500] The collected data is analyzed using natural language processing tools (e.g., spaCy, NLTK) and machine learning models (e.g., TensorFlow, PyTorch).

[0501] What it does: It uses spaCy to tokenize the text content of websites and detect spelling and grammatical errors, and then uses a TensorFlow model to calculate a risk score from the analysis results and assess their trustworthiness.

[0502] 3. Blacklist and Whitelist Management

[0503] Based on the analysis results, the server rates websites and updates the blacklist and whitelist in real time.

[0504] What it does: Accesses a PostgreSQL database and records the evaluation results. It adds websites that are likely to be fraudulent to a blacklist and websites that are deemed safe to a whitelist.

[0505] Device Role

[0506] 1. Sending the URL

[0507] The URL the user attempts to access is sent from the device to the server in real time.

[0508] Specific behavior: When a user enters a URL into a web browser, the device generates an HTTP request containing that information and sends it to the server.

[0509] User Roles

[0510] 1. Web Browsing

[0511] When a user browses the internet using a browser, the URLs they try to access are sent via their device to a server where they are scanned and analyzed.

[0512] Specific operation: When the user enters the URL they want to access and presses the Enter key, the URL is immediately sent from the device to the server.

[0513] Specific examples

[0514] For example, consider a user searching for "online banking" and attempting to access a fake banking site.

[0515] When a user enters the URL "fake-bank.example", this information is sent from the device to the server.

[0516] The server analyzes the URL in real time using AI, for example by looking for misspellings (such as "bannk") and past reputation data to determine whether it is high risk.

[0517] If the server determines that the URL is dangerous, it will send a warning message to the device saying, "This site may be fraudulent," and provide a link to the safe official website ("official-bank.com").

[0518] Users can avoid becoming a victim of fraud by checking the warning and going to the official website.

[0519] Prompt Sentence Examples

[0520] "When a user searches for an online bank and tries to access a fraudulent site like 'fake-bank.example', explain how this system will detect the site as fraudulent and issue a warning."

[0521] This invention collects data on the Internet in real time and analyzes it instantly to prevent users from accessing fraudulent sites and provide a safer web experience.

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] The server collects the latest data from the Internet.

[0525] Specific operations and inputs / outputs:

[0526] Input: List of web page URLs, API endpoints.

[0527] Processing: Use BeautifulSoup to get the HTML data of the specified web page, and use Scrapy to extract the necessary information (e.g., URLs and reputation data of new websites). Also, obtain dangerous site information from the Google Safe Browsing API.

[0528] Output: Extracted website information and dangerous site list.

[0529] Step 2:

[0530] The server analyzes the collected data.

[0531] Specific operations and inputs / outputs:

[0532] Input: HTML data, website URL list, dangerous site information.

[0533] Processing: Uses spaCy to tokenize text content and detect spelling and grammar errors. Uses a TensorFlow model to calculate a website reputation score.

[0534] Output: Analysis results (spelling mistakes, grammar errors, evaluation score).

[0535] Step 3:

[0536] The server manages the blacklist and whitelist.

[0537] Specific operations and inputs / outputs:

[0538] Input: Analysis results (rating score), website URL list.

[0539] Action: Access the PostgreSQL database and update the blacklist and whitelist based on the evaluation results. Add high-risk sites to the blacklist and safe sites to the whitelist.

[0540] Output: Updated blacklists and whitelists.

[0541] Step 4:

[0542] The device sends the URL entered by the user to the server.

[0543] Specific operations and inputs / outputs:

[0544] Input: The URL entered by the user.

[0545] Processing: After the user enters a URL into the browser, the device generates an HTTP request and sends the URL information to the server.

[0546] Output: Data sent to the server (URL).

[0547] Step 5:

[0548] The server scans and evaluates the URLs it receives.

[0549] Specific operations and inputs / outputs:

[0550] Input: URL sent from device, blacklist, whitelist.

[0551] Processing: The server searches the received URL in the form of a database query to see if it is included in the blacklist or whitelist. If it is not included, a new real-time analysis is performed using AI.

[0552] Output: URL evaluation result (safe, dangerous, etc.).

[0553] Step 6:

[0554] The device will display a warning to the user.

[0555] Specific operations and inputs / outputs:

[0556] Input: URL evaluation results received from the server.

[0557] Action: For URLs that are determined to be high risk, the device will generate a browser pop-up with the message "This site may be fraudulent" and provide a link to the official, safe site.

[0558] Output: A warning message and a safe link to display to the user.

[0559] (Application example 1)

[0560] 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."

[0561] When using the Internet, the risk of users accessing fraudulent websites is increasing. However, current security systems lack the ability to issue real-time warnings the moment a user attempts to access a fraudulent website and quickly guide the user to a safe site. As a result, cases of users accessing fraudulent websites continue to increase. Furthermore, the lack of consistency in warnings, especially for users using visual devices, makes it difficult to immediately prevent access to fraudulent websites.

[0562] 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.

[0563] In this invention, the server includes means for collecting data on the Internet in real time, means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and warning the user if the URL is potentially fraudulent, means for directing the user to a safe, official website, and means for displaying a warning on the user's visual device in real time. This allows the user to be informed of the risk of encountering a fraudulent website in real time while using the Internet, and to be promptly directed to a safe website by receiving a warning via the visual device, thereby preventing fraud.

[0564] "Means for collecting data on the Internet" refers to devices or software that have the ability to obtain data from a wide variety of websites on the Internet in real time.

[0565] "AI methods for analyzing website spelling mistakes, grammatical errors, and reputation data" refers to artificial intelligence technology that analyzes the content of websites based on collected data and uses specific algorithms to evaluate spelling mistakes, grammatical errors, and past reputation information.

[0566] "Means for determining whether a website is safe or potentially fraudulent based on the results of analysis" refers to devices or software that have the ability to evaluate and classify whether a specific website is safe or potentially fraudulent based on the results of AI analysis.

[0567] "Means for scanning URLs that users attempt to access in real time and issuing a warning to users if there is a possibility of fraud" refers to devices or software that instantly analyzes the URL entered by the user and displays a warning to the user if the URL is determined to pose a risk of fraud.

[0568] "Means for redirecting users to a safe official website" refers to devices or software that have the function of redirecting users who attempt to access a potentially fraudulent website to a pre-defined safe official website.

[0569] "Means for displaying a real-time warning to a user's visual device" refers to devices or software that have the ability to instantly display a warning to a user about accessing a fraudulent website on a visual device such as smart glasses or a head-mounted display.

[0570] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0571] server

[0572] The server is responsible for the following functions:

[0573] 1. Data Collection:

[0574] The server collects data from a wide variety of websites on the Internet in real time. It also continuously monitors new websites and incorporates this data into the system. This ensures that the whitelist and blacklist databases are always kept up to date.

[0575] 2. Data Analysis:

[0576] The server uses AI to analyze the collected data, including spelling mistakes, grammatical errors, and reputation data of websites. The AI ​​model used is RiskAssessmentModel. The server also analyzes past evaluation data to assess the risk of each website.

[0577] 3. Judgment and Updates:

[0578] Based on the analysis results, it determines which websites are safe and which may be fraudulent. The results are immediately reflected in the database, and blacklists and whitelists are updated.

[0579] Terminal

[0580] The terminal is responsible for the following functions:

[0581] 1. Real-time scanning:

[0582] The URL that the user is trying to access is sent to the server in real time, and the device immediately relays the user's access request to the server and receives the scan results.

[0583] 2. Warnings and Guidance:

[0584] If a potential scam is detected, the device will immediately display a warning to the user and provide a URL to a safe, official website, which will be displayed via a visual device (e.g., smart glasses).

[0585] user

[0586] Users use a web browser to browse the Internet. When a user attempts to access a particular website, the URL is sent via the device to a server where it is scanned and analyzed.

[0587] Specific processing and implementation method

[0588] The server collects whitelists, blacklists, and website data from the internet in real time. This data is analyzed by an AI model (RiskAssessmentModel), which evaluates spelling mistakes, grammatical errors, and past reputation data to determine the risk of a fraudulent website.

[0589] When a user tries to access "fake-bank.example" using smart glasses, the URL is sent to the server. If the server analyzes the URL and determines it to be high-risk, it immediately displays a warning on the smart glasses and provides a link to the official, secure website ("official-bank.com").

[0590] For example, a sample prompt might look like this:

[0591] "The website I'm trying to access has the URL "fake-bank.example". Please provide the data needed to assess whether this website is a scam. The required data elements are:

[0592] Misspellings in the URL

[0593] Past reputation data

[0594] Grammar error on website"

[0595] As a result, this system determines the risk of fraudulent websites in real time when using the Internet, and provides users with immediate warnings via visual devices, quickly directing them to safe websites and preventing them from becoming victims of fraud.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] (Data Collection): The server collects data from a wide variety of websites on the Internet in real time. It constantly monitors the appearance of new websites and imports them into a database. The input is the URL of the website on the Internet, and the output is the collected website data. Data processing involves analyzing and saving the necessary metadata and HTML code.

[0599] Step 2:

[0600] (Data analysis): The server uses the collected data to analyze the website's spelling mistakes, grammatical errors, and reputation data using an AI model called RiskAssessmentModel. The input is the collected website data, and the output is a risk assessment score based on the analysis results. As a data calculation, the AI ​​model evaluates the collected data and generates a risk score.

[0601] Step 3:

[0602] (Risk Assessment): The server assesses the risk of each website based on the analysis results. It categorizes websites as safe or potentially fraudulent, and updates the information in the database. The input is the risk assessment score, and the output is the updated whitelist and blacklist. Data processing involves categorizing websites as safe or dangerous based on the risk score.

[0603] Step 4:

[0604] (URL Scan): When a user attempts to access a specific URL through a web browser, the device sends the URL to the server in real time. The input is the URL the user attempts to access, and the output is the URL sent to the server. Specifically, the device immediately relays the URL to the server.

[0605] Step 5:

[0606] (Risk assessment): The server scans the transmitted URL in real time and assesses its risk based on blacklists and whitelists. The input is the URL transmitted from the terminal, and the output is the assessment result (risk score). Specifically, the server checks whether it corresponds to the blacklist.

[0607] Step 6:

[0608] (Warning): If the fraud risk is determined to be high, the server issues a warning to the user's visual device (such as smart glasses) via the terminal. The input is the risk assessment result, and the output is a warning message. Specifically, the server transfers the warning message and the URL of a safe official website to the terminal.

[0609] Step 7:

[0610] (User Guidance): The device displays a real-time warning on the user's visual device and provides a link to the official secure website. The input is the warning message from the server and the URL of the official website, and the output is the warning message displayed on the visual device. Specifically, the device displays the warning message and the link to the official website on the visual device's display.

[0611] These steps enable the system to assess the risk of fraudulent sites in real time, warn users through visual devices, and quickly direct them to safe websites.

[0612] 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.

[0613] This system collects and analyzes internet data in real time, warns users when they attempt to access fraudulent websites, and guides them to safe, official websites. It also recognizes users' emotions and adaptively adjusts the warnings and guidance. This system is comprised of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[0614] System Configuration

[0615] 1. Server

[0616] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[0617] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[0618] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[0619] It is equipped with an emotion engine that recognizes the user's emotions. The emotion engine has the ability to analyze data such as the user's facial expressions and voice to determine their emotional state.

[0620] 2. Terminal

[0621] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[0622] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[0623] 3. Users

[0624] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[0625] Emotional state is recognized by the emotion engine, which uses the user's facial and voice data to determine emotions in real time.

[0626] Specific program processing explanation

[0627] After the system is launched, the server first collects whitelists and blacklists, as well as data on various websites from the Internet. This collected data is then analyzed by an AI analysis engine, which evaluates the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites and potentially fraudulent websites are determined, and the blacklist or whitelist is updated accordingly.

[0628] When a user attempts to access a specific website, the device sends the URL to the server. The server then scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and suggests the URL of a safe, official website. Furthermore, the emotion engine analyzes the user's emotional state and adaptively adjusts the warning message based on that emotion. This adjustment allows the user to receive the warning in a more understandable format.

[0629] Specific examples

[0630] For example, consider a case where a user searches for "online bank" and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example," is sent to the server, where the server analyzes the URL using AI. Based on spelling errors (e.g., 'bank' replaced with 'bannk'), grammatical errors, and past reputation data, the AI ​​determines the site to be high risk. As a result, the server immediately issues a warning to the device, displays a message stating, "This site may be fraudulent," and provides a link to the safe, official bank site (e.g., 'official-bank.com'). At the same time, the emotion engine analyzes the user's facial expressions and voice. For example, if the user appears anxious, the server adjusts the warning message to include more detailed and clearer explanations.

[0631] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[0635] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[0636] Step 2:

[0637] The server passes the collected data to an AI analysis engine for initial analysis.

