System

Abstract

A system is provided.SOLUTION: This system includes a means for analyzing information contents browsed by a user, a means for transmitting the analyzed information contents to a server, a means for analyzing the possibility of fake news based on the information contents by the server, a means for transmitting the determined result of the fake news to terminals by the server, and a means for displaying warning to the user based on the determined result by the terminals.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, as the speed at which information spreads has increased, the risk of fake news spreading has increased, especially on social media and the web. In such situations, many users may believe false information, causing confusion and anxiety throughout society. Misinformation can spread rapidly during emergencies, particularly disasters, and can potentially exacerbate the damage, so there is a need to prevent this. However, existing information filtering systems and fake news detection systems face the challenge of being insufficiently automated and not being provided in a format that is easy for users to use. [Means for solving the problem]

[0005] The present invention provides a system that analyzes information content viewed by a user in real time and displays a warning if the content is likely to be fake news. Specifically, the system includes a means for analyzing the information content viewed by the user, a means for transmitting the analyzed information content to a server, a means for the server to analyze the possibility of fake news based on the information content, a means for the server to transmit a fake news determination result to a terminal, and a means for the terminal to display a warning to the user based on the determination result. The system also adds a function to maintain a database of reliable primary information sites and identify possible fake news by comparing the analyzed information content with the database, as well as a function to monitor temporary files in social networking applications and web browsers, thereby reducing the risk of users believing false information. This allows users to act based on reliable information.

[0006] "User" refers to an end user who views information content.

[0007] "Information content" means digital content such as text, images, and videos that are viewed on social media applications or web browsers.

[0008] "Means for analyzing" refers to software or hardware functionality for analyzing information content to identify its substance or attributes.

[0009] "Server" means a computer system for analyzing information content and determining fake news via a network.

[0010] "Fake news" refers to news or information that is created with the intention to be false or misleading.

[0011] "Warning" means a message or notification that alerts users to information content that is likely to be fake news.

[0012] "Trusted primary information sites" refer to reputable public sources and websites run by public institutions.

[0013] "Database" means a structured store of information for efficient retrieval and collation of information.

[0014] "Means of verification" refers to the functionality for comparing information content with the database of a reliable primary information site to check for matches or inconsistencies.

[0015] "SNS Application" means a software application that provides social networking services.

[0016] "Web browser" refers to a software application used to view web pages and information on the Internet.

[0017] "Temporary file" means a file used by a terminal for temporary storage.

[0018] "Analysis module" refers to a software component for analyzing the content of information content.

[0019] "AI analysis" means the process of using artificial intelligence techniques to analyze information content and evaluate its reliability and accuracy. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] overview

[0042] This system analyzes information content viewed by users on social media applications or web browsers in real time and displays a warning if the content is suspected to be fake news. In particular, it uses a database of trusted primary information sites to collate the information content and identify fake news.

[0043] Specific Embodiments

[0044] 1. User installs the app

[0045] Users install a dedicated application on their smartphone, and when they launch the app for the first time, it requests necessary permissions, including permission to access data from social networking applications and web browsers.

[0046] 2. Monitoring social media content

[0047] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0048] 3. Sending to the server for content analysis

[0049] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata.

[0050] 4. AI analysis on the server

[0051] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[0052] 5. Identifying fake news

[0053] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[0054] 6. Warning Display

[0055] Based on the result of the judgment received from the server, the device displays a warning to the user, which is displayed as a message saying "This may be fake news."

[0056] Specific examples of processing

[0057] Example 1: A user views a news article on a social networking site

[0058] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[0059] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[0060] The terminal transmits the acquired text content to the server.

[0061] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[0062] The server inputs the received content into the AI ​​analysis module for analysis.

[0063] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[0064] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[0065] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[0066] Example 2: Disseminating disaster information during emergencies

[0067] A user views disaster information on a social networking application.

[0068] The terminal acquires the text content of the disaster information and sends it to the server.

[0069] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[0070] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[0071] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0072] This provides a system that reduces the risk of users believing incorrect information and enables them to act based on accurate information. This system is particularly effective in times of disaster or emergency, and contributes to improving the reliability of society as a whole.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] A user installs an app on their smartphone. When the app is launched for the first time, it requests necessary permissions (such as access to the data folder of a social networking application or web browser).

[0076] Step 2:

[0077] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[0078] Step 3:

[0079] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[0080] Step 4:

[0081] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[0082] Step 5:

[0083] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[0084] Step 6:

[0085] The server passes the received information content to the analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[0086] Step 7:

[0087] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[0088] Step 8:

[0089] The server generates a judgment result and returns it to the device, which includes a flag indicating whether the news is likely to be fake.

[0090] Step 9:

[0091] The device analyzes the results received from the server. If the news is likely to be fake, the device displays a warning message to the user. The warning message clearly states, "This may be fake news."

[0092] Step 10:

[0093] Users check the warning message and reassess the reliability of the information, thereby reducing the risk of believing incorrect information.

[0094] Example 1

[0095] 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."

[0096] In modern society, fake news and false information are increasingly being spread via the Internet. This puts users at risk of believing incorrect information, which can have serious consequences, especially during emergencies and disasters. However, it is difficult for users to verify the veracity of this information themselves. There is also a need for a quick means to make decisions based on reliable information. Therefore, the present invention provides a system that analyzes the information content viewed by users in real time and displays a warning if the content is likely to be fake news. This reduces the risk of users believing incorrect information and enables them to make decisions based on accurate information.

[0097] 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.

[0098] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for comparing the information content with reliable primary information sites, means for the server to transmit the fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for monitoring temporary files of SNS applications and web browsers, and means for automatically transmitting the analyzed information content and processing it in the background. This makes it possible to quickly analyze the authenticity of information content viewed by a user in real time and issue a warning about information that is likely to be fake news.

[0099] "User" refers to a person who uses this system to view information content using a social networking application or web browser.

[0100] "Information content" refers to digital information such as text, images, and videos that users view through social networking applications or web browsers.

[0101] "Means of analysis" refers to the techniques and methods used to analyze acquired information content and understand its contents.

[0102] "Server" refers to a central processing unit that analyzes received information content and evaluates and determines whether it is fake news.

[0103] "Means for transmitting" refers to the technology or method for transmitting the analyzed information content from the terminal to the server.

[0104] "Fake news" refers to news or information that is false or misleading and does not correspond to accurate and reliable primary sources.

[0105] A "trusted primary information site" refers to a government agency, public institution, or other trusted source of information.

[0106] "Means of matching" refers to the technology or method used to compare the analyzed information content with the database of a trusted primary information site to confirm the degree of consistency.

[0107] "Determination result" refers to the evaluation result of whether the information content is fake news, obtained as a result of analysis and comparison by the server.

[0108] "Means for displaying a warning" refers to technologies and methods for notifying users that the information content they are viewing may be fake news.

[0109] "SNS Application" refers to software that provides a social networking service that enables users to share information with other users.

[0110] A "web browser" refers to software that allows users to view web pages on the Internet.

[0111] "Temporary files" refer to data files used by social networking applications and web browsers for short periods of time.

[0112] "Means processed in the background" refers to techniques and methods for automatically processing data within the system without interfering with user operations.

[0113] MODE FOR CARRYING OUT THE INVENTION

[0114] overview

[0115] This invention is a system that analyzes information content viewed by users in social media applications or web browsers in real time and displays a warning if there is a possibility of fake news. This system collates information content using a database of reliable primary information sites to identify fake news.

[0116] The user installs the app

[0117] Users install a dedicated fake news detection application on their smartphone. When the application is launched for the first time, it requests the necessary permissions to access data from social media applications and web browsers, allowing the application to access the necessary data.

[0118] The device monitors social media content

[0119] The device monitors temporary files generated by social media applications and web browsers in real time. Specifically, it monitors specific folders and cache data on the file system, and detects new files as content to be analyzed when they are created. This function detects content such as text, images, and videos viewed by the user in real time.

[0120] The device sends the content to the server

[0121] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and is uninterrupted during user operations. Specifically, metadata such as text content, URLs, and article titles are sent to the server.

[0122] Data reception on the server and AI analysis

[0123] The server receives the information content sent from the device and passes the received data to the AI ​​analysis module. During this process, the data is verified and organized. For example, invalid or incomplete data is removed and formatted for analysis. The server then uses the AI ​​analysis module to analyze the information content. A pre-trained generative AI model is used for the analysis to assess the likelihood of fake news. During this process, the reliability of the information is assessed by comparing it with a database of trusted primary information sites.

[0124] The server determines fake news

[0125] The server determines the possibility of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high possibility of fake news, it returns the result to the device as a warning notification. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[0126] The device displays a warning to the user

[0127] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user can confirm the message and reduce the risk of believing false information.

[0128] Specific examples of processing and prompt statements

[0129] Example 1: When a user browses a news article on a social networking site

[0130] When a user clicks on a news article link in a social networking application, the browser opens the page of the link.

[0131] An application on the device monitors the browser folder and automatically retrieves the text content of opened news articles.

[0132] The device sends the acquired text content and related metadata (title, URL, etc.) to the server.

[0133] The server passes the received data to the AI ​​analysis module for analysis.

[0134] Specifically, a prompt such as "Is this news true?" is fed into the generative AI model, which then matches it with matching primary information sites.

[0135] If the server determines that the news is likely fake, it sends the result of the determination and the reason (e.g., "This news article does not match the official information site") to the device.

[0136] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[0137] Example 2: Viewing disaster information during an emergency

[0138] A user views disaster information on a social networking application.

[0139] The device acquires the text content of the disaster information and related metadata and transmits it to the server.

[0140] The server passes the received information to an AI analysis module, which compares it with reliable primary information sites (e.g., the official website of the Japan Meteorological Agency or the official website of a local government).

[0141] If the server determines that the information is incorrect, it returns the determination result and reason (e.g., "Does not match official information") to the terminal.

[0142] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] Users install the fake news detection application on their smartphones and grant the necessary permissions when they first launch it, including permission to access data from social media applications and web browsers.

[0146] Input: User operation, first launch of application

[0147] Output: Application gets required permissions

[0148] Step 2:

[0149] The device monitors temporary files generated by social networking applications and web browsers in real time. This monitoring detects the information content (text, images, videos, etc.) viewed by the user. Specifically, it monitors designated folders and cache data, and retrieves the contents of new files as soon as they are created.

[0150] Input: Temporary files generated by social networking applications and web browsers

[0151] Output: Detected information content (text, images, videos, etc.)

[0152] Step 3:

[0153] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and does not interfere with the user's operations. Specifically, metadata such as text content, URLs, and article titles are formatted and sent to the server.

[0154] Input: Detected information content (text, images, videos, etc.), associated metadata (title, URL, etc.)

[0155] Output: Formatted data sent to the server

[0156] Step 4:

[0157] The server receives the information content sent from the device, verifies and organizes the data, removing invalid or incomplete data and formatting it for analysis, before passing the received data to the AI ​​analysis module.

[0158] Input: Formatted data sent from the terminal

[0159] Output: Organized data passed to the AI ​​analysis module

[0160] Step 5:

[0161] The server uses an AI analysis module to analyze the information content. It then uses a generative AI model to evaluate the reliability of the information. This analysis process involves matching the information with a database of trusted primary information sites. Specifically, a prompt such as "Is this news true?" is input into the generative AI model to calculate the degree of match.

[0162] Input: Organized data, database of reliable primary information sites

[0163] Output: AI analysis results (score, evaluation reason)

[0164] Step 6:

[0165] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, it returns the result and the reason to the device. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[0166] Input: AI analysis results (score, evaluation reason)

[0167] Output: The result of the judgment sent to the device

[0168] Step 7:

[0169] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user confirms the message, reducing the risk of believing false information.

[0170] Input: Verification result from the server

[0171] Output: A warning message that is displayed to the user.

[0172] Through the above processing steps, users can quickly analyze the authenticity of the information content they are viewing in real time and receive warnings about information that is likely to be fake news.

[0173] (Application example 1)

[0174] 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."

[0175] Traditionally, a lot of information has been distributed through social media applications and web browsers, and this information often contains fake news. This fake news can mislead and confuse users. Furthermore, in emergencies and disasters, the rapid spread of false information can have a significant impact on society as a whole. Conventional fake news countermeasures have tended to be reactive, making it difficult to provide users with fake news warnings in real time. Therefore, there is a need for a system that can analyze the information content viewed by users and warn them of possible fake news in real time.

[0176] 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.

[0177] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for the server to transmit a fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for referencing a database of reliable information sources and comparing the information content, means for using a generative AI model for fake news determination, means for generating a prompt sentence for the generative AI model, means for the terminal to obtain temporary files from a monitored application, and means for transmitting and analyzing data in the background. This enables accurate analysis in real time and enables immediate evaluation of the reliability of the information viewed by the user.

[0178] "Information content viewed by users" refers to digital information such as articles, posts, images, and videos that users view through social media applications or web browsers.

[0179] "Analysis tools" refers to the software or algorithms used to analyze information content and understand its content and structure.