[0638] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[0639] Step 3:

[0640] The server updates the whitelist and blacklist based on the results of the initial analysis.

[0641] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[0642] Step 4:

[0643] The server monitors the Internet for new websites in real time.

[0644] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[0645] Step 5:

[0646] A user attempts to access a particular website.

[0647] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[0648] Step 6:

[0649] The server scans and determines in real time the URLs that users attempt to access.

[0650] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[0651] Step 7:

[0652] If the server determines that the site is fraudulent, it will issue a warning to the user.

[0653] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[0654] Step 8:

[0655] The terminal transmits the user's emotional state to the emotion engine.

[0656] Specific operation: The device uses the built-in camera and microphone to collect the user's facial expressions and voice, and sends that data to the emotion engine.

[0657] Step 9:

[0658] The emotion engine analyzes the user's emotional state.

[0659] Specific behavior: The emotion engine analyzes collected facial and voice data to determine the user's emotional state, such as anxious, confused, or calm.

[0660] Step 10:

[0661] The server adjusts the warning message based on the analysis results from the emotion engine.

[0662] Specific operation: The server adaptively changes the content and display method of the warning message according to the user's emotional state obtained from the emotion engine. For example, if the user is anxious, it displays a more detailed and reassuring message.

[0663] Step 11:

[0664] The server presents the user with a link to the official secure website.

[0665] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[0666] Step 12:

[0667] The user visits the official secure website provided.

[0668] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[0669] In this way, the system detects fraudulent sites in real time through a series of steps, provides adaptive warning messages based on user sentiment, and guides users to safe sites.

[0670] Example 2

[0671] 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."

[0672] Many websites on the Internet are fraudulent or malicious, and accessing them poses a risk to users. This risk is particularly pronounced when users access sites that handle important information. Furthermore, issuing uniform warning messages makes it difficult to encourage users to take appropriate action and fails to take into account the individual emotional state of each user. Therefore, there is a need for a system that can detect fraudulent websites in real time and adaptively adjust warning messages according to the user's emotional state.

[0673] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0674] In this invention, the server includes means for collecting data on the Internet in real time, AI means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and issuing a warning to the user if the URL is potentially fraudulent, means for directing the user to a safe official website, and emotion recognition means for analyzing the user's emotional state and adjusting the warning message. This allows users to be directed to safe websites in real time and receive appropriate warning messages according to their individual emotional state.

[0675] "Means for collecting data on the Internet in real time" refers to a device or method that constantly monitors websites and evaluation data on the Internet and instantly retrieves the latest information.

[0676] A "spelling error" refers to a word that is not spelled correctly, often due to a typing error or a lack of understanding of the language.

[0677] A "grammatical error" refers to an expression or structure that violates the grammatical rules of the language being used, and often makes it difficult for the reader to understand.

[0678] "Reputation data" refers to past user feedback and evaluation information about a website and its operator, and is used as an indicator of reliability and safety.

[0679] "AI means" refers to algorithms or devices that use artificial intelligence techniques to analyze data and recognize various patterns and features.

[0680] "Website assessment means" refers to a process or device that determines whether a website is safe or potentially fraudulent based on the analyzed data.

[0681] "Real-time URL scanning means" refers to a method or device that instantly inspects the URL of a website a user is attempting to access and assesses its safety.

[0682] "Means for issuing a warning" refers to a method or device that displays a message to a user urging them not to take a particular action.

[0683] "Means for guiding users to a safe official website" refers to a method or device for navigating users to a safe and reliable official website.

[0684] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice data to determine their emotional state at any given time.

[0685] "Database management means" refers to a method or device for efficiently managing and updating data such as whitelists and blacklists.

[0686] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, guides the user to a safe, official website, and recognizes the user's emotions to adaptively adjust the warning and guidance. The system is composed of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[0687] Hardware and software used

[0688] 1. Server

[0689] As a means of collecting data, we use data streaming platforms such as Apache Kafka to collect the latest data from the Internet.

[0690] Deep learning frameworks such as TensorFlow and PyTorch are used as AI analysis tools.

[0691] A database management system (MySQL, PostgreSQL, etc.) is used as the database management method.

[0692] To recognize emotions, the system uses Microsoft Azure's Emotion API and Google Cloud Vision to analyze the user's facial expressions and voice data.

[0693] 2. Terminal

[0694] A browser extension is used to send URLs. The device captures the user's access request and sends it to the server in real time.

[0695] To issue a warning, JavaScript is used to display a warning message to the user.

[0696] Example

[0697] For example, consider the case where a user searches for "online bank" on the web and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example.com," is sent to the server via the terminal. The server analyzes this URL using an AI analysis engine. The analysis evaluates spelling and grammatical errors contained in the URL, as well as past reputation data. Based on this evaluation, the server determines that "fake-bank.example.com" is high risk. After the judgment is made, the server updates the database and adds "fake-bank.example.com" to the blacklist.

[0698] The server then immediately issues a warning to the user via their device. Specifically, it displays the warning message "This site may be fraudulent" along with a link to the safe official bank site, "official-bank.com." At the same time, the emotion engine analyzes the user's facial expressions and voice, and if the user appears anxious, it adjusts the warning message to include a more detailed and easy-to-understand explanation.

[0699] Prompt Sentence Examples

[0700] "When a user attempts to access a fake online banking site, the server analyzes the URL in real time and assesses its likelihood of being a fraudulent site. If it is likely to be fraudulent, the server displays a warning message through the device and provides a link to a safe website. In addition, an emotion engine analyzes the user's emotional state and adaptively adjusts the warning message."

[0701] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[0702] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0703] Step 1:

[0704] The server uses a data streaming platform such as Apache Kafka to collect evaluation data and newly created website data from the Internet in real time. The input is various data on the Internet, and the output is that the data is imported into the server.

[0705] Step 2:

[0706] The server uses deep learning frameworks such as TensorFlow to analyze the data collected. The input is the collected website data, and the output is a risk assessment based on the website's spelling mistakes, grammatical errors, and past reputation data. Specifically, the analysis engine analyzes the text data of each website and extracts specific features.

[0707] Step 3:

[0708] Based on the analysis results, the server determines which websites are safe and which are potentially fraudulent, and updates the blacklist and whitelist using a MySQL database. The input is the risk assessment result obtained in step 2, and the output is the updated blacklist and whitelist. Specifically, it adds websites that are likely to be fraudulent to the blacklist and adds safe websites to the whitelist.

[0709] Step 4:

[0710] When a user tries to access a website in their browser, their device sends the URL to the server in real time. The input is the URL entered by the user, and the output is the URL sent to the server. Specifically, the browser extension activates, captures the URL, and relays it to the server.

[0711] Step 5:

[0712] The server scans the received URL with an AI analysis engine and performs a risk assessment by referencing blacklists and whitelists. The input is the URL the user is attempting to access, and the output is the risk assessment result for that URL. Specifically, the server compares the URL with a past database to determine whether the URL is likely to be fraudulent.

[0713] Step 6:

[0714] If the server determines that a site is at high risk of fraud, it displays a warning message to the user via the device. The input is the risk assessment result, and the output is the warning message to be displayed to the user. Specifically, it uses JavaScript to display the warning message "This site may be fraudulent" in the browser and provides a link to the official, safe website.

[0715] Step 7:

[0716] The server uses Microsoft Azure's Emotion API to analyze the user's emotional state and adaptively adjust the warning message. The input is the user's facial and voice data, and the output is the adjusted warning message. Specifically, the emotion engine analyzes the user's real-time facial expressions and voice, and if, for example, the user looks anxious, it adjusts the display of a warning message with a detailed explanation.

[0717] Through these steps, the system achieves fast and accurate detection of fraudulent websites and user protection.

[0718] (Application example 2)

[0719] 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."

[0720] Conventional Internet website security systems focus on detecting fraudulent websites, but do not adaptively adjust warning messages to take into account the user's emotional state. This results in problems such as users overlooking or finding the warning messages difficult to understand. Specifically, while the risk of accessing fraudulent websites is high, users are not adequately protected because notifications are not adaptively tailored to the user's emotions. There is also a need for a system that can monitor new fraudulent websites in real time and respond immediately whenever they appear.

[0721] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0722] In this invention, the server includes means for collecting data on the Internet in real time, artificial intelligence means for analyzing spelling mistakes, grammatical errors, and reputation data of websites based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, and means for recognizing user emotions in real time and adaptively adjusting warning messages according to those emotions. This makes it possible to issue a warning in real time when a user attempts to access a fraudulent website, and by optimizing the warning message to suit the user's emotions, it becomes possible to easily understand and attract the user's attention and guide them to a safe official website.

[0723] "Real time" means processing and responding immediately at that moment.

[0724] "Internet data" refers to information found on websites, social media, news feeds, review sites, etc.

[0725] "Means of collection" refers to devices and programs used to acquire and store data on the Internet.

[0726] A "spelling error" is a misspelling of a word in a sentence.

[0727] A "grammatical error" is a structural or phrasal mistake in a sentence that violates the rules.

[0728] "Reputation Data" refers to user ratings and reviews of websites and services.

[0729] "Artificial intelligence means" refers to algorithms and systems for analyzing data, learning, making predictions, etc.

[0730] A "safe website" is a legitimate site that is free from fraud and malicious intent.

[0731] A "potentially fraudulent website" is a fraudulent site that exists to deceive users.

[0732] "Means for determining" refers to devices or programs that distinguish between safe and fraudulent based on the analysis results.

[0733] "Scanning" means checking data and URLs in real time.

[0734] "Means for issuing a warning" refers to a device or program for displaying a warning message to the user.

[0735] "Means for directing users to official websites" are devices or programs for redirecting users to safe official websites.

[0736] "Emotion" is a psychological state that is inferred based on the user's facial expressions and voice.

[0737] The "means for recognizing and adaptively adjusting the warning message in accordance with the emotion" refers to a device or program for changing the content of the warning based on the user's emotional state.

[0738] This invention is a system that allows users to enjoy a safe browsing experience on the Internet. The system of the present invention is composed of a server that collects and analyzes data on the Internet in real time, a user's terminal, and an emotion engine that recognizes the user's emotions and adjusts warning messages.

[0739] System configuration

[0740] 1. Server:

[0741] Data collection and analysis methods: Real-time website data is collected from the Internet and analyzed using artificial intelligence to identify spelling mistakes, grammatical errors, reputation data, etc. This analysis determines which websites are safe and which may be fraudulent. The server continuously monitors the collected data and regularly updates the whitelist and blacklist.

[0742] URL scanning method: The system receives the URL that the user is trying to access immediately and scans it against the blacklist and whitelist. If there is a possibility of fraud, a warning message is sent to the user's device.

[0743] Emotion Engine: Analyzes the user's facial and voice data to recognize their emotional state in real time, and adaptively adjusts warning messages based on their emotions.

[0744] 2. Terminal:

[0745] URL transmission and warning display method: The URL entered by the user is sent to the server in real time, and a warning message received from the server is displayed. It also guides the user to a safe official website.

[0746] Camera and microphone: Captures the user's facial expressions and voice and sends them to the server as data for analysis by the emotion engine.

[0747] Hardware and software used:

[0748] Hardware:

[0749] Smartphone: Camera, microphone, network communication function

[0750] software:

[0751] OpenCV: Face detection and image processing

[0752] dlib: Face landmark detection

[0753] Keras: Running an emotion recognition model

[0754] Flask: Building a web server and REST API

[0755] Examples:

[0756] For example, consider the case where a user attempts to access an online banking website. In this system, the URL entered by the user is first sent to the server in real time. The server then uses artificial intelligence to analyze the URL and determine the reliability of the website. If it determines that there is a high possibility of fraud, a warning message is immediately sent to the user's device, displaying a message such as, "This site may be fraudulent. Click here for the official website."

[0757] Furthermore, if the user looks anxious, the emotion engine will analyze that information in real time and display a more detailed and reassuring message, such as, "Don't worry. We'll safely redirect you to the official website."

[0758] Example prompt for a generative AI model:

[0759] "A user is attempting to access a banking website. Use emotion recognition through camera video analysis to assess fraud risk in real time and display an appropriate warning message."

[0760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0761] Step 1:

[0762] When a user attempts to access a website, the device retrieves the entered URL.

[0763] Input: The URL that the user types into the browser.

[0764] Output: The input URL.

[0765] Specific operation: The smartphone browser receives the user's input and passes the URL data to the device's URL sending module.

[0766] Step 2:

[0767] The terminal sends the acquired URL to the server.

[0768] Input: The URL that the user types into the browser.

[0769] Output: The URL sent to the server.

[0770] Specific operation: Using the device's network communication function, the obtained URL is sent to the server as an HTTP request.

[0771] Step 3:

[0772] The server scans incoming URLs in real time and analyzes them by referencing blacklists and whitelists.