[0180] "Means for transmitting to the server" refers to the network communication protocol or interface for transferring data from the user's terminal to the server.

[0181] "Measures for analyzing potential fake news" refers to algorithms or AI models used to assess whether information content is false or not.

[0182] "Means for transmitting fake news determination results to the terminal" refers to a communication protocol for informing the terminal of the analysis results from the server.

[0183] "Means for displaying a warning to the user" refers to an interface or notification system for displaying a warning message to the user when there is a high possibility of fake news.

[0184] A "database of trusted sources" is a database that compiles information from official institutions and reliable information sources.

[0185] A "generative AI model" is an AI model that is trained using large datasets and optimized to perform a specific task.

[0186] A "prompt" is an input text given to a generative AI model to perform a specific analysis or judgment.

[0187] "Temporary files" are temporary data files generated while using a social networking application or web browser.

[0188] "Means for transmitting and analyzing data in the background" refers to a system or process that automatically transmits and analyzes data in the background while the user is working.

[0189] System Overview

[0190] The present invention is a system that monitors information content viewed by users through social media applications and web browsers in real time and displays a warning when there is a high possibility that the content is fake news. The system has the following main functions:

[0191] 1. User installs the app

[0192] The user installs a dedicated application on their smartphone, and upon first launch, the application requests necessary permissions, including permission to access data from social networking applications and web browsers.

[0193] 2. Acquiring and monitoring information content

[0194] The user's device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0195] 3. Data transmission

[0196] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background and does not interfere with the user's operations. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[0197] 4. Analysis on the server

[0198] The server analyzes the received information content using a generative AI model, which assesses the reliability of the information by checking it against a database of pre-trusted sources.

[0199] 5. Fake news detection and notification

[0200] If the server determines that the news is likely fake, it sends the result to the device, which then displays a warning message to the user, stating, "This may be fake news."

[0201] Hardware and Software Used

[0202] Hardware: Smartphone (Android or iOS)

[0203] Software: Python, TensorFlow, Firebase

[0204] Database: A database of trusted sources

[0205] Data processing and calculation

[0206] The server analyzes the information content sent by the user in the following steps and generates an appropriate warning.

[0207] 1. Data Acquisition:

[0208] Monitors temporary files from social media applications and web browsers and captures text data and metadata.

[0209] 2. Data transmission and analysis:

[0210] Send the acquired data to the server (Firebase).

[0211] The data is analyzed using an AI analysis module (using TensorFlow) on the server and compared with a database of trusted information sources.

[0212] 3. Sending the results and warnings:

[0213] If there is a high possibility that the news is fake, the analysis results are sent to the user's device and a warning message is displayed on the device.

[0214] Specific examples

[0215] For example, if a user is viewing a news article on social media that says "A large typhoon will hit tonight," they would follow these steps:

[0216] The application monitors the text of news articles and sends it to a server.

[0217] The AI ​​model on the server analyzes the news article and compares it with information from trusted sources (e.g., meteorological agencies).

[0218] If the app determines that the news is likely to be fake, it will warn the user, saying, "This may be fake news."

[0219] Prompt Sentence Examples

[0220] content = "A large typhoon will hit tonight"

[0221] metadata = {"title": "Typhoon News", "url": "example.com / typhoon_news"}

[0222] result = check_fakenews(content, metadata)

[0223] This sentence shows an example of inputting the text and metadata of a news article into an AI model, and then determining whether it is fake news based on the output.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] A user views information content through a social networking application or a web browser.

[0227] Input: Information content (news articles, posts, images, videos, etc.)

[0228] Output: None

[0229] How it works: When a user browses information using a social networking application or web browser on their smartphone, the content of this information is monitored by the application.

[0230] Step 2:

[0231] The device monitors temporary files of social networking applications and web browsers and obtains text data and metadata of information content.

[0232] Input: Temporary file (text data, article title, URL, etc.)

[0233] Output: Retrieved information content and metadata

[0234] How it works: An application on the device monitors temporary files in real time and automatically extracts text data and related metadata from the information content viewed.

[0235] Step 3:

[0236] The terminal transmits the acquired information content and metadata to the server.

[0237] Input: Information content and metadata

[0238] Output: Send data to the server

[0239] How it works: The device sends information content and metadata to the server in the background, without interfering with the user's operations.

[0240] Step 4:

[0241] The server analyzes the received information content using a generative AI model.

[0242] Input: Submitted information content and metadata

[0243] Output: Analysis results (possibility of fake news)

[0244] How it works: The server inputs the received data into an AI analysis module (using TensorFlow), which evaluates the reliability of the information based on a generative AI model, compares it with a database of trusted sources, and calculates the likelihood of it being fake news.

[0245] Step 5:

[0246] The server transmits the determination result to the user's terminal.

[0247] Input: Analysis results (possible fake news)

[0248] Output: Send the result to the user's device

[0249] How it works: Based on the results of AI analysis, the server sends the fake news judgment result to the device, and this data is immediately notified to the user.

[0250] Step 6:

[0251] The terminal displays a warning message to the user based on the determination result received from the server.

[0252] Input: Verification result (high probability of fake news)

[0253] Output: Display a warning message

[0254] Specific operation: The device displays the warning message received from the server to the user, informing them of the warning content, such as "This may be fake news."

[0255] Operation in a specific example

[0256] For example, if a user sees a news article on social media that says, "A large typhoon will hit tonight,"

[0257] Step 1: User browses to a news article.

[0258] Step 2: The device retrieves the text data, article title, and URL.

[0259] Step 3: The device sends this data to the server.

[0260] Step 4: The server uses the generative AI model to analyze the news article and assess its likelihood of being fake news.

[0261] Step 5: The server determines that the news is likely fake and sends the result to the device.

[0262] Step 6: The device displays a warning message to alert the user.

[0263] 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.

[0264] overview

[0265] The present invention is a system that analyzes information content viewed by users in social networking applications or web browsers in real time and displays a warning when there is a possibility of fake news. It also combines an emotion engine that recognizes the user's emotions and adjusts the strength of the warning based on the user's emotions.

[0266] Specific Embodiments

[0267] 1. User installs the app

[0268] The user installs a dedicated application on their smartphone. When the application is launched for the first time, it requests necessary permissions (permission to access data folders of social networking applications and web browsers, and permission to use the emotion engine).

[0269] 2. Monitoring social media content

[0270] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0271] 3. Sending to the server for content analysis

[0272] The device transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[0273] 4. AI analysis on the server

[0274] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[0275] 5. Identifying fake news

[0276] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[0277] 6. Emotion Recognition by Emotion Engine

[0278] The device uses an emotion engine to recognize the user's emotions in real time, for example, by analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone to evaluate the user's emotional state.

[0279] 7. Adjusting warnings based on emotions

[0280] The device adjusts the intensity of the warning based on the emotion data from the emotion engine. For example, if the user is feeling anxious or stressed, the device displays a softer warning message. Conversely, if the user is calm, the device displays a normal warning message.

[0281] 8. Warning Display

[0282] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[0283] Specific examples of processing

[0284] Example 1: A user views a news article on a social networking site

[0285] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[0286] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[0287] The terminal transmits the acquired text content to the server.

[0288] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[0289] The server inputs the received content into the AI ​​analysis module for analysis.

[0290] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[0291] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[0292] The device uses an emotion engine to recognize the user's emotions, for example, assessing stress or relief from the user's facial expressions and tone of voice.

[0293] Based on the received judgment results and the evaluation results of the emotion engine, the device displays a warning message to the user saying, "This may be fake news."

[0294] If the user is feeling stressed, the message is displayed softly.

[0295] If the user is sober, the warning is displayed in the normal format.

[0296] Example 2: Disseminating disaster information during emergencies

[0297] A user views disaster information on a social networking application.

[0298] The terminal acquires the text content of the disaster information and sends it to the server.

[0299] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[0300] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[0301] The device recognizes the user's emotions and adjusts warning messages based on their state.

[0302] For example, in an emergency, if the user is already panicking, the warning will be calming.

[0303] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0304] This allows the user to reduce the risk of believing false information and act based on accurate information. Furthermore, by combining it with an emotion engine, it is possible to provide appropriate warnings tailored to the user's emotional state and adjust how information is received.

[0305] The processing flow will be explained below.

[0306] Step 1:

[0307] The user installs the app on their smartphone. When the app is launched for the first time, it requests the necessary permissions (permission to access the data folder of the social networking application and web browser, and permission to use the emotion engine).

[0308] Step 2:

[0309] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[0310] Step 3:

[0311] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[0312] Step 4:

[0313] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[0314] Step 5:

[0315] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[0316] Step 6:

[0317] The server passes the received information content to an AI analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[0318] Step 7:

[0319] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[0320] Step 8:

[0321] The server generates a judgment result and sends it to the device, which includes a flag indicating the possibility of fake news.

[0322] Step 9:

[0323] The device uses an emotion engine to recognize the user's emotions in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to assess the user's emotional state.

[0324] Step 10:

[0325] Based on the judgment results received by the device and the evaluation results of the emotion engine, the warning is displayed with appropriate intensity. For example, if the user is feeling stressed, the warning message is displayed softly. If the user is calm, the normal warning message is displayed.

[0326] Step 11:

[0327] The device displays a tailored warning message to the user, including the message "This may be fake news," but presented differently depending on the user's emotional state.

[0328] Step 12:

[0329] Users can check the warning message and reassess the reliability of the information, reducing the risk of believing false information. Furthermore, alerts tailored to the user's emotional state optimize how they receive information.

[0330] Example 2

[0331] 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."

[0332] The purpose of this invention is to detect fake news contained in information viewed by users on the Internet in real time and issue appropriate warnings. Furthermore, by providing flexible warning messages that are tailored to the user's emotional state, the problem of providing truthful information while reducing user stress is solved.

[0333] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an AI analysis means, a means for maintaining a database of reliable information sources, and a means for collating information content. This makes it possible to analyze the reliability of information content viewed by a user in real time, identify information that is likely to be fake news, and further adjust the strength of a warning message based on the emotional state of the user.

[0334] "User" means an individual or end user who uses the System.

[0335] A "terminal" is a device used by a user, and includes a smartphone, tablet, PC, etc.

[0336] "Information content" refers to data such as text, images, and videos that users view via the Internet.

[0337] The "means for analyzing in real time" is a function or device for instantly analyzing the information content that the user is viewing on the spot.

[0338] A "server" is a computer system on a network for analyzing and processing information.

[0339] "AI analysis" is an analytical method that uses artificial intelligence technology to evaluate the reliability of information content.

[0340] "Reliable sources" refer to highly accurate information provided by public institutions and authoritative media.

[0341] A "database" is a collection of information that is systematically stored and made easily accessible and collated.

[0342] An "emotion engine" is software or an algorithm that recognizes and evaluates a user's emotional state from facial expressions, tone of voice, etc.

[0343] The "means for adjusting the intensity of the warning" is a function or device for changing the content and expression of the warning message according to the emotional state of the user.

[0344] MODE FOR CARRYING OUT THE INVENTION

[0345] overview

[0346] This system analyzes the information content a user browses online in real time to detect possible fake news. It also recognizes the user's emotional state and adjusts the intensity of warning messages based on that emotion. The system aims to provide accurate information while reducing user stress.

[0347] User installs the app

[0348] Users install a dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. This allows the application to access the necessary data.

[0349] Social media content monitoring

[0350] The device monitors temporary files generated by social media applications and web browsers by periodically checking the data folder for new or updated files and retrieving their contents.

[0351] Send to server for content analysis

[0352] The device encrypts the acquired information content and sends it to the server. The data sent includes the text content of the information being viewed and related metadata (e.g., article title and URL). This allows data to be sent in the background without the user's knowledge.

[0353] AI analysis on the server

[0354] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news, which uses a pre-trained generative AI model to compare the received content with a database of trusted sources to calculate the probability of it being fake news.

[0355] Fake news detection

[0356] The server determines the likelihood that the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined that the information content is likely to be fake news, the server returns the determination result to the device.

[0357] Emotion recognition by emotion engine

[0358] The device activates an emotion engine to recognize the user's emotions in real time. Specifically, it analyzes the user's facial expressions and tone of voice via the smartphone's camera and microphone to assess their emotional state.

[0359] Adjusting alerts based on emotion

[0360] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user feels anxious or stressed, the device displays the warning message in a softer tone, and if the user feels calm, the device displays the warning message in a normal tone.

[0361] Displaying warnings

[0362] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[0363] Specific examples

[0364] An example of a specific prompt is as follows:

[0365] "This system analyzes information content viewed on social media and web browsers in real time and displays a warning to users if it detects possible fake news. It also recognizes the user's emotional state and adjusts the strength of the warning based on their emotions. Specifically, it monitors the data folders of social media applications and web browsers, sends the detected content to a server, and an AI analysis module evaluates the possibility of it being fake news. It then adjusts and displays a warning message according to the user's emotional state."