[0773] Input: The URL sent to the server.

[0774] Output: Site security assessment result (safe / scam).

[0775] How it works: The server retrieves URL data, checks blacklists and whitelists from a pre-collected database to assess fraud risk, and uses artificial intelligence to analyze spelling mistakes, grammatical errors, and reputation data to determine safety.

[0776] Step 4:

[0777] Based on the analysis results, the server generates a warning message if there is a possibility of fraud and sends it to the user's device.

[0778] Input: Site security assessment results.

[0779] Output: A warning message.

[0780] Specific operation: If the server determines that there is a high possibility of fraud based on the analysis results, it generates a warning message and sends it to the user's device as an HTTP response.

[0781] Step 5:

[0782] The device will display a warning message to the user and provide a link directing them to a secure official website.

[0783] Input: The warning message sent by the server.

[0784] Output: A warning message to be displayed to the user along with a link to the official website.

[0785] Specific operation: The device will display the received warning message as a pop-up or in-browser warning screen, and at the same time provide a link to the official website.

[0786] Step 6:

[0787] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to the emotion engine.

[0788] Input: User facial and voice data.

[0789] Output: Facial expression and speech data sent to the emotion engine.

[0790] Specific operation: The device starts input from the camera and microphone, captures the user's facial expressions and voice data, and sends it to the server.

[0791] Step 7:

[0792] The server uses an emotion engine to analyze the user's emotions and adaptively adjusts warning messages based on the results.

[0793] Input: Captured facial and speech data.

[0794] Output: Adjusted warning message.

[0795] How it works: The emotion engine analyzes the received facial and voice data to determine the user's emotional state, and generates adaptive warning messages based on the results.

[0796] Step 8:

[0797] The device will then re-display the adjusted warning message to the user.

[0798] Input: The adjusted warning message.

[0799] Output: An adaptive warning message that is displayed to the user.

[0800] Specific operation: The device updates the warning message again and displays an adaptively adjusted warning message to the user based on the results of sentiment analysis.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] [Third embodiment]

[0805] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0806] 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.

[0807] 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).

[0808] 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.

[0809] 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.

[0810] 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).

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0818] System Configuration

[0819] 1. Server

[0820] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[0821] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[0822] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[0823] 2. Terminal

[0824] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[0825] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[0826] 3. Users

[0827] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[0828] Specific program processing explanation

[0829] After the system is launched, the server first collects whitelists and blacklists from the Internet, as well as data on a wide variety of websites. This collected data is then analyzed by AI to evaluate the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites are determined, as are potentially fraudulent websites, and the blacklist or whitelist is updated accordingly.

[0830] When a user attempts to access a specific website, the device sends the URL to the server. The server scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and provides the URL of a safe, official website. The user can then confirm the warning and be directed to the safe website, thereby protecting them from fraud.

[0831] Specific examples

[0832] For example, consider a case where a user searches for "online bank" on the web and attempts to access a fake bank site. When the user enters the URL "fake-bank.example" and sends it to the server, the server analyzes the URL using AI. Based on spelling mistakes in the URL (e.g., 'bank' becomes 'bannk'), grammatical errors, and past reputation data, the AI ​​determines that the site is high risk. As a result, the server immediately issues a warning to the device, displays a message saying "This site may be fraudulent," and provides a link to a safe, official bank site (e.g., 'official-bank.com'). The user confirms this and proceeds to the official website to avoid becoming a victim of fraud.

[0833] Thus, the system of the present invention has specific means and actions to detect fraudulent sites in real time and protect users.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[0837] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[0838] Step 2:

[0839] The server passes the collected data to an AI analysis engine for initial analysis.

[0840] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[0841] Step 3:

[0842] The server updates the whitelist and blacklist based on the results of the initial analysis.

[0843] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[0844] Step 4:

[0845] The server monitors the Internet for new websites in real time.

[0846] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[0847] Step 5:

[0848] A user attempts to access a particular website.

[0849] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[0850] Step 6:

[0851] The server scans and determines in real time the URLs that users attempt to access.

[0852] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[0853] Step 7:

[0854] If the server determines that the site is fraudulent, it will issue a warning to the user.

[0855] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[0856] Step 8:

[0857] Provide users with a link to a secure official website.

[0858] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[0859] Step 9:

[0860] The user visits the official secure website provided.

[0861] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[0862] In this way, the system goes through a series of steps to detect fraudulent sites in real time and direct users to safe sites.

[0863] Example 1

[0864] 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."

[0865] In recent years, the increase in fraudulent and phishing sites on the Internet has made it difficult for users to browse the web safely. To prevent users from accessing these fraudulent sites, real-time reliability assessment and immediate warnings are necessary. However, conventional methods often suffer from delays in data analysis and updates, making it difficult to provide immediate protection to users. The present invention aims to solve these problems and enable users to use the Internet safely.

[0866] 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.

[0867] In this invention, the server includes means for collecting the latest data from the Internet in real time, means for analyzing websites for spelling mistakes, grammatical errors, and past reputation data using an AI model based on the collected data, means for evaluating the safety of websites based on the analysis results and updating blacklists and whitelists in real time, means for scanning URLs that users attempt to access in real time, warning users if there is a possibility of fraud and directing them to a safer official website, and means for transmitting URLs entered by users from their terminals to the server in real time, thereby enabling users to check the safety of websites in real time, prevent access to fraudulent sites, and be quickly directed to safe official websites.

[0868] "The latest data on the Internet" refers to the latest information, such as newly published websites on the Internet, updated information on existing websites, and related evaluation data.

[0869] "Real-time collection means" refers to technologies and methods for obtaining data on the Internet instantly and without delay.

[0870] A "spelling error" refers to an error that involves different characters that are not spelled correctly.

[0871] A "grammatical error" refers to a situation in which the grammatical rules or sentence structure in a sentence are incorrect.

[0872] "Historical Reputation Data" refers to previous user reviews, ratings, feedback, and credibility information about a website.

[0873] An "AI model" refers to an algorithm or system that uses machine learning, deep learning, etc. to analyze data and make predictions, classifications, and evaluations.

[0874] "Means of analysis" refers to techniques and methods for examining collected data and evaluating and classifying it based on certain criteria.

[0875] A "blacklist" refers to a list of websites that are rated as unsafe and potentially fraudulent.

[0876] A "whitelist" refers to a list of websites that are rated as highly secure and trustworthy.

[0877] "Real-time update methods" refers to the technology and methods used to instantly update information in databases and lists and keep them up to date.

[0878] "URL is an abbreviation for Uniform Resource Locator, and refers to the address used to indicate resources on the web (pages, images, videos, etc.).

[0879] "Scanning methods" refers to technologies and methods used to investigate and analyze the content of specific URLs or websites and assess their reliability and safety.

[0880] "Warning measures" refers to techniques or methods used to inform users when certain actions involve risks.

[0881] "Means of directing users to official websites" refers to techniques and methods that provide appropriate links and information to direct users to trusted and safe sites.

[0882] "Terminal" refers to a device that a user uses to access the Internet, and specifically includes a PC, smartphone, tablet, etc.

[0883] This invention is a system that collects, analyzes, and evaluates data on the Internet in real time. It aims to issue a warning when a user attempts to access a fraudulent website and guide the user to a safe, official website. A specific embodiment of this system is described below.

[0884] Server Roles

[0885] 1. Data collection

[0886] The server collects the latest data from the Internet using web scraping tools (e.g., BeautifulSoup, Scrapy) and Web APIs (e.g., Google Safe Browsing API).

[0887] What it does: It uses BeautifulSoup to get the HTML data of a specific webpage, extracts the URL of the new website and the existing reputation data, and calls the Google Safe Browsing API to get a list of dangerous websites.

[0888] 2. Data Analysis

[0889] The collected data is analyzed using natural language processing tools (e.g., spaCy, NLTK) and machine learning models (e.g., TensorFlow, PyTorch).

[0890] What it does: It uses spaCy to tokenize the text content of websites and detect spelling and grammatical errors, and then uses a TensorFlow model to calculate a risk score from the analysis results and assess their trustworthiness.

[0891] 3. Blacklist and Whitelist Management

[0892] Based on the analysis results, the server rates websites and updates the blacklist and whitelist in real time.

[0893] What it does: Accesses a PostgreSQL database and records the evaluation results. It adds websites that are likely to be fraudulent to a blacklist and websites that are deemed safe to a whitelist.

[0894] Device Role

[0895] 1. Sending the URL

[0896] The URL the user attempts to access is sent from the device to the server in real time.

[0897] Specific behavior: When a user enters a URL into a web browser, the device generates an HTTP request containing that information and sends it to the server.

[0898] User Roles

[0899] 1. Web Browsing

[0900] When a user browses the internet using a browser, the URLs they try to access are sent via their device to a server where they are scanned and analyzed.

[0901] Specific operation: When the user enters the URL they want to access and presses the Enter key, the URL is immediately sent from the device to the server.

[0902] Specific examples

[0903] For example, consider a user searching for "online banking" and attempting to access a fake banking site.

[0904] When a user enters the URL "fake-bank.example", this information is sent from the device to the server.

[0905] The server analyzes the URL in real time using AI, for example by looking for misspellings (such as "bannk") and past reputation data to determine whether it is high risk.

[0906] If the server determines that the URL is dangerous, it will send a warning message to the device saying, "This site may be fraudulent," and provide a link to the safe official website ("official-bank.com").

[0907] Users can avoid becoming a victim of fraud by checking the warning and going to the official website.

[0908] Prompt Sentence Examples

[0909] "When a user searches for an online bank and tries to access a fraudulent site like 'fake-bank.example', explain how this system will detect the site as fraudulent and issue a warning."

[0910] This invention collects data on the Internet in real time and analyzes it instantly to prevent users from accessing fraudulent sites and provide a safer web experience.

[0911] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] The server collects the latest data from the Internet.

[0914] Specific operations and inputs / outputs:

[0915] Input: List of web page URLs, API endpoints.

[0916] Processing: Use BeautifulSoup to get the HTML data of the specified web page, and use Scrapy to extract the necessary information (e.g., URLs and reputation data of new websites). Also, obtain dangerous site information from the Google Safe Browsing API.

[0917] Output: Extracted website information and dangerous site list.

[0918] Step 2:

[0919] The server analyzes the collected data.

[0920] Specific operations and inputs / outputs:

[0921] Input: HTML data, website URL list, dangerous site information.

[0922] Processing: Uses spaCy to tokenize text content and detect spelling and grammar errors. Uses a TensorFlow model to calculate a website reputation score.

[0923] Output: Analysis results (spelling mistakes, grammar errors, evaluation score).

[0924] Step 3:

[0925] The server manages the blacklist and whitelist.

[0926] Specific operations and inputs / outputs:

[0927] Input: Analysis results (rating score), website URL list.

[0928] Action: Access the PostgreSQL database and update the blacklist and whitelist based on the evaluation results. Add high-risk sites to the blacklist and safe sites to the whitelist.

[0929] Output: Updated blacklists and whitelists.

[0930] Step 4:

[0931] The device sends the URL entered by the user to the server.

[0932] Specific operations and inputs / outputs:

[0933] Input: The URL entered by the user.

[0934] Processing: After the user enters a URL into the browser, the device generates an HTTP request and sends the URL information to the server.

[0935] Output: Data sent to the server (URL).

[0936] Step 5:

[0937] The server scans and evaluates the URLs it receives.

[0938] Specific operations and inputs / outputs:

[0939] Input: URL sent from device, blacklist, whitelist.

[0940] Processing: The server searches the received URL in the form of a database query to see if it is included in the blacklist or whitelist. If it is not included, a new real-time analysis is performed using AI.

[0941] Output: URL evaluation result (safe, dangerous, etc.).

[0942] Step 6:

[0943] The device will display a warning to the user.

[0944] Specific operations and inputs / outputs:

[0945] Input: URL evaluation results received from the server.

[0946] Action: For URLs that are determined to be high risk, the device will generate a browser pop-up with the message "This site may be fraudulent" and provide a link to the official, safe site.

[0947] Output: A warning message and a safe link to display to the user.

[0948] (Application example 1)

[0949] 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."

[0950] When using the Internet, the risk of users accessing fraudulent websites is increasing. However, current security systems lack the ability to issue real-time warnings the moment a user attempts to access a fraudulent website and quickly guide the user to a safe site. As a result, cases of users accessing fraudulent websites continue to increase. Furthermore, the lack of consistency in warnings, especially for users using visual devices, makes it difficult to immediately prevent access to fraudulent websites.

[0951] 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.

[0952] In this invention, the server includes means for collecting data on the Internet in real time, means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and warning the user if the URL is potentially fraudulent, means for directing the user to a safe, official website, and means for displaying a warning on the user's visual device in real time. This allows the user to be informed of the risk of encountering a fraudulent website in real time while using the Internet, and to be promptly directed to a safe website by receiving a warning via the visual device, thereby preventing fraud.

[0953] "Means for collecting data on the Internet" refers to devices or software that have the ability to obtain data from a wide variety of websites on the Internet in real time.

[0954] "AI methods for analyzing website spelling mistakes, grammatical errors, and reputation data" refers to artificial intelligence technology that analyzes the content of websites based on collected data and uses specific algorithms to evaluate spelling mistakes, grammatical errors, and past reputation information.

[0955] "Means for determining whether a website is safe or potentially fraudulent based on the results of analysis" refers to devices or software that have the ability to evaluate and classify whether a specific website is safe or potentially fraudulent based on the results of AI analysis.

[0956] "Means for scanning URLs that users attempt to access in real time and issuing a warning to users if there is a possibility of fraud" refers to devices or software that instantly analyzes the URL entered by the user and displays a warning to the user if the URL is determined to pose a risk of fraud.

[0957] "Means for redirecting users to a safe official website" refers to devices or software that have the function of redirecting users who attempt to access a potentially fraudulent website to a pre-defined safe official website.

[0958] "Means for displaying a real-time warning to a user's visual device" refers to devices or software that have the ability to instantly display a warning to a user about accessing a fraudulent website on a visual device such as smart glasses or a head-mounted display.

[0959] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[0960] server

[0961] The server is responsible for the following functions:

[0962] 1. Data Collection:

[0963] The server collects data from a wide variety of websites on the Internet in real time. It also continuously monitors new websites and incorporates this data into the system. This ensures that the whitelist and blacklist databases are always kept up to date.

[0964] 2. Data Analysis:

[0965] The server uses AI to analyze the collected data, including spelling mistakes, grammatical errors, and reputation data of websites. The AI ​​model used is RiskAssessmentModel. The server also analyzes past evaluation data to assess the risk of each website.

[0966] 3. Judgment and Updates:

[0967] Based on the analysis results, it determines which websites are safe and which may be fraudulent. The results are immediately reflected in the database, and blacklists and whitelists are updated.

[0968] Terminal

[0969] The terminal is responsible for the following functions:

[0970] 1. Real-time scanning:

[0971] The URL that the user is trying to access is sent to the server in real time, and the device immediately relays the user's access request to the server and receives the scan results.

[0972] 2. Warnings and Guidance:

[0973] If a potential scam is detected, the device will immediately display a warning to the user and provide a URL to a safe, official website, which will be displayed via a visual device (e.g., smart glasses).

[0974] user

[0975] Users use a web browser to browse the Internet. When a user attempts to access a particular website, the URL is sent via the device to a server where it is scanned and analyzed.

[0976] Specific processing and implementation method

[0977] The server collects whitelists, blacklists, and website data from the internet in real time. This data is analyzed by an AI model (RiskAssessmentModel), which evaluates spelling mistakes, grammatical errors, and past reputation data to determine the risk of a fraudulent website.

[0978] When a user tries to access "fake-bank.example" using smart glasses, the URL is sent to the server. If the server analyzes the URL and determines it to be high-risk, it immediately displays a warning on the smart glasses and provides a link to the official, secure website ("official-bank.com").

[0979] For example, a sample prompt might look like this:

[0980] "The website I'm trying to access has the URL "fake-bank.example". Please provide the data needed to assess whether this website is a scam. The required data elements are:

[0981] Misspellings in the URL

[0982] Past reputation data

[0983] Grammar error on website"

[0984] As a result, this system determines the risk of fraudulent websites in real time when using the Internet, and provides users with immediate warnings via visual devices, quickly directing them to safe websites and preventing them from becoming victims of fraud.

[0985] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0986] Step 1:

[0987] (Data Collection): The server collects data from a wide variety of websites on the Internet in real time. It constantly monitors the appearance of new websites and imports them into a database. The input is the URL of the website on the Internet, and the output is the collected website data. Data processing involves analyzing and saving the necessary metadata and HTML code.

[0988] Step 2:

[0989] (Data analysis): The server uses the collected data to analyze the website's spelling mistakes, grammatical errors, and reputation data using an AI model called RiskAssessmentModel. The input is the collected website data, and the output is a risk assessment score based on the analysis results. As a data calculation, the AI ​​model evaluates the collected data and generates a risk score.

[0990] Step 3:

[0991] (Risk Assessment): The server assesses the risk of each website based on the analysis results. It categorizes websites as safe or potentially fraudulent, and updates the information in the database. The input is the risk assessment score, and the output is the updated whitelist and blacklist. Data processing involves categorizing websites as safe or dangerous based on the risk score.

[0992] Step 4:

[0993] (URL Scan): When a user attempts to access a specific URL through a web browser, the device sends the URL to the server in real time. The input is the URL the user attempts to access, and the output is the URL sent to the server. Specifically, the device immediately relays the URL to the server.

[0994] Step 5:

[0995] (Risk assessment): The server scans the transmitted URL in real time and assesses its risk based on blacklists and whitelists. The input is the URL transmitted from the terminal, and the output is the assessment result (risk score). Specifically, the server checks whether it corresponds to the blacklist.

[0996] Step 6:

[0997] (Warning): If the fraud risk is determined to be high, the server issues a warning to the user's visual device (such as smart glasses) via the terminal. The input is the risk assessment result, and the output is a warning message. Specifically, the server transfers the warning message and the URL of a safe official website to the terminal.

[0998] Step 7:

[0999] (User Guidance): The device displays a real-time warning on the user's visual device and provides a link to the official secure website. The input is the warning message from the server and the URL of the official website, and the output is the warning message displayed on the visual device. Specifically, the device displays the warning message and the link to the official website on the visual device's display.

[1000] These steps enable the system to assess the risk of fraudulent sites in real time, warn users through visual devices, and quickly direct them to safe websites.

[1001] 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.

[1002] This system collects and analyzes internet data in real time, warns users when they attempt to access fraudulent websites, and guides them to safe, official websites. It also recognizes users' emotions and adaptively adjusts the warnings and guidance. This system is comprised of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[1003] System Configuration

[1004] 1. Server

[1005] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[1006] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[1007] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[1008] It is equipped with an emotion engine that recognizes the user's emotions. The emotion engine has the ability to analyze data such as the user's facial expressions and voice to determine their emotional state.

[1009] 2. Terminal

[1010] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[1011] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[1012] 3. Users

[1013] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[1014] Emotional state is recognized by the emotion engine, which uses the user's facial and voice data to determine emotions in real time.

[1015] Specific program processing explanation

[1016] After the system is launched, the server first collects whitelists and blacklists, as well as data on various websites from the Internet. This collected data is then analyzed by an AI analysis engine, which evaluates the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites and potentially fraudulent websites are determined, and the blacklist or whitelist is updated accordingly.

[1017] When a user attempts to access a specific website, the device sends the URL to the server. The server then scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and suggests the URL of a safe, official website. Furthermore, the emotion engine analyzes the user's emotional state and adaptively adjusts the warning message based on that emotion. This adjustment allows the user to receive the warning in a more understandable format.

[1018] Specific examples

[1019] For example, consider a case where a user searches for "online bank" and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example," is sent to the server, where the server analyzes the URL using AI. Based on spelling errors (e.g., 'bank' replaced with 'bannk'), grammatical errors, and past reputation data, the AI ​​determines the site to be high risk. As a result, the server immediately issues a warning to the device, displays a message stating, "This site may be fraudulent," and provides a link to the safe, official bank site (e.g., 'official-bank.com'). At the same time, the emotion engine analyzes the user's facial expressions and voice. For example, if the user appears anxious, the server adjusts the warning message to include more detailed and clearer explanations.

[1020] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[1024] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[1025] Step 2:

[1026] The server passes the collected data to an AI analysis engine for initial analysis.

[1027] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[1028] Step 3:

[1029] The server updates the whitelist and blacklist based on the results of the initial analysis.

[1030] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[1031] Step 4:

[1032] The server monitors the Internet for new websites in real time.

[1033] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[1034] Step 5:

[1035] A user attempts to access a particular website.

[1036] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[1037] Step 6:

[1038] The server scans and determines in real time the URLs that users attempt to access.

[1039] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[1040] Step 7:

[1041] If the server determines that the site is fraudulent, it will issue a warning to the user.

[1042] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[1043] Step 8:

[1044] The terminal transmits the user's emotional state to the emotion engine.

[1045] Specific operation: The device uses the built-in camera and microphone to collect the user's facial expressions and voice, and sends that data to the emotion engine.

[1046] Step 9:

[1047] The emotion engine analyzes the user's emotional state.

[1048] Specific behavior: The emotion engine analyzes collected facial and voice data to determine the user's emotional state, such as anxious, confused, or calm.

[1049] Step 10:

[1050] The server adjusts the warning message based on the analysis results from the emotion engine.

[1051] Specific operation: The server adaptively changes the content and display method of the warning message according to the user's emotional state obtained from the emotion engine. For example, if the user is anxious, it displays a more detailed and reassuring message.

[1052] Step 11:

[1053] The server presents the user with a link to the official secure website.

[1054] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[1055] Step 12:

[1056] The user visits the official secure website provided.

[1057] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[1058] In this way, the system detects fraudulent sites in real time through a series of steps, provides adaptive warning messages based on user sentiment, and guides users to safe sites.

[1059] Example 2

[1060] 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."

[1061] Many websites on the Internet are fraudulent or malicious, and accessing them poses a risk to users. This risk is particularly pronounced when users access sites that handle important information. Furthermore, issuing uniform warning messages makes it difficult to encourage users to take appropriate action and fails to take into account the individual emotional state of each user. Therefore, there is a need for a system that can detect fraudulent websites in real time and adaptively adjust warning messages according to the user's emotional state.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1063] In this invention, the server includes means for collecting data on the Internet in real time, AI means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and issuing a warning to the user if the URL is potentially fraudulent, means for directing the user to a safe official website, and emotion recognition means for analyzing the user's emotional state and adjusting the warning message. This allows users to be directed to safe websites in real time and receive appropriate warning messages according to their individual emotional state.

[1064] "Means for collecting data on the Internet in real time" refers to a device or method that constantly monitors websites and evaluation data on the Internet and instantly retrieves the latest information.

[1065] A "spelling error" refers to a word that is not spelled correctly, often due to a typing error or a lack of understanding of the language.

[1066] A "grammatical error" refers to an expression or structure that violates the grammatical rules of the language being used, and often makes it difficult for the reader to understand.

[1067] "Reputation data" refers to past user feedback and evaluation information about a website and its operator, and is used as an indicator of reliability and safety.

[1068] "AI means" refers to algorithms or devices that use artificial intelligence techniques to analyze data and recognize various patterns and features.

[1069] "Website assessment means" refers to a process or device that determines whether a website is safe or potentially fraudulent based on the analyzed data.

[1070] "Real-time URL scanning means" refers to a method or device that instantly inspects the URL of a website a user is attempting to access and assesses its safety.

[1071] "Means for issuing a warning" refers to a method or device that displays a message to a user urging them not to take a particular action.

[1072] "Means for guiding users to a safe official website" refers to a method or device for navigating users to a safe and reliable official website.

[1073] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice data to determine their emotional state at any given time.

[1074] "Database management means" refers to a method or device for efficiently managing and updating data such as whitelists and blacklists.

[1075] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, guides the user to a safe, official website, and recognizes the user's emotions to adaptively adjust the warning and guidance. The system is composed of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[1076] Hardware and software used

[1077] 1. Server

[1078] As a means of collecting data, we use data streaming platforms such as Apache Kafka to collect the latest data from the Internet.

[1079] Deep learning frameworks such as TensorFlow and PyTorch are used as AI analysis tools.

[1080] A database management system (MySQL, PostgreSQL, etc.) is used as the database management method.

[1081] To recognize emotions, the system uses Microsoft Azure's Emotion API and Google Cloud Vision to analyze the user's facial expressions and voice data.

[1082] 2. Terminal

[1083] A browser extension is used to send URLs. The device captures the user's access request and sends it to the server in real time.

[1084] To issue a warning, JavaScript is used to display a warning message to the user.

[1085] Example

[1086] For example, consider the case where a user searches for "online bank" on the web and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example.com," is sent to the server via the terminal. The server analyzes this URL using an AI analysis engine. The analysis evaluates spelling and grammatical errors contained in the URL, as well as past reputation data. Based on this evaluation, the server determines that "fake-bank.example.com" is high risk. After the judgment is made, the server updates the database and adds "fake-bank.example.com" to the blacklist.

[1087] The server then immediately issues a warning to the user via their device. Specifically, it displays the warning message "This site may be fraudulent" along with a link to the safe official bank site, "official-bank.com." At the same time, the emotion engine analyzes the user's facial expressions and voice, and if the user appears anxious, it adjusts the warning message to include a more detailed and easy-to-understand explanation.