[0366] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0367] Step 1:

[0368] The user installs the dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. Once the user grants these permissions, the application is able to access the necessary data.

[0369] Input: User installation operations, access permissions

[0370] Output: App initial setup complete, necessary permissions obtained

[0371] Step 2:

[0372] The device monitors temporary files generated by social media applications and web browsers. Applications periodically check the data folder for new or updated files and retrieve their contents. For example, the monitoring software may scan the data folder every second.

[0373] Input: Temporary files from social networking applications and web browsers

[0374] Output: New information content detected (text, images, videos, etc.)

[0375] Step 3:

[0376] The device then transmits the retrieved information content to the server. This transmission process occurs automatically in the background, and the transmitted data includes the text content of the information and associated metadata (e.g., article title and URL). Specifically, the device encrypts the data and transmits it via a secure communications protocol.

[0377] Input: Detected information content

[0378] Output: The encrypted information content is sent to the server

[0379] Step 4:

[0380] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses a pre-trained generative AI model to compare it with a database of trusted sources. Specifically, the analysis module runs an algorithm to calculate the reliability of the information.

[0381] Input: Encrypted information content

[0382] Output: Assessment result on the likelihood of fake news

[0383] Step 5:

[0384] The server determines whether the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined to be fake news, the server sends the result of that judgment to the device. Specifically, the server analyzes the evaluation results and generates an appropriate warning message.

[0385] Input: Evaluation results from the AI ​​analysis module

[0386] Output: Fake news detection result, warning message

[0387] Step 6:

[0388] The device activates an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through the smartphone's camera and microphone to assess their emotional state. Specifically, the emotion recognition software uses image processing and voice analysis algorithms.

[0389] Input: User's facial expressions, tone of voice

[0390] Output: User's emotional data (stress, relief, etc.)

[0391] Step 7:

[0392] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user is feeling anxious or stressed, the warning message is displayed in a softer tone, and if the user is calm, the warning message is displayed in a normal format. Specifically, the device dynamically changes the content and expression of the warning message based on the emotion data.

[0393] Input: User emotion data, warning message

[0394] Output: Adjusted warning message

[0395] Step 8:

[0396] The device then displays a tailored warning message to the user, which includes the message "This may be fake news," but is presented differently depending on the user's emotional state. Specifically, the message appears on the screen for the user to immediately acknowledge.

[0397] Input: Adjusted warning message

[0398] Output: The warning message displayed to the user.

[0399] (Application example 2)

[0400] 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."

[0401] In today's information society, there is a high possibility that fake news is included in the information content that users visually view, and there are risks associated with believing and acting on that information. Furthermore, viewing fake news while in an emotionally unstable state can have a particularly significant impact on users. Therefore, a system is needed that analyzes the content viewed by users in real time and issues appropriate warnings when there is a possibility of fake news. Furthermore, it is necessary to ensure that warnings are appropriately accepted by adjusting the wording of the warning according to the user's emotional state.

[0402] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing information content visually viewed by the user, means for transmitting the analyzed information content to the server, and means for the server to analyze the possibility of fake news based on the information content. This makes it possible to analyze the content viewed by the user in real time and display a warning if there is a high possibility that it is fake news. In addition, the display device includes means for recognizing the user's emotions using an emotion engine and means for adjusting the strength of the warning based on the user's emotions, making it possible to issue an appropriate warning according to the user's emotional state.

[0403] "User" refers to a person who uses this system.

[0404] "Visually viewed information content" refers to digital information such as text, images, and videos that can be visually viewed by a user.

[0405] "Means for analyzing" refers to a method or apparatus for analyzing visually viewed information content.

[0406] "Server" refers to a computer system capable of receiving analyzed information content and transmitting the results of the analysis.

[0407] "Means for sending" refers to a method or apparatus for sending the parsed information content to a server.

[0408] "Fake news" refers to news that contains intentionally false information.

[0409] "Decision result" refers to the evaluation result after the server analyzes the possibility of fake news.

[0410] "Display device" refers to a device for visually displaying analysis results and warning messages to a user.

[0411] "Means for displaying a warning" refers to a method or device for warning users of possible fake news.

[0412] An "emotion engine" refers to an algorithm or software for analyzing a user's emotions.

[0413] "Means for recognizing emotions" refers to a method or device for detecting the emotional state of a user.

[0414] "Means for adjusting the intensity of an alert" refers to a method or apparatus for adjusting the wording or content of an alert based on the user's emotional state.

[0415] A "reliable source" is a source that provides accurate and reliable information.

[0416] "Source database" means a digital database for storing information collected from reliable sources.

[0417] "Means for matching" refers to a method or device for comparing the analyzed information content with a database of information sources to identify matches and discrepancies.

[0418] "Temporary files" refer to data files that are temporarily stored and used by social networking applications and web browsers.

[0419] "Means for monitoring" refers to a method or device for periodically checking the contents of temporary files and extracting necessary information.

[0420] "Optical character recognition" refers to techniques and devices for extracting text information from image data.

[0421] This invention is a system that analyzes information content visually viewed by users in real time and displays a warning when there is a possibility of fake news. In particular, it combines a function that recognizes the user's emotions using an emotion engine and adjusts the strength of the warning.

[0422] Hardware and Software Examples

[0423] Hardware

[0424] Smart Glasses: As an example, we use general-purpose smart glasses, which have a camera to capture the information content that the user visually views, and a built-in microphone to recognize the user's facial expressions.

[0425] Server: Uses sophisticated computer systems to analyze information content and assess potential fake news.

[0426] software

[0427] OCR library: Using Tesseract OCR as an example, we extract text information from image data captured by the camera in smart glasses.

[0428] Network library: Using Retrofit as an example, we send parsed text data to a server.

[0429] Emotion Recognition Library: As an example, we will use the Affectiva SDK to analyze emotions from the user's facial expressions and tone of voice.

[0430] AI analysis module: Evaluates the reliability of information content using a server-side built-in AI model, which includes pre-trained generative AI.

[0431] Process Overview

[0432] 1. Information content analysis

[0433] The camera in the smart glasses captures the information content that the user is viewing, converts the image data into text data using an OCR library, and then transmits the text data to a server using a network library.

[0434] 2. Analysis on the server

[0435] The server passes the received text data to an AI analysis module to assess the likelihood of it being fake news, comparing it with a database of trusted sources. The AI ​​analysis module uses a trained model to assess the reliability of the information and obtains a verdict.

[0436] 3. Emotional awareness and alert regulation

[0437] The server sends the result of the assessment back to the smart glasses. At the same time, the smart glasses' microphone and camera capture the user's facial expressions and tone of voice, and an emotion recognition library is used to analyze the user's emotions. The strength of the warning is adjusted based on this emotional data. For example, if the user is feeling anxious or stressed, the warning will be displayed in a more gentle manner, and if the user is calm, the warning will be displayed in a more normal manner.

[0438] 4. Warning Display

[0439] The smart glasses display a warning message saying "This may be fake news." The display format of the warning message is adjusted according to the user's emotional state.

[0440] Examples of concrete examples and prompts

[0441] Specific examples

[0442] When a user is wearing smart glasses and browsing a news site, the following happens:

[0443] 1. The displayed content of a news article is captured by the camera in the smart glasses.

[0444] 2. The captured image is converted to text using an OCR library.

[0445] 3. The converted text data is sent to a server, where an AI analysis module evaluates the likelihood of it being fake news.

[0446] 4. The smart glasses' camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the user's emotions.

[0447] 5. Based on the fake news judgment results and emotional data, a warning message will be displayed on the smart glasses display.

[0448] Prompt Sentence Examples

[0449] Use the following prompt to ask the generative AI model to analyze a news article:

[0450] Rate the following news articles as to whether they are fake news or not.

[0451] News article title: [Title]

[0452] News article content: [Content]

[0453] Comparison with reliable primary information sites: [Comparison results]

[0454] Please assess the possibility of fake news based on the verification results.

[0455] Using this prompt, the generative AI model can analyze news articles and determine whether they are fake news.

[0456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0457] Step 1:

[0458] A user wears smart glasses and visually views information content. The camera in the smart glasses captures the information content the user is viewing. The input at this stage is the image data captured by the camera, and the output is the image data.

[0459] Step 2:

[0460] The device uses an OCR library (e.g., Tesseract OCR) to convert the captured image into text data. The input is the captured image data, and the output is the text data extracted from the image. Specifically, the OCR library detects each character or word and converts it into text format.

[0461] Step 3:

[0462] The device uses a network library (e.g., Retrofit) to send the parsed text data to the server. The input is the text data obtained by OCR, and the output is the text data sent to the server. Specifically, the network library generates an HTTP request and sends the text data as a payload to the server.

[0463] Step 4:

[0464] The server passes the received text data to an AI analysis module (e.g., a generative AI model) to evaluate the likelihood of it being fake news. The input is the text data sent to the server, and the output is the result of the fake news judgment. Specifically, the generative AI model refers to the data it has been trained on and compares it with reliable sources to calculate the probability. The following prompt sentences are used:

[0465] Rate the following news articles as to whether they are fake news or not.

[0466] News article title: [Title]

[0467] News article content: [Content]

[0468] Comparison with reliable primary information sites: [Comparison results]

[0469] Please assess the possibility of fake news based on the verification results.

[0470] Step 5:

[0471] The server sends the fake news judgment result to the terminal. The input is the judgment result from the AI ​​analysis module, and the output is the judgment result sent to the terminal. Specifically, the server returns the judgment result to the terminal as an HTTP response.

[0472] Step 6:

[0473] The device uses the camera and microphone of the smart glasses to capture the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[0474] Step 7:

[0475] The device uses an emotion recognition library (e.g., Affectiva SDK) to recognize the user's emotions from the captured facial and voice data. The input is the captured facial and voice data, and the output is the analyzed emotion data. Specifically, the emotion recognition library uses a facial expression analysis algorithm to evaluate the user's emotional state.

[0476] Step 8:

[0477] The device adjusts the strength of the warning based on the fake news judgment result and emotional data. The input is the judgment result and emotional data, and the output is a warning message with adjusted strength. Specifically, the emotion engine changes the tone and strength of the warning depending on the user's stress level.

[0478] Step 9:

[0479] The terminal displays a warning message on the display of the smart glasses. The input is a warning message with adjusted intensity, and the output is a visually displayed warning message. Specifically, the warning "This may be fake news" is displayed on the display in a softer or normal form depending on the user's emotion.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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."

[0496] overview

[0497] This system analyzes information content viewed by users on social media applications or web browsers in real time and displays a warning if the content is suspected to be fake news. In particular, it uses a database of trusted primary information sites to collate the information content and identify fake news.

[0498] Specific Embodiments

[0499] 1. User installs the app

[0500] Users install a dedicated application on their smartphone, and when they launch the app for the first time, it requests necessary permissions, including permission to access data from social networking applications and web browsers.

[0501] 2. Monitoring social media content

[0502] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0503] 3. Sending to the server for content analysis

[0504] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata.

[0505] 4. AI analysis on the server

[0506] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[0507] 5. Identifying fake news

[0508] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[0509] 6. Warning Display

[0510] Based on the result of the judgment received from the server, the device displays a warning to the user, which is displayed as a message saying "This may be fake news."

[0511] Specific examples of processing

[0512] Example 1: A user views a news article on a social networking site

[0513] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[0514] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[0515] The terminal transmits the acquired text content to the server.

[0516] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[0517] The server inputs the received content into the AI ​​analysis module for analysis.

[0518] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[0519] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[0520] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[0521] Example 2: Disseminating disaster information during emergencies

[0522] A user views disaster information on a social networking application.

[0523] The terminal acquires the text content of the disaster information and sends it to the server.

[0524] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[0525] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[0526] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0527] This provides a system that reduces the risk of users believing incorrect information and enables them to act based on accurate information. This system is particularly effective in times of disaster or emergency, and contributes to improving the reliability of society as a whole.

[0528] The processing flow will be explained below.

[0529] Step 1:

[0530] A user installs an app on their smartphone. When the app is launched for the first time, it requests necessary permissions (such as access to the data folder of a social networking application or web browser).

[0531] Step 2:

[0532] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[0533] Step 3:

[0534] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[0535] Step 4:

[0536] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[0537] Step 5:

[0538] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[0539] Step 6:

[0540] The server passes the received information content to the analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[0541] Step 7:

[0542] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[0543] Step 8:

[0544] The server generates a judgment result and returns it to the device, which includes a flag indicating whether the news is likely to be fake.

[0545] Step 9:

[0546] The device analyzes the results received from the server. If the news is likely to be fake, the device displays a warning message to the user. The warning message clearly states, "This may be fake news."

[0547] Step 10:

[0548] Users check the warning message and reassess the reliability of the information, thereby reducing the risk of believing incorrect information.

[0549] Example 1

[0550] 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."