[1088] Prompt Sentence Examples

[1089] "When a user attempts to access a fake online banking site, the server analyzes the URL in real time and assesses its likelihood of being a fraudulent site. If it is likely to be fraudulent, the server displays a warning message through the device and provides a link to a safe website. In addition, an emotion engine analyzes the user's emotional state and adaptively adjusts the warning message."

[1090] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[1091] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1092] Step 1:

[1093] The server uses a data streaming platform such as Apache Kafka to collect evaluation data and newly created website data from the Internet in real time. The input is various data on the Internet, and the output is that the data is imported into the server.

[1094] Step 2:

[1095] The server uses deep learning frameworks such as TensorFlow to analyze the data collected. The input is the collected website data, and the output is a risk assessment based on the website's spelling mistakes, grammatical errors, and past reputation data. Specifically, the analysis engine analyzes the text data of each website and extracts specific features.

[1096] Step 3:

[1097] Based on the analysis results, the server determines which websites are safe and which are potentially fraudulent, and updates the blacklist and whitelist using a MySQL database. The input is the risk assessment result obtained in step 2, and the output is the updated blacklist and whitelist. Specifically, it adds websites that are likely to be fraudulent to the blacklist and adds safe websites to the whitelist.

[1098] Step 4:

[1099] When a user tries to access a website in their browser, their device sends the URL to the server in real time. The input is the URL entered by the user, and the output is the URL sent to the server. Specifically, the browser extension activates, captures the URL, and relays it to the server.

[1100] Step 5:

[1101] The server scans the received URL with an AI analysis engine and performs a risk assessment by referencing blacklists and whitelists. The input is the URL the user is attempting to access, and the output is the risk assessment result for that URL. Specifically, the server compares the URL with a past database to determine whether the URL is likely to be fraudulent.

[1102] Step 6:

[1103] If the server determines that a site is at high risk of fraud, it displays a warning message to the user via the device. The input is the risk assessment result, and the output is the warning message to be displayed to the user. Specifically, it uses JavaScript to display the warning message "This site may be fraudulent" in the browser and provides a link to the official, safe website.

[1104] Step 7:

[1105] The server uses Microsoft Azure's Emotion API to analyze the user's emotional state and adaptively adjust the warning message. The input is the user's facial and voice data, and the output is the adjusted warning message. Specifically, the emotion engine analyzes the user's real-time facial expressions and voice, and if, for example, the user looks anxious, it adjusts the display of a warning message with a detailed explanation.

[1106] Through these steps, the system achieves fast and accurate detection of fraudulent websites and user protection.

[1107] (Application example 2)

[1108] 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."

[1109] Conventional Internet website security systems focus on detecting fraudulent websites, but do not adaptively adjust warning messages to take into account the user's emotional state. This results in problems such as users overlooking or finding the warning messages difficult to understand. Specifically, while the risk of accessing fraudulent websites is high, users are not adequately protected because notifications are not adaptively tailored to the user's emotions. There is also a need for a system that can monitor new fraudulent websites in real time and respond immediately whenever they appear.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1111] In this invention, the server includes means for collecting data on the Internet in real time, artificial intelligence means for analyzing spelling mistakes, grammatical errors, and reputation data of websites based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, and means for recognizing user emotions in real time and adaptively adjusting warning messages according to those emotions. This makes it possible to issue a warning in real time when a user attempts to access a fraudulent website, and by optimizing the warning message to suit the user's emotions, it becomes possible to easily understand and attract the user's attention and guide them to a safe official website.

[1112] "Real time" means processing and responding immediately at that moment.

[1113] "Internet data" refers to information found on websites, social media, news feeds, review sites, etc.

[1114] "Means of collection" refers to devices and programs used to acquire and store data on the Internet.

[1115] A "spelling error" is a misspelling of a word in a sentence.

[1116] A "grammatical error" is a structural or phrasal mistake in a sentence that violates the rules.

[1117] "Reputation Data" refers to user ratings and reviews of websites and services.

[1118] "Artificial intelligence means" refers to algorithms and systems for analyzing data, learning, making predictions, etc.

[1119] A "safe website" is a legitimate site that is free from fraud and malicious intent.

[1120] A "potentially fraudulent website" is a fraudulent site that exists to deceive users.

[1121] "Means for determining" refers to devices or programs that distinguish between safe and fraudulent based on the analysis results.

[1122] "Scanning" means checking data and URLs in real time.

[1123] "Means for issuing a warning" refers to a device or program for displaying a warning message to the user.

[1124] "Means for directing users to official websites" are devices or programs for redirecting users to safe official websites.

[1125] "Emotion" is a psychological state that is inferred based on the user's facial expressions and voice.

[1126] The "means for recognizing and adaptively adjusting the warning message in accordance with the emotion" refers to a device or program for changing the content of the warning based on the user's emotional state.

[1127] This invention is a system that allows users to enjoy a safe browsing experience on the Internet. The system of the present invention is composed of a server that collects and analyzes data on the Internet in real time, a user's terminal, and an emotion engine that recognizes the user's emotions and adjusts warning messages.

[1128] System configuration

[1129] 1. Server:

[1130] Data collection and analysis methods: Real-time website data is collected from the Internet and analyzed using artificial intelligence to identify spelling mistakes, grammatical errors, reputation data, etc. This analysis determines which websites are safe and which may be fraudulent. The server continuously monitors the collected data and regularly updates the whitelist and blacklist.

[1131] URL scanning method: The system receives the URL that the user is trying to access immediately and scans it against the blacklist and whitelist. If there is a possibility of fraud, a warning message is sent to the user's device.

[1132] Emotion Engine: Analyzes the user's facial and voice data to recognize their emotional state in real time, and adaptively adjusts warning messages based on their emotions.

[1133] 2. Terminal:

[1134] URL transmission and warning display method: The URL entered by the user is sent to the server in real time, and a warning message received from the server is displayed. It also guides the user to a safe official website.

[1135] Camera and microphone: Captures the user's facial expressions and voice and sends them to the server as data for analysis by the emotion engine.

[1136] Hardware and software used:

[1137] Hardware:

[1138] Smartphone: Camera, microphone, network communication function

[1139] software:

[1140] OpenCV: Face detection and image processing

[1141] dlib: Face landmark detection

[1142] Keras: Running an emotion recognition model

[1143] Flask: Building a web server and REST API

[1144] Examples:

[1145] For example, consider the case where a user attempts to access an online banking website. In this system, the URL entered by the user is first sent to the server in real time. The server then uses artificial intelligence to analyze the URL and determine the reliability of the website. If it determines that there is a high possibility of fraud, a warning message is immediately sent to the user's device, displaying a message such as, "This site may be fraudulent. Click here for the official website."

[1146] Furthermore, if the user looks anxious, the emotion engine will analyze that information in real time and display a more detailed and reassuring message, such as, "Don't worry. We'll safely redirect you to the official website."

[1147] Example prompt for a generative AI model:

[1148] "A user is attempting to access a banking website. Use emotion recognition through camera video analysis to assess fraud risk in real time and display an appropriate warning message."

[1149] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1150] Step 1:

[1151] When a user attempts to access a website, the device retrieves the entered URL.

[1152] Input: The URL that the user types into the browser.

[1153] Output: The input URL.

[1154] Specific operation: The smartphone browser receives the user's input and passes the URL data to the device's URL sending module.

[1155] Step 2:

[1156] The terminal sends the acquired URL to the server.

[1157] Input: The URL that the user types into the browser.

[1158] Output: The URL sent to the server.

[1159] Specific operation: Using the device's network communication function, the obtained URL is sent to the server as an HTTP request.

[1160] Step 3:

[1161] The server scans incoming URLs in real time and analyzes them by referencing blacklists and whitelists.

[1162] Input: The URL sent to the server.

[1163] Output: Site security assessment result (safe / scam).

[1164] How it works: The server retrieves URL data, checks blacklists and whitelists from a pre-collected database to assess fraud risk, and uses artificial intelligence to analyze spelling mistakes, grammatical errors, and reputation data to determine safety.

[1165] Step 4:

[1166] Based on the analysis results, the server generates a warning message if there is a possibility of fraud and sends it to the user's device.

[1167] Input: Site security assessment results.

[1168] Output: A warning message.

[1169] Specific operation: If the server determines that there is a high possibility of fraud based on the analysis results, it generates a warning message and sends it to the user's device as an HTTP response.

[1170] Step 5:

[1171] The device will display a warning message to the user and provide a link directing them to a secure official website.

[1172] Input: The warning message sent by the server.

[1173] Output: A warning message to be displayed to the user along with a link to the official website.

[1174] Specific operation: The device will display the received warning message as a pop-up or in-browser warning screen, and at the same time provide a link to the official website.

[1175] Step 6:

[1176] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to the emotion engine.

[1177] Input: User facial and voice data.

[1178] Output: Facial expression and speech data sent to the emotion engine.

[1179] Specific operation: The device starts input from the camera and microphone, captures the user's facial expressions and voice data, and sends it to the server.

[1180] Step 7:

[1181] The server uses an emotion engine to analyze the user's emotions and adaptively adjusts warning messages based on the results.

[1182] Input: Captured facial and speech data.

[1183] Output: Adjusted warning message.

[1184] How it works: The emotion engine analyzes the received facial and voice data to determine the user's emotional state, and generates adaptive warning messages based on the results.

[1185] Step 8:

[1186] The device will then re-display the adjusted warning message to the user.

[1187] Input: The adjusted warning message.

[1188] Output: An adaptive warning message that is displayed to the user.

[1189] Specific operation: The device updates the warning message again and displays an adaptively adjusted warning message to the user based on the results of sentiment analysis.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] [Fourth embodiment]

[1194] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1195] 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.

[1196] 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).

[1197] 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.

[1198] 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.

[1199] 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).

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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.

[1204] 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.

[1205] 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.

[1206] 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."

[1207] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[1208] System Configuration

[1209] 1. Server

[1210] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[1211] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[1212] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[1213] 2. Terminal

[1214] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[1215] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[1216] 3. Users

[1217] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[1218] Specific program processing explanation

[1219] After the system is launched, the server first collects whitelists and blacklists from the Internet, as well as data on a wide variety of websites. This collected data is then analyzed by AI to evaluate the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites are determined, as are potentially fraudulent websites, and the blacklist or whitelist is updated accordingly.

[1220] When a user attempts to access a specific website, the device sends the URL to the server. The server scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and provides the URL of a safe, official website. The user can then confirm the warning and be directed to the safe website, thereby protecting them from fraud.

[1221] Specific examples

[1222] For example, consider a case where a user searches for "online bank" on the web and attempts to access a fake bank site. When the user enters the URL "fake-bank.example" and sends it to the server, the server analyzes the URL using AI. Based on spelling mistakes in the URL (e.g., 'bank' becomes 'bannk'), grammatical errors, and past reputation data, the AI ​​determines that the site is high risk. As a result, the server immediately issues a warning to the device, displays a message saying "This site may be fraudulent," and provides a link to a safe, official bank site (e.g., 'official-bank.com'). The user confirms this and proceeds to the official website to avoid becoming a victim of fraud.

[1223] Thus, the system of the present invention has specific means and actions to detect fraudulent sites in real time and protect users.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[1227] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[1228] Step 2:

[1229] The server passes the collected data to an AI analysis engine for initial analysis.

[1230] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[1231] Step 3:

[1232] The server updates the whitelist and blacklist based on the results of the initial analysis.

[1233] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[1234] Step 4:

[1235] The server monitors the Internet for new websites in real time.

[1236] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[1237] Step 5:

[1238] A user attempts to access a particular website.

[1239] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[1240] Step 6:

[1241] The server scans and determines in real time the URLs that users attempt to access.

[1242] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[1243] Step 7:

[1244] If the server determines that the site is fraudulent, it will issue a warning to the user.

[1245] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[1246] Step 8:

[1247] Provide users with a link to a secure official website.

[1248] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[1249] Step 9:

[1250] The user visits the official secure website provided.

[1251] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[1252] In this way, the system goes through a series of steps to detect fraudulent sites in real time and direct users to safe sites.

[1253] Example 1

[1254] 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."

[1255] In recent years, the increase in fraudulent and phishing sites on the Internet has made it difficult for users to browse the web safely. To prevent users from accessing these fraudulent sites, real-time reliability assessment and immediate warnings are necessary. However, conventional methods often suffer from delays in data analysis and updates, making it difficult to provide immediate protection to users. The present invention aims to solve these problems and enable users to use the Internet safely.

[1256] 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.