[0551] In modern society, fake news and false information are increasingly being spread via the Internet. This puts users at risk of believing incorrect information, which can have serious consequences, especially during emergencies and disasters. However, it is difficult for users to verify the veracity of this information themselves. There is also a need for a quick means to make decisions based on reliable information. Therefore, the present invention provides a system that analyzes the information content viewed by users in real time and displays a warning if the content is likely to be fake news. This reduces the risk of users believing incorrect information and enables them to make decisions based on accurate information.

[0552] 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.

[0553] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for comparing the information content with reliable primary information sites, means for the server to transmit the fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for monitoring temporary files of SNS applications and web browsers, and means for automatically transmitting the analyzed information content and processing it in the background. This makes it possible to quickly analyze the authenticity of information content viewed by a user in real time and issue a warning about information that is likely to be fake news.

[0554] "User" refers to a person who uses this system to view information content using a social networking application or web browser.

[0555] "Information content" refers to digital information such as text, images, and videos that users view through social networking applications or web browsers.

[0556] "Means of analysis" refers to the techniques and methods used to analyze acquired information content and understand its contents.

[0557] "Server" refers to a central processing unit that analyzes received information content and evaluates and determines whether it is fake news.

[0558] "Means for transmitting" refers to the technology or method for transmitting the analyzed information content from the terminal to the server.

[0559] "Fake news" refers to news or information that is false or misleading and does not correspond to accurate and reliable primary sources.

[0560] A "trusted primary information site" refers to a government agency, public institution, or other trusted source of information.

[0561] "Means of matching" refers to the technology or method used to compare the analyzed information content with the database of a trusted primary information site to confirm the degree of consistency.

[0562] "Determination result" refers to the evaluation result of whether the information content is fake news, obtained as a result of analysis and comparison by the server.

[0563] "Means for displaying a warning" refers to technologies and methods for notifying users that the information content they are viewing may be fake news.

[0564] "SNS Application" refers to software that provides a social networking service that enables users to share information with other users.

[0565] A "web browser" refers to software that allows users to view web pages on the Internet.

[0566] "Temporary files" refer to data files used by social networking applications and web browsers for short periods of time.

[0567] "Means processed in the background" refers to techniques and methods for automatically processing data within the system without interfering with user operations.

[0568] MODE FOR CARRYING OUT THE INVENTION

[0569] overview

[0570] This invention is a system that analyzes information content viewed by users in social media applications or web browsers in real time and displays a warning if there is a possibility of fake news. This system collates information content using a database of reliable primary information sites to identify fake news.

[0571] The user installs the app

[0572] Users install a dedicated fake news detection application on their smartphone. When the application is launched for the first time, it requests the necessary permissions to access data from social media applications and web browsers, allowing the application to access the necessary data.

[0573] The device monitors social media content

[0574] The device monitors temporary files generated by social media applications and web browsers in real time. Specifically, it monitors specific folders and cache data on the file system, and detects new files as content to be analyzed when they are created. This function detects content such as text, images, and videos viewed by the user in real time.

[0575] The device sends the content to the server

[0576] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and is uninterrupted during user operations. Specifically, metadata such as text content, URLs, and article titles are sent to the server.

[0577] Data reception on the server and AI analysis

[0578] The server receives the information content sent from the device and passes the received data to the AI ​​analysis module. During this process, the data is verified and organized. For example, invalid or incomplete data is removed and formatted for analysis. The server then uses the AI ​​analysis module to analyze the information content. A pre-trained generative AI model is used for the analysis to assess the likelihood of fake news. During this process, the reliability of the information is assessed by comparing it with a database of trusted primary information sites.

[0579] The server determines fake news

[0580] The server determines the possibility of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high possibility of fake news, it returns the result to the device as a warning notification. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[0581] The device displays a warning to the user

[0582] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user can confirm the message and reduce the risk of believing false information.

[0583] Specific examples of processing and prompt statements

[0584] Example 1: When a user browses a news article on a social networking site

[0585] When a user clicks on a news article link in a social networking application, the browser opens the page of the link.

[0586] An application on the device monitors the browser folder and automatically retrieves the text content of opened news articles.

[0587] The device sends the acquired text content and related metadata (title, URL, etc.) to the server.

[0588] The server passes the received data to the AI ​​analysis module for analysis.

[0589] Specifically, a prompt such as "Is this news true?" is fed into the generative AI model, which then matches it with matching primary information sites.

[0590] If the server determines that the news is likely fake, it sends the result of the determination and the reason (e.g., "This news article does not match the official information site") to the device.

[0591] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[0592] Example 2: Viewing disaster information during an emergency

[0593] A user views disaster information on a social networking application.

[0594] The device acquires the text content of the disaster information and related metadata and transmits it to the server.

[0595] The server passes the received information to an AI analysis module, which compares it with reliable primary information sites (e.g., the official website of the Japan Meteorological Agency or the official website of a local government).

[0596] If the server determines that the information is incorrect, it returns the determination result and reason (e.g., "Does not match official information") to the terminal.

[0597] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0598] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0599] Step 1:

[0600] Users install the fake news detection application on their smartphones and grant the necessary permissions when they first launch it, including permission to access data from social media applications and web browsers.

[0601] Input: User operation, first launch of application

[0602] Output: Application gets required permissions

[0603] Step 2:

[0604] The device monitors temporary files generated by social networking applications and web browsers in real time. This monitoring detects the information content (text, images, videos, etc.) viewed by the user. Specifically, it monitors designated folders and cache data, and retrieves the contents of new files as soon as they are created.

[0605] Input: Temporary files generated by social networking applications and web browsers

[0606] Output: Detected information content (text, images, videos, etc.)

[0607] Step 3:

[0608] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and does not interfere with the user's operations. Specifically, metadata such as text content, URLs, and article titles are formatted and sent to the server.

[0609] Input: Detected information content (text, images, videos, etc.), associated metadata (title, URL, etc.)

[0610] Output: Formatted data sent to the server

[0611] Step 4:

[0612] The server receives the information content sent from the device, verifies and organizes the data, removing invalid or incomplete data and formatting it for analysis, before passing the received data to the AI ​​analysis module.

[0613] Input: Formatted data sent from the terminal

[0614] Output: Organized data passed to the AI ​​analysis module

[0615] Step 5:

[0616] The server uses an AI analysis module to analyze the information content. It then uses a generative AI model to evaluate the reliability of the information. This analysis process involves matching the information with a database of trusted primary information sites. Specifically, a prompt such as "Is this news true?" is input into the generative AI model to calculate the degree of match.

[0617] Input: Organized data, database of reliable primary information sites

[0618] Output: AI analysis results (score, evaluation reason)

[0619] Step 6:

[0620] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, it returns the result and the reason to the device. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[0621] Input: AI analysis results (score, evaluation reason)

[0622] Output: The result of the judgment sent to the device

[0623] Step 7:

[0624] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user confirms the message, reducing the risk of believing false information.

[0625] Input: Verification result from the server

[0626] Output: A warning message that is displayed to the user.

[0627] Through the above processing steps, users can quickly analyze the authenticity of the information content they are viewing in real time and receive warnings about information that is likely to be fake news.

[0628] (Application example 1)

[0629] 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."

[0630] Traditionally, a lot of information has been distributed through social media applications and web browsers, and this information often contains fake news. This fake news can mislead and confuse users. Furthermore, in emergencies and disasters, the rapid spread of false information can have a significant impact on society as a whole. Conventional fake news countermeasures have tended to be reactive, making it difficult to provide users with fake news warnings in real time. Therefore, there is a need for a system that can analyze the information content viewed by users and warn them of possible fake news in real time.

[0631] 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.

[0632] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for the server to transmit a fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for referencing a database of reliable information sources and comparing the information content, means for using a generative AI model for fake news determination, means for generating a prompt sentence for the generative AI model, means for the terminal to obtain temporary files from a monitored application, and means for transmitting and analyzing data in the background. This enables accurate analysis in real time and enables immediate evaluation of the reliability of the information viewed by the user.

[0633] "Information content viewed by users" refers to digital information such as articles, posts, images, and videos that users view through social media applications or web browsers.

[0634] "Analysis tools" refers to the software or algorithms used to analyze information content and understand its content and structure.

[0635] "Means for transmitting to the server" refers to the network communication protocol or interface for transferring data from the user's terminal to the server.

[0636] "Measures for analyzing potential fake news" refers to algorithms or AI models used to assess whether information content is false or not.

[0637] "Means for transmitting fake news determination results to the terminal" refers to a communication protocol for informing the terminal of the analysis results from the server.

[0638] "Means for displaying a warning to the user" refers to an interface or notification system for displaying a warning message to the user when there is a high possibility of fake news.

[0639] A "database of trusted sources" is a database that compiles information from official institutions and reliable information sources.

[0640] A "generative AI model" is an AI model that is trained using large datasets and optimized to perform a specific task.

[0641] A "prompt" is an input text given to a generative AI model to perform a specific analysis or judgment.

[0642] "Temporary files" are temporary data files generated while using a social networking application or web browser.

[0643] "Means for transmitting and analyzing data in the background" refers to a system or process that automatically transmits and analyzes data in the background while the user is working.

[0644] System Overview

[0645] The present invention is a system that monitors information content viewed by users through social media applications and web browsers in real time and displays a warning when there is a high possibility that the content is fake news. The system has the following main functions:

[0646] 1. User installs the app

[0647] The user installs a dedicated application on their smartphone, and upon first launch, the application requests necessary permissions, including permission to access data from social networking applications and web browsers.

[0648] 2. Acquiring and monitoring information content

[0649] The user's device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0650] 3. Data transmission

[0651] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background and does not interfere with the user's operations. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[0652] 4. Analysis on the server

[0653] The server analyzes the received information content using a generative AI model, which assesses the reliability of the information by checking it against a database of pre-trusted sources.

[0654] 5. Fake news detection and notification

[0655] If the server determines that the news is likely fake, it sends the result to the device, which then displays a warning message to the user, stating, "This may be fake news."

[0656] Hardware and Software Used

[0657] Hardware: Smartphone (Android or iOS)

[0658] Software: Python, TensorFlow, Firebase

[0659] Database: A database of trusted sources

[0660] Data processing and calculation

[0661] The server analyzes the information content sent by the user in the following steps and generates an appropriate warning.

[0662] 1. Data Acquisition:

[0663] Monitors temporary files from social media applications and web browsers and captures text data and metadata.

[0664] 2. Data transmission and analysis:

[0665] Send the acquired data to the server (Firebase).

[0666] The data is analyzed using an AI analysis module (using TensorFlow) on the server and compared with a database of trusted information sources.

[0667] 3. Sending the results and warnings:

[0668] If there is a high possibility that the news is fake, the analysis results are sent to the user's device and a warning message is displayed on the device.

[0669] Specific examples

[0670] For example, if a user is viewing a news article on social media that says "A large typhoon will hit tonight," they would follow these steps:

[0671] The application monitors the text of news articles and sends it to a server.

[0672] The AI ​​model on the server analyzes the news article and compares it with information from trusted sources (e.g., meteorological agencies).

[0673] If the app determines that the news is likely to be fake, it will warn the user, saying, "This may be fake news."

[0674] Prompt Sentence Examples

[0675] content = "A large typhoon will hit tonight"

[0676] metadata = {"title": "Typhoon News", "url": "example.com / typhoon_news"}

[0677] result = check_fakenews(content, metadata)

[0678] This sentence shows an example of inputting the text and metadata of a news article into an AI model, and then determining whether it is fake news based on the output.

[0679] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0680] Step 1:

[0681] A user views information content through a social networking application or a web browser.

[0682] Input: Information content (news articles, posts, images, videos, etc.)

[0683] Output: None

[0684] How it works: When a user browses information using a social networking application or web browser on their smartphone, the content of this information is monitored by the application.

[0685] Step 2:

[0686] The device monitors temporary files of social networking applications and web browsers and obtains text data and metadata of information content.

[0687] Input: Temporary file (text data, article title, URL, etc.)

[0688] Output: Retrieved information content and metadata

[0689] How it works: An application on the device monitors temporary files in real time and automatically extracts text data and related metadata from the information content viewed.

[0690] Step 3:

[0691] The terminal transmits the acquired information content and metadata to the server.

[0692] Input: Information content and metadata

[0693] Output: Send data to the server

[0694] How it works: The device sends information content and metadata to the server in the background, without interfering with the user's operations.

[0695] Step 4:

[0696] The server analyzes the received information content using a generative AI model.

[0697] Input: Submitted information content and metadata

[0698] Output: Analysis results (possibility of fake news)

[0699] How it works: The server inputs the received data into an AI analysis module (using TensorFlow), which evaluates the reliability of the information based on a generative AI model, compares it with a database of trusted sources, and calculates the likelihood of it being fake news.

[0700] Step 5:

[0701] The server transmits the determination result to the user's terminal.

[0702] Input: Analysis results (possible fake news)

[0703] Output: Send the result to the user's device

[0704] How it works: Based on the results of AI analysis, the server sends the fake news judgment result to the device, and this data is immediately notified to the user.

[0705] Step 6:

[0706] The terminal displays a warning message to the user based on the determination result received from the server.

[0707] Input: Verification result (high probability of fake news)

[0708] Output: Display a warning message

[0709] Specific operation: The device displays the warning message received from the server to the user, informing them of the warning content, such as "This may be fake news."

[0710] Operation in a specific example

[0711] For example, if a user sees a news article on social media that says, "A large typhoon will hit tonight,"

[0712] Step 1: User browses to a news article.

[0713] Step 2: The device retrieves the text data, article title, and URL.

[0714] Step 3: The device sends this data to the server.

[0715] Step 4: The server uses the generative AI model to analyze the news article and assess its likelihood of being fake news.

[0716] Step 5: The server determines that the news is likely fake and sends the result to the device.

[0717] Step 6: The device displays a warning message to alert the user.

[0718] 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.

[0719] overview

[0720] The present invention is a system that analyzes information content viewed by users in social networking applications or web browsers in real time and displays a warning when there is a possibility of fake news. It also combines an emotion engine that recognizes the user's emotions and adjusts the strength of the warning based on the user's emotions.

[0721] Specific Embodiments

[0722] 1. User installs the app

[0723] The user installs a dedicated application on their smartphone. When the application is launched for the first time, it requests necessary permissions (permission to access data folders of social networking applications and web browsers, and permission to use the emotion engine).

[0724] 2. Monitoring social media content

[0725] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0726] 3. Sending to the server for content analysis

[0727] The device transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[0728] 4. AI analysis on the server

[0729] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[0730] 5. Identifying fake news

[0731] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[0732] 6. Emotion Recognition by Emotion Engine

[0733] The device uses an emotion engine to recognize the user's emotions in real time, for example, by analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone to evaluate the user's emotional state.

[0734] 7. Adjusting warnings based on emotions

[0735] The device adjusts the intensity of the warning based on the emotion data from the emotion engine. For example, if the user is feeling anxious or stressed, the device displays a softer warning message. Conversely, if the user is calm, the device displays a normal warning message.

[0736] 8. Warning Display

[0737] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[0738] Specific examples of processing

[0739] Example 1: A user views a news article on a social networking site

[0740] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[0741] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[0742] The terminal transmits the acquired text content to the server.

[0743] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[0744] The server inputs the received content into the AI ​​analysis module for analysis.

[0745] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[0746] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[0747] The device uses an emotion engine to recognize the user's emotions, for example, assessing stress or relief from the user's facial expressions and tone of voice.

[0748] Based on the received judgment results and the evaluation results of the emotion engine, the device displays a warning message to the user saying, "This may be fake news."

[0749] If the user is feeling stressed, the message is displayed softly.

[0750] If the user is sober, the warning is displayed in the normal format.

[0751] Example 2: Disseminating disaster information during emergencies

[0752] A user views disaster information on a social networking application.

[0753] The terminal acquires the text content of the disaster information and sends it to the server.

[0754] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[0755] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[0756] The device recognizes the user's emotions and adjusts warning messages based on their state.

[0757] For example, in an emergency, if the user is already panicking, the warning will be calming.

[0758] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0759] This allows the user to reduce the risk of believing false information and act based on accurate information. Furthermore, by combining it with an emotion engine, it is possible to provide appropriate warnings tailored to the user's emotional state and adjust how information is received.

[0760] The processing flow will be explained below.

[0761] Step 1:

[0762] The user installs the app on their smartphone. When the app is launched for the first time, it requests the necessary permissions (permission to access the data folder of the social networking application and web browser, and permission to use the emotion engine).

[0763] Step 2:

[0764] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[0765] Step 3:

[0766] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[0767] Step 4:

[0768] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[0769] Step 5:

[0770] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[0771] Step 6:

[0772] The server passes the received information content to an AI analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[0773] Step 7:

[0774] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[0775] Step 8:

[0776] The server generates a judgment result and sends it to the device, which includes a flag indicating the possibility of fake news.

[0777] Step 9:

[0778] The device uses an emotion engine to recognize the user's emotions in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to assess the user's emotional state.

[0779] Step 10:

[0780] Based on the judgment results received by the device and the evaluation results of the emotion engine, the warning is displayed with appropriate intensity. For example, if the user is feeling stressed, the warning message is displayed softly. If the user is calm, the normal warning message is displayed.

[0781] Step 11:

[0782] The device displays a tailored warning message to the user, including the message "This may be fake news," but presented differently depending on the user's emotional state.

[0783] Step 12:

[0784] Users can check the warning message and reassess the reliability of the information, reducing the risk of believing false information. Furthermore, alerts tailored to the user's emotional state optimize how they receive information.

[0785] Example 2

[0786] 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."

[0787] The purpose of this invention is to detect fake news contained in information viewed by users on the Internet in real time and issue appropriate warnings. Furthermore, by providing flexible warning messages that are tailored to the user's emotional state, the problem of providing truthful information while reducing user stress is solved.

[0788] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an AI analysis means, a means for maintaining a database of reliable information sources, and a means for collating information content. This makes it possible to analyze the reliability of information content viewed by a user in real time, identify information that is likely to be fake news, and further adjust the strength of a warning message based on the emotional state of the user.

[0789] "User" means an individual or end user who uses the System.

[0790] A "terminal" is a device used by a user, and includes a smartphone, tablet, PC, etc.

[0791] "Information content" refers to data such as text, images, and videos that users view via the Internet.

[0792] The "means for analyzing in real time" is a function or device for instantly analyzing the information content that the user is viewing on the spot.

[0793] A "server" is a computer system on a network for analyzing and processing information.

[0794] "AI analysis" is an analytical method that uses artificial intelligence technology to evaluate the reliability of information content.

[0795] "Reliable sources" refer to highly accurate information provided by public institutions and authoritative media.

[0796] A "database" is a collection of information that is systematically stored and made easily accessible and collated.

[0797] An "emotion engine" is software or an algorithm that recognizes and evaluates a user's emotional state from facial expressions, tone of voice, etc.

[0798] The "means for adjusting the intensity of the warning" is a function or device for changing the content and expression of the warning message according to the emotional state of the user.

[0799] MODE FOR CARRYING OUT THE INVENTION

[0800] overview

[0801] This system analyzes the information content a user browses online in real time to detect possible fake news. It also recognizes the user's emotional state and adjusts the intensity of warning messages based on that emotion. The system aims to provide accurate information while reducing user stress.

[0802] User installs the app

[0803] Users install a dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. This allows the application to access the necessary data.

[0804] Social media content monitoring

[0805] The device monitors temporary files generated by social media applications and web browsers by periodically checking the data folder for new or updated files and retrieving their contents.

[0806] Send to server for content analysis

[0807] The device encrypts the acquired information content and sends it to the server. The data sent includes the text content of the information being viewed and related metadata (e.g., article title and URL). This allows data to be sent in the background without the user's knowledge.

[0808] AI analysis on the server

[0809] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news, which uses a pre-trained generative AI model to compare the received content with a database of trusted sources to calculate the probability of it being fake news.

[0810] Fake news detection

[0811] The server determines the likelihood that the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined that the information content is likely to be fake news, the server returns the determination result to the device.

[0812] Emotion recognition by emotion engine

[0813] The device activates an emotion engine to recognize the user's emotions in real time. Specifically, it analyzes the user's facial expressions and tone of voice via the smartphone's camera and microphone to assess their emotional state.

[0814] Adjusting alerts based on emotion

[0815] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user feels anxious or stressed, the device displays the warning message in a softer tone, and if the user feels calm, the device displays the warning message in a normal tone.

[0816] Displaying warnings

[0817] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[0818] Specific examples

[0819] An example of a specific prompt is as follows:

[0820] "This system analyzes information content viewed on social media and web browsers in real time and displays a warning to users if it detects possible fake news. It also recognizes the user's emotional state and adjusts the strength of the warning based on their emotions. Specifically, it monitors the data folders of social media applications and web browsers, sends the detected content to a server, and an AI analysis module evaluates the possibility of it being fake news. It then adjusts and displays a warning message according to the user's emotional state."

[0821] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0822] Step 1:

[0823] The user installs the dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. Once the user grants these permissions, the application is able to access the necessary data.

[0824] Input: User installation operations, access permissions

[0825] Output: App initial setup complete, necessary permissions obtained

[0826] Step 2:

[0827] The device monitors temporary files generated by social media applications and web browsers. Applications periodically check the data folder for new or updated files and retrieve their contents. For example, the monitoring software may scan the data folder every second.

[0828] Input: Temporary files from social networking applications and web browsers

[0829] Output: New information content detected (text, images, videos, etc.)

[0830] Step 3:

[0831] The device then transmits the retrieved information content to the server. This transmission process occurs automatically in the background, and the transmitted data includes the text content of the information and associated metadata (e.g., article title and URL). Specifically, the device encrypts the data and transmits it via a secure communications protocol.

[0832] Input: Detected information content

[0833] Output: The encrypted information content is sent to the server

[0834] Step 4:

[0835] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses a pre-trained generative AI model to compare it with a database of trusted sources. Specifically, the analysis module runs an algorithm to calculate the reliability of the information.

[0836] Input: Encrypted information content

[0837] Output: Assessment result on the likelihood of fake news

[0838] Step 5:

[0839] The server determines whether the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined to be fake news, the server sends the result of that judgment to the device. Specifically, the server analyzes the evaluation results and generates an appropriate warning message.

[0840] Input: Evaluation results from the AI ​​analysis module

[0841] Output: Fake news detection result, warning message

[0842] Step 6:

[0843] The device activates an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through the smartphone's camera and microphone to assess their emotional state. Specifically, the emotion recognition software uses image processing and voice analysis algorithms.

[0844] Input: User's facial expressions, tone of voice

[0845] Output: User's emotional data (stress, relief, etc.)

[0846] Step 7:

[0847] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user is feeling anxious or stressed, the warning message is displayed in a softer tone, and if the user is calm, the warning message is displayed in a normal format. Specifically, the device dynamically changes the content and expression of the warning message based on the emotion data.

[0848] Input: User emotion data, warning message

[0849] Output: Adjusted warning message

[0850] Step 8:

[0851] The device then displays a tailored warning message to the user, which includes the message "This may be fake news," but is presented differently depending on the user's emotional state. Specifically, the message appears on the screen for the user to immediately acknowledge.

[0852] Input: Adjusted warning message

[0853] Output: The warning message displayed to the user.

[0854] (Application example 2)

[0855] 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."

[0856] In today's information society, there is a high possibility that fake news is included in the information content that users visually view, and there are risks associated with believing and acting on that information. Furthermore, viewing fake news while in an emotionally unstable state can have a particularly significant impact on users. Therefore, a system is needed that analyzes the content viewed by users in real time and issues appropriate warnings when there is a possibility of fake news. Furthermore, it is necessary to ensure that warnings are appropriately accepted by adjusting the wording of the warning according to the user's emotional state.

[0857] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing information content visually viewed by the user, means for transmitting the analyzed information content to the server, and means for the server to analyze the possibility of fake news based on the information content. This makes it possible to analyze the content viewed by the user in real time and display a warning if there is a high possibility that it is fake news. In addition, the display device includes means for recognizing the user's emotions using an emotion engine and means for adjusting the strength of the warning based on the user's emotions, making it possible to issue an appropriate warning according to the user's emotional state.

[0858] "User" refers to a person who uses this system.

[0859] "Visually viewed information content" refers to digital information such as text, images, and videos that can be visually viewed by a user.

[0860] "Means for analyzing" refers to a method or apparatus for analyzing visually viewed information content.

[0861] "Server" refers to a computer system capable of receiving analyzed information content and transmitting the results of the analysis.

[0862] "Means for sending" refers to a method or apparatus for sending the parsed information content to a server.

[0863] "Fake news" refers to news that contains intentionally false information.

[0864] "Decision result" refers to the evaluation result after the server analyzes the possibility of fake news.

[0865] "Display device" refers to a device for visually displaying analysis results and warning messages to a user.

[0866] "Means for displaying a warning" refers to a method or device for warning users of possible fake news.

[0867] An "emotion engine" refers to an algorithm or software for analyzing a user's emotions.

[0868] "Means for recognizing emotions" refers to a method or device for detecting the emotional state of a user.

[0869] "Means for adjusting the intensity of an alert" refers to a method or apparatus for adjusting the wording or content of an alert based on the user's emotional state.

[0870] A "reliable source" is a source that provides accurate and reliable information.

[0871] "Source database" means a digital database for storing information collected from reliable sources.

[0872] "Means for matching" refers to a method or device for comparing the analyzed information content with a database of information sources to identify matches and discrepancies.

[0873] "Temporary files" refer to data files that are temporarily stored and used by social networking applications and web browsers.

[0874] "Means for monitoring" refers to a method or device for periodically checking the contents of temporary files and extracting necessary information.

[0875] "Optical character recognition" refers to techniques and devices for extracting text information from image data.