[1257] In this invention, the server includes means for collecting the latest data from the Internet in real time, means for analyzing websites for spelling mistakes, grammatical errors, and past reputation data using an AI model based on the collected data, means for evaluating the safety of websites based on the analysis results and updating blacklists and whitelists in real time, means for scanning URLs that users attempt to access in real time, warning users if there is a possibility of fraud and directing them to a safer official website, and means for transmitting URLs entered by users from their terminals to the server in real time, thereby enabling users to check the safety of websites in real time, prevent access to fraudulent sites, and be quickly directed to safe official websites.

[1258] "The latest data on the Internet" refers to the latest information, such as newly published websites on the Internet, updated information on existing websites, and related evaluation data.

[1259] "Real-time collection means" refers to technologies and methods for obtaining data on the Internet instantly and without delay.

[1260] A "spelling error" refers to an error that involves different characters that are not spelled correctly.

[1261] A "grammatical error" refers to a situation in which the grammatical rules or sentence structure in a sentence are incorrect.

[1262] "Historical Reputation Data" refers to previous user reviews, ratings, feedback, and credibility information about a website.

[1263] An "AI model" refers to an algorithm or system that uses machine learning, deep learning, etc. to analyze data and make predictions, classifications, and evaluations.

[1264] "Means of analysis" refers to techniques and methods for examining collected data and evaluating and classifying it based on certain criteria.

[1265] A "blacklist" refers to a list of websites that are rated as unsafe and potentially fraudulent.

[1266] A "whitelist" refers to a list of websites that are rated as highly secure and trustworthy.

[1267] "Real-time update methods" refers to the technology and methods used to instantly update information in databases and lists and keep them up to date.

[1268] "URL is an abbreviation for Uniform Resource Locator, and refers to the address used to indicate resources on the web (pages, images, videos, etc.).

[1269] "Scanning methods" refers to technologies and methods used to investigate and analyze the content of specific URLs or websites and assess their reliability and safety.

[1270] "Warning measures" refers to techniques or methods used to inform users when certain actions involve risks.

[1271] "Means of directing users to official websites" refers to techniques and methods that provide appropriate links and information to direct users to trusted and safe sites.

[1272] "Terminal" refers to a device that a user uses to access the Internet, and specifically includes a PC, smartphone, tablet, etc.

[1273] This invention is a system that collects, analyzes, and evaluates data on the Internet in real time. It aims to issue a warning when a user attempts to access a fraudulent website and guide the user to a safe, official website. A specific embodiment of this system is described below.

[1274] Server Roles

[1275] 1. Data collection

[1276] The server collects the latest data from the Internet using web scraping tools (e.g., BeautifulSoup, Scrapy) and Web APIs (e.g., Google Safe Browsing API).

[1277] What it does: It uses BeautifulSoup to get the HTML data of a specific webpage, extracts the URL of the new website and the existing reputation data, and calls the Google Safe Browsing API to get a list of dangerous websites.

[1278] 2. Data Analysis

[1279] The collected data is analyzed using natural language processing tools (e.g., spaCy, NLTK) and machine learning models (e.g., TensorFlow, PyTorch).

[1280] What it does: It uses spaCy to tokenize the text content of websites and detect spelling and grammatical errors, and then uses a TensorFlow model to calculate a risk score from the analysis results and assess their trustworthiness.

[1281] 3. Blacklist and Whitelist Management

[1282] Based on the analysis results, the server rates websites and updates the blacklist and whitelist in real time.

[1283] What it does: Accesses a PostgreSQL database and records the evaluation results. It adds websites that are likely to be fraudulent to a blacklist and websites that are deemed safe to a whitelist.

[1284] Device Role

[1285] 1. Sending the URL

[1286] The URL the user attempts to access is sent from the device to the server in real time.

[1287] Specific behavior: When a user enters a URL into a web browser, the device generates an HTTP request containing that information and sends it to the server.

[1288] User Roles

[1289] 1. Web Browsing

[1290] When a user browses the internet using a browser, the URLs they try to access are sent via their device to a server where they are scanned and analyzed.

[1291] Specific operation: When the user enters the URL they want to access and presses the Enter key, the URL is immediately sent from the device to the server.

[1292] Specific examples

[1293] For example, consider a user searching for "online banking" and attempting to access a fake banking site.

[1294] When a user enters the URL "fake-bank.example", this information is sent from the device to the server.

[1295] The server analyzes the URL in real time using AI, for example by looking for misspellings (such as "bannk") and past reputation data to determine whether it is high risk.

[1296] If the server determines that the URL is dangerous, it will send a warning message to the device saying, "This site may be fraudulent," and provide a link to the safe official website ("official-bank.com").

[1297] Users can avoid becoming a victim of fraud by checking the warning and going to the official website.

[1298] Prompt Sentence Examples

[1299] "When a user searches for an online bank and tries to access a fraudulent site like 'fake-bank.example', explain how this system will detect the site as fraudulent and issue a warning."

[1300] This invention collects data on the Internet in real time and analyzes it instantly to prevent users from accessing fraudulent sites and provide a safer web experience.

[1301] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1302] Step 1:

[1303] The server collects the latest data from the Internet.

[1304] Specific operations and inputs / outputs:

[1305] Input: List of web page URLs, API endpoints.

[1306] Processing: Use BeautifulSoup to get the HTML data of the specified web page, and use Scrapy to extract the necessary information (e.g., URLs and reputation data of new websites). Also, obtain dangerous site information from the Google Safe Browsing API.

[1307] Output: Extracted website information and dangerous site list.

[1308] Step 2:

[1309] The server analyzes the collected data.

[1310] Specific operations and inputs / outputs:

[1311] Input: HTML data, website URL list, dangerous site information.

[1312] Processing: Uses spaCy to tokenize text content and detect spelling and grammar errors. Uses a TensorFlow model to calculate a website reputation score.

[1313] Output: Analysis results (spelling mistakes, grammar errors, evaluation score).

[1314] Step 3:

[1315] The server manages the blacklist and whitelist.

[1316] Specific operations and inputs / outputs:

[1317] Input: Analysis results (rating score), website URL list.

[1318] Action: Access the PostgreSQL database and update the blacklist and whitelist based on the evaluation results. Add high-risk sites to the blacklist and safe sites to the whitelist.

[1319] Output: Updated blacklists and whitelists.

[1320] Step 4:

[1321] The device sends the URL entered by the user to the server.

[1322] Specific operations and inputs / outputs:

[1323] Input: The URL entered by the user.

[1324] Processing: After the user enters a URL into the browser, the device generates an HTTP request and sends the URL information to the server.

[1325] Output: Data sent to the server (URL).

[1326] Step 5:

[1327] The server scans and evaluates the URLs it receives.

[1328] Specific operations and inputs / outputs:

[1329] Input: URL sent from device, blacklist, whitelist.

[1330] Processing: The server searches the received URL in the form of a database query to see if it is included in the blacklist or whitelist. If it is not included, a new real-time analysis is performed using AI.

[1331] Output: URL evaluation result (safe, dangerous, etc.).

[1332] Step 6:

[1333] The device will display a warning to the user.

[1334] Specific operations and inputs / outputs:

[1335] Input: URL evaluation results received from the server.

[1336] Action: For URLs that are determined to be high risk, the device will generate a browser pop-up with the message "This site may be fraudulent" and provide a link to the official, safe site.

[1337] Output: A warning message and a safe link to display to the user.

[1338] (Application example 1)

[1339] 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."

[1340] When using the Internet, the risk of users accessing fraudulent websites is increasing. However, current security systems lack the ability to issue real-time warnings the moment a user attempts to access a fraudulent website and quickly guide the user to a safe site. As a result, cases of users accessing fraudulent websites continue to increase. Furthermore, the lack of consistency in warnings, especially for users using visual devices, makes it difficult to immediately prevent access to fraudulent websites.

[1341] 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.

[1342] In this invention, the server includes means for collecting data on the Internet in real time, means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and warning the user if the URL is potentially fraudulent, means for directing the user to a safe, official website, and means for displaying a warning on the user's visual device in real time. This allows the user to be informed of the risk of encountering a fraudulent website in real time while using the Internet, and to be promptly directed to a safe website by receiving a warning via the visual device, thereby preventing fraud.

[1343] "Means for collecting data on the Internet" refers to devices or software that have the ability to obtain data from a wide variety of websites on the Internet in real time.

[1344] "AI methods for analyzing website spelling mistakes, grammatical errors, and reputation data" refers to artificial intelligence technology that analyzes the content of websites based on collected data and uses specific algorithms to evaluate spelling mistakes, grammatical errors, and past reputation information.

[1345] "Means for determining whether a website is safe or potentially fraudulent based on the results of analysis" refers to devices or software that have the ability to evaluate and classify whether a specific website is safe or potentially fraudulent based on the results of AI analysis.

[1346] "Means for scanning URLs that users attempt to access in real time and issuing a warning to users if there is a possibility of fraud" refers to devices or software that instantly analyzes the URL entered by the user and displays a warning to the user if the URL is determined to pose a risk of fraud.

[1347] "Means for redirecting users to a safe official website" refers to devices or software that have the function of redirecting users who attempt to access a potentially fraudulent website to a pre-defined safe official website.

[1348] "Means for displaying a real-time warning to a user's visual device" refers to devices or software that have the ability to instantly display a warning to a user about accessing a fraudulent website on a visual device such as smart glasses or a head-mounted display.

[1349] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, and guides the user to a safe, official website. This system is composed of elements such as a server, a terminal, and a user, and by linking these elements, it achieves rapid and accurate detection of fraudulent websites and user protection.

[1350] server

[1351] The server is responsible for the following functions:

[1352] 1. Data Collection:

[1353] The server collects data from a wide variety of websites on the Internet in real time. It also continuously monitors new websites and incorporates this data into the system. This ensures that the whitelist and blacklist databases are always kept up to date.

[1354] 2. Data Analysis:

[1355] The server uses AI to analyze the collected data, including spelling mistakes, grammatical errors, and reputation data of websites. The AI ​​model used is RiskAssessmentModel. The server also analyzes past evaluation data to assess the risk of each website.

[1356] 3. Judgment and Updates:

[1357] Based on the analysis results, it determines which websites are safe and which may be fraudulent. The results are immediately reflected in the database, and blacklists and whitelists are updated.

[1358] Terminal

[1359] The terminal is responsible for the following functions:

[1360] 1. Real-time scanning:

[1361] The URL that the user is trying to access is sent to the server in real time, and the device immediately relays the user's access request to the server and receives the scan results.

[1362] 2. Warnings and Guidance:

[1363] If a potential scam is detected, the device will immediately display a warning to the user and provide a URL to a safe, official website, which will be displayed via a visual device (e.g., smart glasses).

[1364] user

[1365] Users use a web browser to browse the Internet. When a user attempts to access a particular website, the URL is sent via the device to a server where it is scanned and analyzed.

[1366] Specific processing and implementation method

[1367] The server collects whitelists, blacklists, and website data from the internet in real time. This data is analyzed by an AI model (RiskAssessmentModel), which evaluates spelling mistakes, grammatical errors, and past reputation data to determine the risk of a fraudulent website.

[1368] When a user tries to access "fake-bank.example" using smart glasses, the URL is sent to the server. If the server analyzes the URL and determines it to be high-risk, it immediately displays a warning on the smart glasses and provides a link to the official, secure website ("official-bank.com").

[1369] For example, a sample prompt might look like this:

[1370] "The website I'm trying to access has the URL "fake-bank.example". Please provide the data needed to assess whether this website is a scam. The required data elements are:

[1371] Misspellings in the URL

[1372] Past reputation data

[1373] Grammar error on website"

[1374] As a result, this system determines the risk of fraudulent websites in real time when using the Internet, and provides users with immediate warnings via visual devices, quickly directing them to safe websites and preventing them from becoming victims of fraud.

[1375] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1376] Step 1:

[1377] (Data Collection): The server collects data from a wide variety of websites on the Internet in real time. It constantly monitors the appearance of new websites and imports them into a database. The input is the URL of the website on the Internet, and the output is the collected website data. Data processing involves analyzing and saving the necessary metadata and HTML code.

[1378] Step 2:

[1379] (Data analysis): The server uses the collected data to analyze the website's spelling mistakes, grammatical errors, and reputation data using an AI model called RiskAssessmentModel. The input is the collected website data, and the output is a risk assessment score based on the analysis results. As a data calculation, the AI ​​model evaluates the collected data and generates a risk score.

[1380] Step 3:

[1381] (Risk Assessment): The server assesses the risk of each website based on the analysis results. It categorizes websites as safe or potentially fraudulent, and updates the information in the database. The input is the risk assessment score, and the output is the updated whitelist and blacklist. Data processing involves categorizing websites as safe or dangerous based on the risk score.

[1382] Step 4:

[1383] (URL Scan): When a user attempts to access a specific URL through a web browser, the device sends the URL to the server in real time. The input is the URL the user attempts to access, and the output is the URL sent to the server. Specifically, the device immediately relays the URL to the server.