[0876] This invention is a system that analyzes information content visually viewed by users in real time and displays a warning when there is a possibility of fake news. In particular, it combines a function that recognizes the user's emotions using an emotion engine and adjusts the strength of the warning.

[0877] Hardware and Software Examples

[0878] Hardware

[0879] Smart Glasses: As an example, we use general-purpose smart glasses, which have a camera to capture the information content that the user visually views, and a built-in microphone to recognize the user's facial expressions.

[0880] Server: Uses sophisticated computer systems to analyze information content and assess potential fake news.

[0881] software

[0882] OCR library: Using Tesseract OCR as an example, we extract text information from image data captured by the camera in smart glasses.

[0883] Network library: Using Retrofit as an example, we send parsed text data to a server.

[0884] Emotion Recognition Library: As an example, we will use the Affectiva SDK to analyze emotions from the user's facial expressions and tone of voice.

[0885] AI analysis module: Evaluates the reliability of information content using a server-side built-in AI model, which includes pre-trained generative AI.

[0886] Process Overview

[0887] 1. Information content analysis

[0888] The camera in the smart glasses captures the information content that the user is viewing, converts the image data into text data using an OCR library, and then transmits the text data to a server using a network library.

[0889] 2. Analysis on the server

[0890] The server passes the received text data to an AI analysis module to assess the likelihood of it being fake news, comparing it with a database of trusted sources. The AI ​​analysis module uses a trained model to assess the reliability of the information and obtains a verdict.

[0891] 3. Emotional awareness and alert regulation

[0892] The server sends the result of the assessment back to the smart glasses. At the same time, the smart glasses' microphone and camera capture the user's facial expressions and tone of voice, and an emotion recognition library is used to analyze the user's emotions. The strength of the warning is adjusted based on this emotional data. For example, if the user is feeling anxious or stressed, the warning will be displayed in a more gentle manner, and if the user is calm, the warning will be displayed in a more normal manner.

[0893] 4. Warning Display

[0894] The smart glasses display a warning message saying "This may be fake news." The display format of the warning message is adjusted according to the user's emotional state.

[0895] Examples of concrete examples and prompts

[0896] Specific examples

[0897] When a user is wearing smart glasses and browsing a news site, the following happens:

[0898] 1. The displayed content of a news article is captured by the camera in the smart glasses.

[0899] 2. The captured image is converted to text using an OCR library.

[0900] 3. The converted text data is sent to a server, where an AI analysis module evaluates the likelihood of it being fake news.

[0901] 4. The smart glasses' camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the user's emotions.

[0902] 5. Based on the fake news judgment results and emotional data, a warning message will be displayed on the smart glasses display.

[0903] Prompt Sentence Examples

[0904] Use the following prompt to ask the generative AI model to analyze a news article:

[0905] Rate the following news articles as to whether they are fake news or not.

[0906] News article title: [Title]

[0907] News article content: [Content]

[0908] Comparison with reliable primary information sites: [Comparison results]

[0909] Please assess the possibility of fake news based on the verification results.

[0910] Using this prompt, the generative AI model can analyze news articles and determine whether they are fake news.

[0911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0912] Step 1:

[0913] A user wears smart glasses and visually views information content. The camera in the smart glasses captures the information content the user is viewing. The input at this stage is the image data captured by the camera, and the output is the image data.

[0914] Step 2:

[0915] The device uses an OCR library (e.g., Tesseract OCR) to convert the captured image into text data. The input is the captured image data, and the output is the text data extracted from the image. Specifically, the OCR library detects each character or word and converts it into text format.

[0916] Step 3:

[0917] The device uses a network library (e.g., Retrofit) to send the parsed text data to the server. The input is the text data obtained by OCR, and the output is the text data sent to the server. Specifically, the network library generates an HTTP request and sends the text data as a payload to the server.

[0918] Step 4:

[0919] The server passes the received text data to an AI analysis module (e.g., a generative AI model) to evaluate the likelihood of it being fake news. The input is the text data sent to the server, and the output is the result of the fake news judgment. Specifically, the generative AI model refers to the data it has been trained on and compares it with reliable sources to calculate the probability. The following prompt sentences are used:

[0920] Rate the following news articles as to whether they are fake news or not.

[0921] News article title: [Title]

[0922] News article content: [Content]

[0923] Comparison with reliable primary information sites: [Comparison results]

[0924] Please assess the possibility of fake news based on the verification results.

[0925] Step 5:

[0926] The server sends the fake news judgment result to the terminal. The input is the judgment result from the AI ​​analysis module, and the output is the judgment result sent to the terminal. Specifically, the server returns the judgment result to the terminal as an HTTP response.

[0927] Step 6:

[0928] The device uses the camera and microphone of the smart glasses to capture the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[0929] Step 7:

[0930] The device uses an emotion recognition library (e.g., Affectiva SDK) to recognize the user's emotions from the captured facial and voice data. The input is the captured facial and voice data, and the output is the analyzed emotion data. Specifically, the emotion recognition library uses a facial expression analysis algorithm to evaluate the user's emotional state.

[0931] Step 8:

[0932] The device adjusts the strength of the warning based on the fake news judgment result and emotional data. The input is the judgment result and emotional data, and the output is a warning message with adjusted strength. Specifically, the emotion engine changes the tone and strength of the warning depending on the user's stress level.

[0933] Step 9:

[0934] The terminal displays a warning message on the display of the smart glasses. The input is a warning message with adjusted intensity, and the output is a visually displayed warning message. Specifically, the warning "This may be fake news" is displayed on the display in a softer or normal form depending on the user's emotion.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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."

[0951] overview

[0952] This system analyzes information content viewed by users on social media applications or web browsers in real time and displays a warning if the content is suspected to be fake news. In particular, it uses a database of trusted primary information sites to collate the information content and identify fake news.

[0953] Specific Embodiments

[0954] 1. User installs the app

[0955] Users install a dedicated application on their smartphone, and when they launch the app for the first time, it requests necessary permissions, including permission to access data from social networking applications and web browsers.

[0956] 2. Monitoring social media content

[0957] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[0958] 3. Sending to the server for content analysis

[0959] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata.

[0960] 4. AI analysis on the server

[0961] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[0962] 5. Identifying fake news

[0963] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[0964] 6. Warning Display

[0965] Based on the result of the judgment received from the server, the device displays a warning to the user, which is displayed as a message saying "This may be fake news."

[0966] Specific examples of processing

[0967] Example 1: A user views a news article on a social networking site

[0968] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[0969] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[0970] The terminal transmits the acquired text content to the server.

[0971] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[0972] The server inputs the received content into the AI ​​analysis module for analysis.

[0973] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[0974] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[0975] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[0976] Example 2: Disseminating disaster information during emergencies

[0977] A user views disaster information on a social networking application.

[0978] The terminal acquires the text content of the disaster information and sends it to the server.

[0979] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[0980] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[0981] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[0982] This provides a system that reduces the risk of users believing incorrect information and enables them to act based on accurate information. This system is particularly effective in times of disaster or emergency, and contributes to improving the reliability of society as a whole.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] A user installs an app on their smartphone. When the app is launched for the first time, it requests necessary permissions (such as access to the data folder of a social networking application or web browser).

[0986] Step 2:

[0987] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[0988] Step 3:

[0989] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[0990] Step 4:

[0991] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[0992] Step 5:

[0993] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[0994] Step 6:

[0995] The server passes the received information content to the analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[0996] Step 7:

[0997] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[0998] Step 8:

[0999] The server generates a judgment result and returns it to the device, which includes a flag indicating whether the news is likely to be fake.

[1000] Step 9:

[1001] The device analyzes the results received from the server. If the news is likely to be fake, the device displays a warning message to the user. The warning message clearly states, "This may be fake news."

[1002] Step 10:

[1003] Users check the warning message and reassess the reliability of the information, thereby reducing the risk of believing incorrect information.

[1004] Example 1

[1005] 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."

[1006] In modern society, fake news and false information are increasingly being spread via the Internet. This puts users at risk of believing incorrect information, which can have serious consequences, especially during emergencies and disasters. However, it is difficult for users to verify the veracity of this information themselves. There is also a need for a quick means to make decisions based on reliable information. Therefore, the present invention provides a system that analyzes the information content viewed by users in real time and displays a warning if the content is likely to be fake news. This reduces the risk of users believing incorrect information and enables them to make decisions based on accurate information.

[1007] 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.

[1008] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for comparing the information content with reliable primary information sites, means for the server to transmit the fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for monitoring temporary files of SNS applications and web browsers, and means for automatically transmitting the analyzed information content and processing it in the background. This makes it possible to quickly analyze the authenticity of information content viewed by a user in real time and issue a warning about information that is likely to be fake news.

[1009] "User" refers to a person who uses this system to view information content using a social networking application or web browser.

[1010] "Information content" refers to digital information such as text, images, and videos that users view through social networking applications or web browsers.

[1011] "Means of analysis" refers to the techniques and methods used to analyze acquired information content and understand its contents.

[1012] "Server" refers to a central processing unit that analyzes received information content and evaluates and determines whether it is fake news.

[1013] "Means for transmitting" refers to the technology or method for transmitting the analyzed information content from the terminal to the server.

[1014] "Fake news" refers to news or information that is false or misleading and does not correspond to accurate and reliable primary sources.

[1015] A "trusted primary information site" refers to a government agency, public institution, or other trusted source of information.

[1016] "Means of matching" refers to the technology or method used to compare the analyzed information content with the database of a trusted primary information site to confirm the degree of consistency.

[1017] "Determination result" refers to the evaluation result of whether the information content is fake news, obtained as a result of analysis and comparison by the server.

[1018] "Means for displaying a warning" refers to technologies and methods for notifying users that the information content they are viewing may be fake news.

[1019] "SNS Application" refers to software that provides a social networking service that enables users to share information with other users.

[1020] A "web browser" refers to software that allows users to view web pages on the Internet.

[1021] "Temporary files" refer to data files used by social networking applications and web browsers for short periods of time.

[1022] "Means processed in the background" refers to techniques and methods for automatically processing data within the system without interfering with user operations.

[1023] MODE FOR CARRYING OUT THE INVENTION

[1024] overview

[1025] This invention is a system that analyzes information content viewed by users in social media applications or web browsers in real time and displays a warning if there is a possibility of fake news. This system collates information content using a database of reliable primary information sites to identify fake news.

[1026] The user installs the app

[1027] Users install a dedicated fake news detection application on their smartphone. When the application is launched for the first time, it requests the necessary permissions to access data from social media applications and web browsers, allowing the application to access the necessary data.

[1028] The device monitors social media content

[1029] The device monitors temporary files generated by social media applications and web browsers in real time. Specifically, it monitors specific folders and cache data on the file system, and detects new files as content to be analyzed when they are created. This function detects content such as text, images, and videos viewed by the user in real time.

[1030] The device sends the content to the server

[1031] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and is uninterrupted during user operations. Specifically, metadata such as text content, URLs, and article titles are sent to the server.

[1032] Data reception on the server and AI analysis

[1033] The server receives the information content sent from the device and passes the received data to the AI ​​analysis module. During this process, the data is verified and organized. For example, invalid or incomplete data is removed and formatted for analysis. The server then uses the AI ​​analysis module to analyze the information content. A pre-trained generative AI model is used for the analysis to assess the likelihood of fake news. During this process, the reliability of the information is assessed by comparing it with a database of trusted primary information sites.

[1034] The server determines fake news

[1035] The server determines the possibility of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high possibility of fake news, it returns the result to the device as a warning notification. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[1036] The device displays a warning to the user

[1037] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user can confirm the message and reduce the risk of believing false information.

[1038] Specific examples of processing and prompt statements

[1039] Example 1: When a user browses a news article on a social networking site

[1040] When a user clicks on a news article link in a social networking application, the browser opens the page of the link.

[1041] An application on the device monitors the browser folder and automatically retrieves the text content of opened news articles.

[1042] The device sends the acquired text content and related metadata (title, URL, etc.) to the server.

[1043] The server passes the received data to the AI ​​analysis module for analysis.

[1044] Specifically, a prompt such as "Is this news true?" is fed into the generative AI model, which then matches it with matching primary information sites.

[1045] If the server determines that the news is likely fake, it sends the result of the determination and the reason (e.g., "This news article does not match the official information site") to the device.

[1046] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[1047] Example 2: Viewing disaster information during an emergency

[1048] A user views disaster information on a social networking application.

[1049] The device acquires the text content of the disaster information and related metadata and transmits it to the server.

[1050] The server passes the received information to an AI analysis module, which compares it with reliable primary information sites (e.g., the official website of the Japan Meteorological Agency or the official website of a local government).

[1051] If the server determines that the information is incorrect, it returns the determination result and reason (e.g., "Does not match official information") to the terminal.

[1052] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[1053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1054] Step 1:

[1055] Users install the fake news detection application on their smartphones and grant the necessary permissions when they first launch it, including permission to access data from social media applications and web browsers.