[1384] Step 5:

[1385] (Risk assessment): The server scans the transmitted URL in real time and assesses its risk based on blacklists and whitelists. The input is the URL transmitted from the terminal, and the output is the assessment result (risk score). Specifically, the server checks whether it corresponds to the blacklist.

[1386] Step 6:

[1387] (Warning): If the fraud risk is determined to be high, the server issues a warning to the user's visual device (such as smart glasses) via the terminal. The input is the risk assessment result, and the output is a warning message. Specifically, the server transfers the warning message and the URL of a safe official website to the terminal.

[1388] Step 7:

[1389] (User Guidance): The device displays a real-time warning on the user's visual device and provides a link to the official secure website. The input is the warning message from the server and the URL of the official website, and the output is the warning message displayed on the visual device. Specifically, the device displays the warning message and the link to the official website on the visual device's display.

[1390] These steps enable the system to assess the risk of fraudulent sites in real time, warn users through visual devices, and quickly direct them to safe websites.

[1391] 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.

[1392] This system collects and analyzes internet data in real time, warns users when they attempt to access fraudulent websites, and guides them to safe, official websites. It also recognizes users' emotions and adaptively adjusts the warnings and guidance. This system is comprised of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[1393] System Configuration

[1394] 1. Server

[1395] It has a means of collecting the latest data from the Internet. The server monitors newly appearing websites and existing evaluation data in real time and immediately imports it into the system.

[1396] It has AI means to analyze the collected data, and the server will give each website a risk assessment based on spelling mistakes, grammatical errors, past reputation data, etc.

[1397] Based on the analysis results, the system is equipped with a means to distinguish between fraudulent and safe sites. The server updates the results of this judgment to a database and manages blacklists and whitelists in real time.

[1398] It is equipped with an emotion engine that recognizes the user's emotions. The emotion engine has the ability to analyze data such as the user's facial expressions and voice to determine their emotional state.

[1399] 2. Terminal

[1400] It has a means to send the URL that the user is trying to access to the server in real time. The terminal immediately relays the user's access request to the server and receives the scan results.

[1401] The device has a means to warn users of possible fraud. The device displays a warning message from the server to the user, urging them to stop accessing the device.

[1402] 3. Users

[1403] When a user browses the Internet using a web browser, the URL entered by the user is sent via the device to a server where it is scanned and analyzed.

[1404] Emotional state is recognized by the emotion engine, which uses the user's facial and voice data to determine emotions in real time.

[1405] Specific program processing explanation

[1406] After the system is launched, the server first collects whitelists and blacklists, as well as data on various websites from the Internet. This collected data is then analyzed by an AI analysis engine, which evaluates the websites' spelling mistakes, grammatical errors, and past reputation data. Based on the results of this analysis, safe websites and potentially fraudulent websites are determined, and the blacklist or whitelist is updated accordingly.

[1407] When a user attempts to access a specific website, the device sends the URL to the server. The server then scans the URL in real time and evaluates its likelihood of being a fraudulent site based on blacklists and whitelists. If it determines that the site is potentially fraudulent, the server immediately displays a warning to the user via the device and suggests the URL of a safe, official website. Furthermore, the emotion engine analyzes the user's emotional state and adaptively adjusts the warning message based on that emotion. This adjustment allows the user to receive the warning in a more understandable format.

[1408] Specific examples

[1409] For example, consider a case where a user searches for "online bank" and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example," is sent to the server, where the server analyzes the URL using AI. Based on spelling errors (e.g., 'bank' replaced with 'bannk'), grammatical errors, and past reputation data, the AI ​​determines the site to be high risk. As a result, the server immediately issues a warning to the device, displays a message stating, "This site may be fraudulent," and provides a link to the safe, official bank site (e.g., 'official-bank.com'). At the same time, the emotion engine analyzes the user's facial expressions and voice. For example, if the user appears anxious, the server adjusts the warning message to include more detailed and clearer explanations.

[1410] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[1411] The processing flow will be explained below.

[1412] Step 1:

[1413] The server collects a wide variety of data from the Internet, including whitelists and blacklists.

[1414] Specific operation: The server accesses databases of public institutions, user report data, and data lists provided by third parties, and periodically downloads this data and imports it into the system.

[1415] Step 2:

[1416] The server passes the collected data to an AI analysis engine for initial analysis.

[1417] How it works: The server inputs spelling mistakes, grammatical errors, and past reputation data into the AI ​​to generate a risk rating for each website. In this process, the AI ​​learns the characteristics of fraudulent websites.

[1418] Step 3:

[1419] The server updates the whitelist and blacklist based on the results of the initial analysis.

[1420] Specific operation: The server classifies websites based on the analysis results, adding websites that are deemed safe to a whitelist and websites that are likely to be fraudulent to a blacklist.

[1421] Step 4:

[1422] The server monitors the Internet for new websites in real time.

[1423] How it works: The server continuously scans new domain registration information and the latest websites, sending them to an AI analysis engine for real-time analysis.

[1424] Step 5:

[1425] A user attempts to access a particular website.

[1426] Specific operation: When a user enters a URL using a browser, the device sends this URL to the server.

[1427] Step 6:

[1428] The server scans and determines in real time the URLs that users attempt to access.

[1429] How it works: The server immediately passes the received URL to an AI analysis engine, which checks it against blacklists and whitelists, while also re-evaluating spelling mistakes, grammatical errors, and past reputation data.

[1430] Step 7:

[1431] If the server determines that the site is fraudulent, it will issue a warning to the user.

[1432] Specific operation: The server sends a warning message to the device, displaying an alert saying "This site may be fraudulent."

[1433] Step 8:

[1434] The terminal transmits the user's emotional state to the emotion engine.

[1435] Specific operation: The device uses the built-in camera and microphone to collect the user's facial expressions and voice, and sends that data to the emotion engine.

[1436] Step 9:

[1437] The emotion engine analyzes the user's emotional state.

[1438] Specific behavior: The emotion engine analyzes collected facial and voice data to determine the user's emotional state, such as anxious, confused, or calm.

[1439] Step 10:

[1440] The server adjusts the warning message based on the analysis results from the emotion engine.

[1441] Specific operation: The server adaptively changes the content and display method of the warning message according to the user's emotional state obtained from the emotion engine. For example, if the user is anxious, it displays a more detailed and reassuring message.

[1442] Step 11:

[1443] The server presents the user with a link to the official secure website.

[1444] Specific operation: The server refers to a whitelist or official database to find a safe URL and sends the link to the device.

[1445] Step 12:

[1446] The user visits the official secure website provided.

[1447] What it does: Users acknowledge the alert message and are redirected to a safe URL, avoiding access to fraudulent sites.

[1448] In this way, the system detects fraudulent sites in real time through a series of steps, provides adaptive warning messages based on user sentiment, and guides users to safe sites.

[1449] Example 2

[1450] 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."

[1451] Many websites on the Internet are fraudulent or malicious, and accessing them poses a risk to users. This risk is particularly pronounced when users access sites that handle important information. Furthermore, issuing uniform warning messages makes it difficult to encourage users to take appropriate action and fails to take into account the individual emotional state of each user. Therefore, there is a need for a system that can detect fraudulent websites in real time and adaptively adjust warning messages according to the user's emotional state.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1453] In this invention, the server includes means for collecting data on the Internet in real time, AI means for analyzing website spelling mistakes, grammatical errors, and reputation data based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, means for scanning URLs that users attempt to access in real time and issuing a warning to the user if the URL is potentially fraudulent, means for directing the user to a safe official website, and emotion recognition means for analyzing the user's emotional state and adjusting the warning message. This allows users to be directed to safe websites in real time and receive appropriate warning messages according to their individual emotional state.

[1454] "Means for collecting data on the Internet in real time" refers to a device or method that constantly monitors websites and evaluation data on the Internet and instantly retrieves the latest information.

[1455] A "spelling error" refers to a word that is not spelled correctly, often due to a typing error or a lack of understanding of the language.

[1456] A "grammatical error" refers to an expression or structure that violates the grammatical rules of the language being used, and often makes it difficult for the reader to understand.

[1457] "Reputation data" refers to past user feedback and evaluation information about a website and its operator, and is used as an indicator of reliability and safety.

[1458] "AI means" refers to algorithms or devices that use artificial intelligence techniques to analyze data and recognize various patterns and features.

[1459] "Website assessment means" refers to a process or device that determines whether a website is safe or potentially fraudulent based on the analyzed data.

[1460] "Real-time URL scanning means" refers to a method or device that instantly inspects the URL of a website a user is attempting to access and assesses its safety.

[1461] "Means for issuing a warning" refers to a method or device that displays a message to a user urging them not to take a particular action.

[1462] "Means for guiding users to a safe official website" refers to a method or device for navigating users to a safe and reliable official website.

[1463] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice data to determine their emotional state at any given time.

[1464] "Database management means" refers to a method or device for efficiently managing and updating data such as whitelists and blacklists.

[1465] This invention is a system that collects and analyzes data on the Internet in real time, issues a warning when a user attempts to access a fraudulent website, guides the user to a safe, official website, and recognizes the user's emotions to adaptively adjust the warning and guidance. The system is composed of a server, terminals, users, and an emotion engine, and their cooperation enables rapid and accurate detection of fraudulent websites and user protection.

[1466] Hardware and software used

[1467] 1. Server

[1468] As a means of collecting data, we use data streaming platforms such as Apache Kafka to collect the latest data from the Internet.

[1469] Deep learning frameworks such as TensorFlow and PyTorch are used as AI analysis tools.

[1470] A database management system (MySQL, PostgreSQL, etc.) is used as the database management method.

[1471] To recognize emotions, the system uses Microsoft Azure's Emotion API and Google Cloud Vision to analyze the user's facial expressions and voice data.

[1472] 2. Terminal

[1473] A browser extension is used to send URLs. The device captures the user's access request and sends it to the server in real time.

[1474] To issue a warning, JavaScript is used to display a warning message to the user.

[1475] Example

[1476] For example, consider the case where a user searches for "online bank" on the web and attempts to access a fake bank site. The URL entered by the user, "fake-bank.example.com," is sent to the server via the terminal. The server analyzes this URL using an AI analysis engine. The analysis evaluates spelling and grammatical errors contained in the URL, as well as past reputation data. Based on this evaluation, the server determines that "fake-bank.example.com" is high risk. After the judgment is made, the server updates the database and adds "fake-bank.example.com" to the blacklist.

[1477] The server then immediately issues a warning to the user via their device. Specifically, it displays the warning message "This site may be fraudulent" along with a link to the safe official bank site, "official-bank.com." At the same time, the emotion engine analyzes the user's facial expressions and voice, and if the user appears anxious, it adjusts the warning message to include a more detailed and easy-to-understand explanation.

[1478] Prompt Sentence Examples

[1479] "When a user attempts to access a fake online banking site, the server analyzes the URL in real time and assesses its likelihood of being a fraudulent site. If it is likely to be fraudulent, the server displays a warning message through the device and provides a link to a safe website. In addition, an emotion engine analyzes the user's emotional state and adaptively adjusts the warning message."

[1480] In this way, the system of the present invention has specific means and operations to detect fraudulent sites in real time and guide users to safe sites by providing adaptive warning messages according to the user's emotions.

[1481] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1482] Step 1:

[1483] The server uses a data streaming platform such as Apache Kafka to collect evaluation data and newly created website data from the Internet in real time. The input is various data on the Internet, and the output is that the data is imported into the server.

[1484] Step 2:

[1485] The server uses deep learning frameworks such as TensorFlow to analyze the data collected. The input is the collected website data, and the output is a risk assessment based on the website's spelling mistakes, grammatical errors, and past reputation data. Specifically, the analysis engine analyzes the text data of each website and extracts specific features.

[1486] Step 3:

[1487] Based on the analysis results, the server determines which websites are safe and which are potentially fraudulent, and updates the blacklist and whitelist using a MySQL database. The input is the risk assessment result obtained in step 2, and the output is the updated blacklist and whitelist. Specifically, it adds websites that are likely to be fraudulent to the blacklist and adds safe websites to the whitelist.

[1488] Step 4:

[1489] When a user tries to access a website in their browser, their device sends the URL to the server in real time. The input is the URL entered by the user, and the output is the URL sent to the server. Specifically, the browser extension activates, captures the URL, and relays it to the server.

[1490] Step 5:

[1491] The server scans the received URL with an AI analysis engine and performs a risk assessment by referencing blacklists and whitelists. The input is the URL the user is attempting to access, and the output is the risk assessment result for that URL. Specifically, the server compares the URL with a past database to determine whether the URL is likely to be fraudulent.

[1492] Step 6:

[1493] If the server determines that a site is at high risk of fraud, it displays a warning message to the user via the device. The input is the risk assessment result, and the output is the warning message to be displayed to the user. Specifically, it uses JavaScript to display the warning message "This site may be fraudulent" in the browser and provides a link to the official, safe website.

[1494] Step 7:

[1495] The server uses Microsoft Azure's Emotion API to analyze the user's emotional state and adaptively adjust the warning message. The input is the user's facial and voice data, and the output is the adjusted warning message. Specifically, the emotion engine analyzes the user's real-time facial expressions and voice, and if, for example, the user looks anxious, it adjusts the display of a warning message with a detailed explanation.

[1496] Through these steps, the system achieves fast and accurate detection of fraudulent websites and user protection.

[1497] (Application example 2)

[1498] 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."

[1499] Conventional Internet website security systems focus on detecting fraudulent websites, but do not adaptively adjust warning messages to take into account the user's emotional state. This results in problems such as users overlooking or finding the warning messages difficult to understand. Specifically, while the risk of accessing fraudulent websites is high, users are not adequately protected because notifications are not adaptively tailored to the user's emotions. There is also a need for a system that can monitor new fraudulent websites in real time and respond immediately whenever they appear.

[1500] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1501] In this invention, the server includes means for collecting data on the Internet in real time, artificial intelligence means for analyzing spelling mistakes, grammatical errors, and reputation data of websites based on the collected data, means for determining whether a website is safe or potentially fraudulent based on the analysis results, and means for recognizing user emotions in real time and adaptively adjusting warning messages according to those emotions. This makes it possible to issue a warning in real time when a user attempts to access a fraudulent website, and by optimizing the warning message to suit the user's emotions, it becomes possible to easily understand and attract the user's attention and guide them to a safe official website.

[1502] "Real time" means processing and responding immediately at that moment.

[1503] "Internet data" refers to information found on websites, social media, news feeds, review sites, etc.

[1504] "Means of collection" refers to devices and programs used to acquire and store data on the Internet.

[1505] A "spelling error" is a misspelling of a word in a sentence.

[1506] A "grammatical error" is a structural or phrasal mistake in a sentence that violates the rules.

[1507] "Reputation Data" refers to user ratings and reviews of websites and services.

[1508] "Artificial intelligence means" refers to algorithms and systems for analyzing data, learning, making predictions, etc.

[1509] A "safe website" is a legitimate site that is free from fraud and malicious intent.

[1510] A "potentially fraudulent website" is a fraudulent site that exists to deceive users.

[1511] "Means for determining" refers to devices or programs that distinguish between safe and fraudulent based on the analysis results.

[1512] "Scanning" means checking data and URLs in real time.

[1513] "Means for issuing a warning" refers to a device or program for displaying a warning message to the user.

[1514] "Means for directing users to official websites" are devices or programs for redirecting users to safe official websites.

[1515] "Emotion" is a psychological state that is inferred based on the user's facial expressions and voice.

[1516] The "means for recognizing and adaptively adjusting the warning message in accordance with the emotion" refers to a device or program for changing the content of the warning based on the user's emotional state.

[1517] This invention is a system that allows users to enjoy a safe browsing experience on the Internet. The system of the present invention is composed of a server that collects and analyzes data on the Internet in real time, a user's terminal, and an emotion engine that recognizes the user's emotions and adjusts warning messages.

[1518] System configuration

[1519] 1. Server:

[1520] Data collection and analysis methods: Real-time website data is collected from the Internet and analyzed using artificial intelligence to identify spelling mistakes, grammatical errors, reputation data, etc. This analysis determines which websites are safe and which may be fraudulent. The server continuously monitors the collected data and regularly updates the whitelist and blacklist.

[1521] URL scanning method: The system receives the URL that the user is trying to access immediately and scans it against the blacklist and whitelist. If there is a possibility of fraud, a warning message is sent to the user's device.

[1522] Emotion Engine: Analyzes the user's facial and voice data to recognize their emotional state in real time, and adaptively adjusts warning messages based on their emotions.

[1523] 2. Terminal:

[1524] URL transmission and warning display method: The URL entered by the user is sent to the server in real time, and a warning message received from the server is displayed. It also guides the user to a safe official website.

[1525] Camera and microphone: Captures the user's facial expressions and voice and sends them to the server as data for analysis by the emotion engine.

[1526] Hardware and software used:

[1527] Hardware:

[1528] Smartphone: Camera, microphone, network communication function

[1529] software:

[1530] OpenCV: Face detection and image processing

[1531] dlib: Face landmark detection

[1532] Keras: Running an emotion recognition model

[1533] Flask: Building a web server and REST API

[1534] Examples:

[1535] For example, consider the case where a user attempts to access an online banking website. In this system, the URL entered by the user is first sent to the server in real time. The server then uses artificial intelligence to analyze the URL and determine the reliability of the website. If it determines that there is a high possibility of fraud, a warning message is immediately sent to the user's device, displaying a message such as, "This site may be fraudulent. Click here for the official website."

[1536] Furthermore, if the user looks anxious, the emotion engine will analyze that information in real time and display a more detailed and reassuring message, such as, "Don't worry. We'll safely redirect you to the official website."

[1537] Example prompt for a generative AI model:

[1538] "A user is attempting to access a banking website. Use emotion recognition through camera video analysis to assess fraud risk in real time and display an appropriate warning message."

[1539] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1540] Step 1:

[1541] When a user attempts to access a website, the device retrieves the entered URL.

[1542] Input: The URL that the user types into the browser.

[1543] Output: The input URL.

[1544] Specific operation: The smartphone browser receives the user's input and passes the URL data to the device's URL sending module.

[1545] Step 2:

[1546] The terminal sends the acquired URL to the server.

[1547] Input: The URL that the user types into the browser.

[1548] Output: The URL sent to the server.

[1549] Specific operation: Using the device's network communication function, the obtained URL is sent to the server as an HTTP request.

[1550] Step 3:

[1551] The server scans incoming URLs in real time and analyzes them by referencing blacklists and whitelists.

[1552] Input: The URL sent to the server.

[1553] Output: Site security assessment result (safe / scam).

[1554] How it works: The server retrieves URL data, checks blacklists and whitelists from a pre-collected database to assess fraud risk, and uses artificial intelligence to analyze spelling mistakes, grammatical errors, and reputation data to determine safety.

[1555] Step 4:

[1556] Based on the analysis results, the server generates a warning message if there is a possibility of fraud and sends it to the user's device.

[1557] Input: Site security assessment results.

[1558] Output: A warning message.

[1559] Specific operation: If the server determines that there is a high possibility of fraud based on the analysis results, it generates a warning message and sends it to the user's device as an HTTP response.

[1560] Step 5:

[1561] The device will display a warning message to the user and provide a link directing them to a secure official website.

[1562] Input: The warning message sent by the server.

[1563] Output: A warning message to be displayed to the user along with a link to the official website.

[1564] Specific operation: The device will display the received warning message as a pop-up or in-browser warning screen, and at the same time provide a link to the official website.

[1565] Step 6:

[1566] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to the emotion engine.

[1567] Input: User facial and voice data.

[1568] Output: Facial expression and speech data sent to the emotion engine.

[1569] Specific operation: The device starts input from the camera and microphone, captures the user's facial expressions and voice data, and sends it to the server.

[1570] Step 7:

[1571] The server uses an emotion engine to analyze the user's emotions and adaptively adjusts warning messages based on the results.

[1572] Input: Captured facial and speech data.

[1573] Output: Adjusted warning message.

[1574] How it works: The emotion engine analyzes the received facial and voice data to determine the user's emotional state, and generates adaptive warning messages based on the results.

[1575] Step 8:

[1576] The device will then re-display the adjusted warning message to the user.

[1577] Input: The adjusted warning message.

[1578] Output: An adaptive warning message that is displayed to the user.

[1579] Specific operation: The device updates the warning message again and displays an adaptively adjusted warning message to the user based on the results of sentiment analysis.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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).

[1587] 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.

[1588] 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."

[1589] 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.

[1590] 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).

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] The following is further disclosed regarding the above embodiment.

[1602] (Claim 1)

[1603] A means for collecting data over the Internet in real time;

[1604] Based on the collected data, AI methods are used to analyze websites for spelling mistakes, grammatical errors, and reputation data.

[1605] Based on the analysis results, a method for determining whether a website is safe or potentially fraudulent;

[1606] A means of scanning URLs that users attempt to access in real time and alerting users to potential fraud;

[1607] A means of redirecting users to a secure official website;

[1608] A system including:

[1609] (Claim 2)

[1610] further comprising database management means for periodically updating the whitelist and blacklist;

[1611] 10. The system of claim 1.

[1612] (Claim 3)

[1613] further including means for monitoring emerging websites and updating the data in real time;

[1614] 10. The system of claim 1.

[1615] "Example 1"

[1616] (Claim 1)

[1617] A means of collecting the latest data on the Internet in real time,

[1618] Using the collected data, we analyze websites for spelling mistakes, grammatical errors, and historical reputation data using AI models; and

[1619] Based on the analysis results, a method for evaluating the safety of websites and updating blacklists and whitelists in real time is provided.

[1620] It scans URLs that users try to access in real time, warns users if there is a possibility of fraud, and directs them to safer official websites.

[1621] A means to send the URL entered by the user from the terminal to the server in real time,

[1622] A system including:

[1623] (Claim 2)

[1624] 10. The system of claim 1, further comprising database management means for periodically updating the whitelist and blacklist and for maintaining trustworthiness ratings.

[1625] (Claim 3)

[1626] 10. The system of claim 1, further comprising means for monitoring newly emerging websites and updating the data in real time.

[1627] "Application Example 1"

[1628] (Claim 1)

[1629] A means for collecting data over the Internet in real time;

[1630] Based on the collected data, AI methods are used to analyze websites for spelling mistakes, grammatical errors, and reputation data.

[1631] Based on the analysis results, a method for determining whether a website is safe or potentially fraudulent;

[1632] A means of scanning URLs that users attempt to access in real time and alerting users to potential fraud;

[1633] A means of redirecting users to a secure official website;

[1634] means for displaying a real-time alert to a user's visual device;

[1635] A system including:

[1636] (Claim 2)

[1637] 10. The system of claim 1, further comprising database management means for periodically updating the whitelist and the blacklist.

[1638] (Claim 3)

[1639] 10. The system of claim 1, further comprising means for monitoring newly emerging websites and updating the data in real time.

[1640] "Example 2: Combining Emotion Engines"

[1641] (Claim 1)

[1642] A means for collecting data over the Internet in real time;

[1643] Based on the collected data, AI methods are used to analyze websites for spelling mistakes, grammatical errors, and reputation data.

[1644] Based on the analysis results, a method for determining whether a website is safe or potentially fraudulent;

[1645] A means of scanning URLs that users attempt to access in real time and alerting users to potential fraud;

[1646] A means of redirecting users to a secure official website;

[1647] emotion recognition means for analyzing the user's emotional state and adjusting the warning message;

[1648] A system including:

[1649] (Claim 2)

[1650] further comprising database management means for periodically updating the whitelist and blacklist;

[1651] 10. The system of claim 1.

[1652] (Claim 3)

[1653] further including means for monitoring emerging websites and updating the data in real time;

[1654] 10. The system of claim 1.

[1655] (Claim 4)

[1656] and further comprising means for analyzing the user's facial expression and voice data to recognize the user's emotional state.

[1657] 10. The system of claim 1.

[1658] (Claim 5)

[1659] and means for adaptively adjusting the warning message based on the emotion recognition result.

[1660] 10. The system of claim 1.

[1661] "Application example 2 when combining emotion engines"

[1662] (Claim 1)

[1663] A means for collecting data over the Internet in real time;

[1664] Artificial intelligence means to analyze the website's spelling mistakes, grammatical errors, and reputation data based on the collected data;

[1665] Based on the analysis results, a method for determining whether a website is safe or potentially fraudulent;

[1666] A means of scanning URLs that users attempt to access in real time and alerting users to potential fraud;

[1667] A means of redirecting users to a secure official website;

[1668] a means for recognizing a user's emotions in real time and adaptively adjusting a warning message according to the emotions;

[1669] A system including:

[1670] (Claim 2)

[1671] further comprising database management means for periodically updating the whitelist and blacklist;

[1672] 10. The system of claim 1.

[1673] (Claim 3)

[1674] further including means for monitoring emerging websites and updating the data in real time;

[1675] 10. The system of claim 1. [Explanation of symbols]

[1676] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting data over the Internet in real time; Based on the collected data, AI methods are used to analyze websites for spelling mistakes, grammatical errors, and reputation data. Based on the analysis results, a method for determining whether a website is safe or potentially fraudulent; A means of scanning URLs that users attempt to access in real time and alerting users to potential fraud; A means of redirecting users to a secure official website; A system including:

2. further comprising database management means for periodically updating the whitelist and blacklist; The system of claim 1 .

3. further including means for monitoring emerging websites and updating the data in real time; The system of claim 1 .

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