[1056] Input: User operation, first launch of application

[1057] Output: Application gets required permissions

[1058] Step 2:

[1059] The device monitors temporary files generated by social networking applications and web browsers in real time. This monitoring detects the information content (text, images, videos, etc.) viewed by the user. Specifically, it monitors designated folders and cache data, and retrieves the contents of new files as soon as they are created.

[1060] Input: Temporary files generated by social networking applications and web browsers

[1061] Output: Detected information content (text, images, videos, etc.)

[1062] Step 3:

[1063] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and does not interfere with the user's operations. Specifically, metadata such as text content, URLs, and article titles are formatted and sent to the server.

[1064] Input: Detected information content (text, images, videos, etc.), associated metadata (title, URL, etc.)

[1065] Output: Formatted data sent to the server

[1066] Step 4:

[1067] The server receives the information content sent from the device, verifies and organizes the data, removing invalid or incomplete data and formatting it for analysis, before passing the received data to the AI ​​analysis module.

[1068] Input: Formatted data sent from the terminal

[1069] Output: Organized data passed to the AI ​​analysis module

[1070] Step 5:

[1071] The server uses an AI analysis module to analyze the information content. It then uses a generative AI model to evaluate the reliability of the information. This analysis process involves matching the information with a database of trusted primary information sites. Specifically, a prompt such as "Is this news true?" is input into the generative AI model to calculate the degree of match.

[1072] Input: Organized data, database of reliable primary information sites

[1073] Output: AI analysis results (score, evaluation reason)

[1074] Step 6:

[1075] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, it returns the result and the reason to the device. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[1076] Input: AI analysis results (score, evaluation reason)

[1077] Output: The result of the judgment sent to the device

[1078] Step 7:

[1079] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user confirms the message, reducing the risk of believing false information.

[1080] Input: Verification result from the server

[1081] Output: A warning message that is displayed to the user.

[1082] Through the above processing steps, users can quickly analyze the authenticity of the information content they are viewing in real time and receive warnings about information that is likely to be fake news.

[1083] (Application example 1)

[1084] 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."

[1085] Traditionally, a lot of information has been distributed through social media applications and web browsers, and this information often contains fake news. This fake news can mislead and confuse users. Furthermore, in emergencies and disasters, the rapid spread of false information can have a significant impact on society as a whole. Conventional fake news countermeasures have tended to be reactive, making it difficult to provide users with fake news warnings in real time. Therefore, there is a need for a system that can analyze the information content viewed by users and warn them of possible fake news in real time.

[1086] 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.

[1087] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for the server to transmit a fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for referencing a database of reliable information sources and comparing the information content, means for using a generative AI model for fake news determination, means for generating a prompt sentence for the generative AI model, means for the terminal to obtain temporary files from a monitored application, and means for transmitting and analyzing data in the background. This enables accurate analysis in real time and enables immediate evaluation of the reliability of the information viewed by the user.

[1088] "Information content viewed by users" refers to digital information such as articles, posts, images, and videos that users view through social media applications or web browsers.

[1089] "Analysis tools" refers to the software or algorithms used to analyze information content and understand its content and structure.

[1090] "Means for transmitting to the server" refers to the network communication protocol or interface for transferring data from the user's terminal to the server.

[1091] "Measures for analyzing potential fake news" refers to algorithms or AI models used to assess whether information content is false or not.

[1092] "Means for transmitting fake news determination results to the terminal" refers to a communication protocol for informing the terminal of the analysis results from the server.

[1093] "Means for displaying a warning to the user" refers to an interface or notification system for displaying a warning message to the user when there is a high possibility of fake news.

[1094] A "database of trusted sources" is a database that compiles information from official institutions and reliable information sources.

[1095] A "generative AI model" is an AI model that is trained using large datasets and optimized to perform a specific task.

[1096] A "prompt" is an input text given to a generative AI model to perform a specific analysis or judgment.

[1097] "Temporary files" are temporary data files generated while using a social networking application or web browser.

[1098] "Means for transmitting and analyzing data in the background" refers to a system or process that automatically transmits and analyzes data in the background while the user is working.

[1099] System Overview

[1100] The present invention is a system that monitors information content viewed by users through social media applications and web browsers in real time and displays a warning when there is a high possibility that the content is fake news. The system has the following main functions:

[1101] 1. User installs the app

[1102] The user installs a dedicated application on their smartphone, and upon first launch, the application requests necessary permissions, including permission to access data from social networking applications and web browsers.

[1103] 2. Acquiring and monitoring information content

[1104] The user's device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[1105] 3. Data transmission

[1106] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background and does not interfere with the user's operations. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[1107] 4. Analysis on the server

[1108] The server analyzes the received information content using a generative AI model, which assesses the reliability of the information by checking it against a database of pre-trusted sources.

[1109] 5. Fake news detection and notification

[1110] If the server determines that the news is likely fake, it sends the result to the device, which then displays a warning message to the user, stating, "This may be fake news."

[1111] Hardware and Software Used

[1112] Hardware: Smartphone (Android or iOS)

[1113] Software: Python, TensorFlow, Firebase

[1114] Database: A database of trusted sources

[1115] Data processing and calculation

[1116] The server analyzes the information content sent by the user in the following steps and generates an appropriate warning.

[1117] 1. Data Acquisition:

[1118] Monitors temporary files from social media applications and web browsers and captures text data and metadata.

[1119] 2. Data transmission and analysis:

[1120] Send the acquired data to the server (Firebase).

[1121] The data is analyzed using an AI analysis module (using TensorFlow) on the server and compared with a database of trusted information sources.

[1122] 3. Sending the results and warnings:

[1123] If there is a high possibility that the news is fake, the analysis results are sent to the user's device and a warning message is displayed on the device.

[1124] Specific examples

[1125] For example, if a user is viewing a news article on social media that says "A large typhoon will hit tonight," they would follow these steps:

[1126] The application monitors the text of news articles and sends it to a server.

[1127] The AI ​​model on the server analyzes the news article and compares it with information from trusted sources (e.g., meteorological agencies).

[1128] If the app determines that the news is likely to be fake, it will warn the user, saying, "This may be fake news."

[1129] Prompt Sentence Examples

[1130] content = "A large typhoon will hit tonight"

[1131] metadata = {"title": "Typhoon News", "url": "example.com / typhoon_news"}

[1132] result = check_fakenews(content, metadata)

[1133] This sentence shows an example of inputting the text and metadata of a news article into an AI model, and then determining whether it is fake news based on the output.

[1134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1135] Step 1:

[1136] A user views information content through a social networking application or a web browser.

[1137] Input: Information content (news articles, posts, images, videos, etc.)

[1138] Output: None

[1139] How it works: When a user browses information using a social networking application or web browser on their smartphone, the content of this information is monitored by the application.

[1140] Step 2:

[1141] The device monitors temporary files of social networking applications and web browsers and obtains text data and metadata of information content.

[1142] Input: Temporary file (text data, article title, URL, etc.)

[1143] Output: Retrieved information content and metadata

[1144] How it works: An application on the device monitors temporary files in real time and automatically extracts text data and related metadata from the information content viewed.

[1145] Step 3:

[1146] The terminal transmits the acquired information content and metadata to the server.

[1147] Input: Information content and metadata

[1148] Output: Send data to the server

[1149] How it works: The device sends information content and metadata to the server in the background, without interfering with the user's operations.

[1150] Step 4:

[1151] The server analyzes the received information content using a generative AI model.

[1152] Input: Submitted information content and metadata

[1153] Output: Analysis results (possibility of fake news)

[1154] How it works: The server inputs the received data into an AI analysis module (using TensorFlow), which evaluates the reliability of the information based on a generative AI model, compares it with a database of trusted sources, and calculates the likelihood of it being fake news.

[1155] Step 5:

[1156] The server transmits the determination result to the user's terminal.

[1157] Input: Analysis results (possible fake news)

[1158] Output: Send the result to the user's device

[1159] How it works: Based on the results of AI analysis, the server sends the fake news judgment result to the device, and this data is immediately notified to the user.

[1160] Step 6:

[1161] The terminal displays a warning message to the user based on the determination result received from the server.

[1162] Input: Verification result (high probability of fake news)

[1163] Output: Display a warning message

[1164] Specific operation: The device displays the warning message received from the server to the user, informing them of the warning content, such as "This may be fake news."

[1165] Operation in a specific example

[1166] For example, if a user sees a news article on social media that says, "A large typhoon will hit tonight,"

[1167] Step 1: User browses to a news article.

[1168] Step 2: The device retrieves the text data, article title, and URL.

[1169] Step 3: The device sends this data to the server.

[1170] Step 4: The server uses the generative AI model to analyze the news article and assess its likelihood of being fake news.

[1171] Step 5: The server determines that the news is likely fake and sends the result to the device.

[1172] Step 6: The device displays a warning message to alert the user.

[1173] 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.

[1174] overview

[1175] The present invention is a system that analyzes information content viewed by users in social networking applications or web browsers in real time and displays a warning when there is a possibility of fake news. It also combines an emotion engine that recognizes the user's emotions and adjusts the strength of the warning based on the user's emotions.

[1176] Specific Embodiments

[1177] 1. User installs the app

[1178] The user installs a dedicated application on their smartphone. When the application is launched for the first time, it requests necessary permissions (permission to access data folders of social networking applications and web browsers, and permission to use the emotion engine).

[1179] 2. Monitoring social media content

[1180] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[1181] 3. Sending to the server for content analysis

[1182] The device transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[1183] 4. AI analysis on the server

[1184] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[1185] 5. Identifying fake news

[1186] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[1187] 6. Emotion Recognition by Emotion Engine

[1188] The device uses an emotion engine to recognize the user's emotions in real time, for example, by analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone to evaluate the user's emotional state.

[1189] 7. Adjusting warnings based on emotions

[1190] The device adjusts the intensity of the warning based on the emotion data from the emotion engine. For example, if the user is feeling anxious or stressed, the device displays a softer warning message. Conversely, if the user is calm, the device displays a normal warning message.

[1191] 8. Warning Display

[1192] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[1193] Specific examples of processing

[1194] Example 1: A user views a news article on a social networking site

[1195] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[1196] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[1197] The terminal transmits the acquired text content to the server.

[1198] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[1199] The server inputs the received content into the AI ​​analysis module for analysis.

[1200] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[1201] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[1202] The device uses an emotion engine to recognize the user's emotions, for example, assessing stress or relief from the user's facial expressions and tone of voice.

[1203] Based on the received judgment results and the evaluation results of the emotion engine, the device displays a warning message to the user saying, "This may be fake news."

[1204] If the user is feeling stressed, the message is displayed softly.

[1205] If the user is sober, the warning is displayed in the normal format.

[1206] Example 2: Disseminating disaster information during emergencies

[1207] A user views disaster information on a social networking application.

[1208] The terminal acquires the text content of the disaster information and sends it to the server.

[1209] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[1210] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[1211] The device recognizes the user's emotions and adjusts warning messages based on their state.

[1212] For example, in an emergency, if the user is already panicking, the warning will be calming.

[1213] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[1214] This allows the user to reduce the risk of believing false information and act based on accurate information. Furthermore, by combining it with an emotion engine, it is possible to provide appropriate warnings tailored to the user's emotional state and adjust how information is received.

[1215] The processing flow will be explained below.

[1216] Step 1:

[1217] The user installs the app on their smartphone. When the app is launched for the first time, it requests the necessary permissions (permission to access the data folder of the social networking application and web browser, and permission to use the emotion engine).

[1218] Step 2:

[1219] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[1220] Step 3:

[1221] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[1222] Step 4:

[1223] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[1224] Step 5:

[1225] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[1226] Step 6:

[1227] The server passes the received information content to an AI analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[1228] Step 7:

[1229] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[1230] Step 8:

[1231] The server generates a judgment result and sends it to the device, which includes a flag indicating the possibility of fake news.

[1232] Step 9:

[1233] The device uses an emotion engine to recognize the user's emotions in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to assess the user's emotional state.

[1234] Step 10:

[1235] Based on the judgment results received by the device and the evaluation results of the emotion engine, the warning is displayed with appropriate intensity. For example, if the user is feeling stressed, the warning message is displayed softly. If the user is calm, the normal warning message is displayed.

[1236] Step 11:

[1237] The device displays a tailored warning message to the user, including the message "This may be fake news," but presented differently depending on the user's emotional state.

[1238] Step 12:

[1239] Users can check the warning message and reassess the reliability of the information, reducing the risk of believing false information. Furthermore, alerts tailored to the user's emotional state optimize how they receive information.

[1240] Example 2

[1241] 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."

[1242] The purpose of this invention is to detect fake news contained in information viewed by users on the Internet in real time and issue appropriate warnings. Furthermore, by providing flexible warning messages that are tailored to the user's emotional state, the problem of providing truthful information while reducing user stress is solved.

[1243] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an AI analysis means, a means for maintaining a database of reliable information sources, and a means for collating information content. This makes it possible to analyze the reliability of information content viewed by a user in real time, identify information that is likely to be fake news, and further adjust the strength of a warning message based on the emotional state of the user.

[1244] "User" means an individual or end user who uses the System.

[1245] A "terminal" is a device used by a user, and includes a smartphone, tablet, PC, etc.

[1246] "Information content" refers to data such as text, images, and videos that users view via the Internet.

[1247] The "means for analyzing in real time" is a function or device for instantly analyzing the information content that the user is viewing on the spot.

[1248] A "server" is a computer system on a network for analyzing and processing information.

[1249] "AI analysis" is an analytical method that uses artificial intelligence technology to evaluate the reliability of information content.

[1250] "Reliable sources" refer to highly accurate information provided by public institutions and authoritative media.

[1251] A "database" is a collection of information that is systematically stored and made easily accessible and collated.

[1252] An "emotion engine" is software or an algorithm that recognizes and evaluates a user's emotional state from facial expressions, tone of voice, etc.

[1253] The "means for adjusting the intensity of the warning" is a function or device for changing the content and expression of the warning message according to the emotional state of the user.

[1254] MODE FOR CARRYING OUT THE INVENTION

[1255] overview

[1256] This system analyzes the information content a user browses online in real time to detect possible fake news. It also recognizes the user's emotional state and adjusts the intensity of warning messages based on that emotion. The system aims to provide accurate information while reducing user stress.

[1257] User installs the app

[1258] Users install a dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. This allows the application to access the necessary data.

[1259] Social media content monitoring

[1260] The device monitors temporary files generated by social media applications and web browsers by periodically checking the data folder for new or updated files and retrieving their contents.

[1261] Send to server for content analysis

[1262] The device encrypts the acquired information content and sends it to the server. The data sent includes the text content of the information being viewed and related metadata (e.g., article title and URL). This allows data to be sent in the background without the user's knowledge.

[1263] AI analysis on the server

[1264] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news, which uses a pre-trained generative AI model to compare the received content with a database of trusted sources to calculate the probability of it being fake news.

[1265] Fake news detection

[1266] The server determines the likelihood that the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined that the information content is likely to be fake news, the server returns the determination result to the device.

[1267] Emotion recognition by emotion engine

[1268] The device activates an emotion engine to recognize the user's emotions in real time. Specifically, it analyzes the user's facial expressions and tone of voice via the smartphone's camera and microphone to assess their emotional state.

[1269] Adjusting alerts based on emotion

[1270] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user feels anxious or stressed, the device displays the warning message in a softer tone, and if the user feels calm, the device displays the warning message in a normal tone.

[1271] Displaying warnings

[1272] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[1273] Specific examples

[1274] An example of a specific prompt is as follows:

[1275] "This system analyzes information content viewed on social media and web browsers in real time and displays a warning to users if it detects possible fake news. It also recognizes the user's emotional state and adjusts the strength of the warning based on their emotions. Specifically, it monitors the data folders of social media applications and web browsers, sends the detected content to a server, and an AI analysis module evaluates the possibility of it being fake news. It then adjusts and displays a warning message according to the user's emotional state."

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277] Step 1:

[1278] The user installs the dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. Once the user grants these permissions, the application is able to access the necessary data.

[1279] Input: User installation operations, access permissions

[1280] Output: App initial setup complete, necessary permissions obtained

[1281] Step 2:

[1282] The device monitors temporary files generated by social media applications and web browsers. Applications periodically check the data folder for new or updated files and retrieve their contents. For example, the monitoring software may scan the data folder every second.

[1283] Input: Temporary files from social networking applications and web browsers

[1284] Output: New information content detected (text, images, videos, etc.)

[1285] Step 3:

[1286] The device then transmits the retrieved information content to the server. This transmission process occurs automatically in the background, and the transmitted data includes the text content of the information and associated metadata (e.g., article title and URL). Specifically, the device encrypts the data and transmits it via a secure communications protocol.

[1287] Input: Detected information content

[1288] Output: The encrypted information content is sent to the server

[1289] Step 4:

[1290] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses a pre-trained generative AI model to compare it with a database of trusted sources. Specifically, the analysis module runs an algorithm to calculate the reliability of the information.

[1291] Input: Encrypted information content

[1292] Output: Assessment result on the likelihood of fake news

[1293] Step 5:

[1294] The server determines whether the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined to be fake news, the server sends the result of that judgment to the device. Specifically, the server analyzes the evaluation results and generates an appropriate warning message.

[1295] Input: Evaluation results from the AI ​​analysis module

[1296] Output: Fake news detection result, warning message

[1297] Step 6:

[1298] The device activates an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through the smartphone's camera and microphone to assess their emotional state. Specifically, the emotion recognition software uses image processing and voice analysis algorithms.

[1299] Input: User's facial expressions, tone of voice

[1300] Output: User's emotional data (stress, relief, etc.)

[1301] Step 7:

[1302] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user is feeling anxious or stressed, the warning message is displayed in a softer tone, and if the user is calm, the warning message is displayed in a normal format. Specifically, the device dynamically changes the content and expression of the warning message based on the emotion data.

[1303] Input: User emotion data, warning message

[1304] Output: Adjusted warning message

[1305] Step 8:

[1306] The device then displays a tailored warning message to the user, which includes the message "This may be fake news," but is presented differently depending on the user's emotional state. Specifically, the message appears on the screen for the user to immediately acknowledge.

[1307] Input: Adjusted warning message

[1308] Output: The warning message displayed to the user.

[1309] (Application example 2)

[1310] 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."

[1311] In today's information society, there is a high possibility that fake news is included in the information content that users visually view, and there are risks associated with believing and acting on that information. Furthermore, viewing fake news while in an emotionally unstable state can have a particularly significant impact on users. Therefore, a system is needed that analyzes the content viewed by users in real time and issues appropriate warnings when there is a possibility of fake news. Furthermore, it is necessary to ensure that warnings are appropriately accepted by adjusting the wording of the warning according to the user's emotional state.

[1312] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing information content visually viewed by the user, means for transmitting the analyzed information content to the server, and means for the server to analyze the possibility of fake news based on the information content. This makes it possible to analyze the content viewed by the user in real time and display a warning if there is a high possibility that it is fake news. In addition, the display device includes means for recognizing the user's emotions using an emotion engine and means for adjusting the strength of the warning based on the user's emotions, making it possible to issue an appropriate warning according to the user's emotional state.

[1313] "User" refers to a person who uses this system.

[1314] "Visually viewed information content" refers to digital information such as text, images, and videos that can be visually viewed by a user.

[1315] "Means for analyzing" refers to a method or apparatus for analyzing visually viewed information content.

[1316] "Server" refers to a computer system capable of receiving analyzed information content and transmitting the results of the analysis.

[1317] "Means for sending" refers to a method or apparatus for sending the parsed information content to a server.

[1318] "Fake news" refers to news that contains intentionally false information.

[1319] "Decision result" refers to the evaluation result after the server analyzes the possibility of fake news.

[1320] "Display device" refers to a device for visually displaying analysis results and warning messages to a user.

[1321] "Means for displaying a warning" refers to a method or device for warning users of possible fake news.

[1322] An "emotion engine" refers to an algorithm or software for analyzing a user's emotions.

[1323] "Means for recognizing emotions" refers to a method or device for detecting the emotional state of a user.

[1324] "Means for adjusting the intensity of an alert" refers to a method or apparatus for adjusting the wording or content of an alert based on the user's emotional state.

[1325] A "reliable source" is a source that provides accurate and reliable information.

[1326] "Source database" means a digital database for storing information collected from reliable sources.

[1327] "Means for matching" refers to a method or device for comparing the analyzed information content with a database of information sources to identify matches and discrepancies.

[1328] "Temporary files" refer to data files that are temporarily stored and used by social networking applications and web browsers.

[1329] "Means for monitoring" refers to a method or device for periodically checking the contents of temporary files and extracting necessary information.

[1330] "Optical character recognition" refers to techniques and devices for extracting text information from image data.

[1331] This invention is a system that analyzes information content visually viewed by users in real time and displays a warning when there is a possibility of fake news. In particular, it combines a function that recognizes the user's emotions using an emotion engine and adjusts the strength of the warning.

[1332] Hardware and Software Examples

[1333] Hardware

[1334] Smart Glasses: As an example, we use general-purpose smart glasses, which have a camera to capture the information content that the user visually views, and a built-in microphone to recognize the user's facial expressions.

[1335] Server: Uses sophisticated computer systems to analyze information content and assess potential fake news.

[1336] software

[1337] OCR library: Using Tesseract OCR as an example, we extract text information from image data captured by the camera in smart glasses.

[1338] Network library: Using Retrofit as an example, we send parsed text data to a server.

[1339] Emotion Recognition Library: As an example, we will use the Affectiva SDK to analyze emotions from the user's facial expressions and tone of voice.

[1340] AI analysis module: Evaluates the reliability of information content using a server-side built-in AI model, which includes pre-trained generative AI.

[1341] Process Overview

[1342] 1. Information content analysis

[1343] The camera in the smart glasses captures the information content that the user is viewing, converts the image data into text data using an OCR library, and then transmits the text data to a server using a network library.

[1344] 2. Analysis on the server

[1345] The server passes the received text data to an AI analysis module to assess the likelihood of it being fake news, comparing it with a database of trusted sources. The AI ​​analysis module uses a trained model to assess the reliability of the information and obtains a verdict.

[1346] 3. Emotional awareness and alert regulation

[1347] The server sends the result of the assessment back to the smart glasses. At the same time, the smart glasses' microphone and camera capture the user's facial expressions and tone of voice, and an emotion recognition library is used to analyze the user's emotions. The strength of the warning is adjusted based on this emotional data. For example, if the user is feeling anxious or stressed, the warning will be displayed in a more gentle manner, and if the user is calm, the warning will be displayed in a more normal manner.

[1348] 4. Warning Display

[1349] The smart glasses display a warning message saying "This may be fake news." The display format of the warning message is adjusted according to the user's emotional state.

[1350] Examples of concrete examples and prompts

[1351] Specific examples

[1352] When a user is wearing smart glasses and browsing a news site, the following happens:

[1353] 1. The displayed content of a news article is captured by the camera in the smart glasses.

[1354] 2. The captured image is converted to text using an OCR library.

[1355] 3. The converted text data is sent to a server, where an AI analysis module evaluates the likelihood of it being fake news.

[1356] 4. The smart glasses' camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the user's emotions.

[1357] 5. Based on the fake news judgment results and emotional data, a warning message will be displayed on the smart glasses display.

[1358] Prompt Sentence Examples

[1359] Use the following prompt to ask the generative AI model to analyze a news article:

[1360] Rate the following news articles as to whether they are fake news or not.

[1361] News article title: [Title]

[1362] News article content: [Content]

[1363] Comparison with reliable primary information sites: [Comparison results]

[1364] Please assess the possibility of fake news based on the verification results.

[1365] Using this prompt, the generative AI model can analyze news articles and determine whether they are fake news.

[1366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1367] Step 1:

[1368] A user wears smart glasses and visually views information content. The camera in the smart glasses captures the information content the user is viewing. The input at this stage is the image data captured by the camera, and the output is the image data.

[1369] Step 2:

[1370] The device uses an OCR library (e.g., Tesseract OCR) to convert the captured image into text data. The input is the captured image data, and the output is the text data extracted from the image. Specifically, the OCR library detects each character or word and converts it into text format.

[1371] Step 3:

[1372] The device uses a network library (e.g., Retrofit) to send the parsed text data to the server. The input is the text data obtained by OCR, and the output is the text data sent to the server. Specifically, the network library generates an HTTP request and sends the text data as a payload to the server.

[1373] Step 4:

[1374] The server passes the received text data to an AI analysis module (e.g., a generative AI model) to evaluate the likelihood of it being fake news. The input is the text data sent to the server, and the output is the result of the fake news judgment. Specifically, the generative AI model refers to the data it has been trained on and compares it with reliable sources to calculate the probability. The following prompt sentences are used:

[1375] Rate the following news articles as to whether they are fake news or not.

[1376] News article title: [Title]

[1377] News article content: [Content]

[1378] Comparison with reliable primary information sites: [Comparison results]

[1379] Please assess the possibility of fake news based on the verification results.

[1380] Step 5:

[1381] The server sends the fake news judgment result to the terminal. The input is the judgment result from the AI ​​analysis module, and the output is the judgment result sent to the terminal. Specifically, the server returns the judgment result to the terminal as an HTTP response.

[1382] Step 6:

[1383] The device uses the camera and microphone of the smart glasses to capture the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[1384] Step 7:

[1385] The device uses an emotion recognition library (e.g., Affectiva SDK) to recognize the user's emotions from the captured facial and voice data. The input is the captured facial and voice data, and the output is the analyzed emotion data. Specifically, the emotion recognition library uses a facial expression analysis algorithm to evaluate the user's emotional state.

[1386] Step 8:

[1387] The device adjusts the strength of the warning based on the fake news judgment result and emotional data. The input is the judgment result and emotional data, and the output is a warning message with adjusted strength. Specifically, the emotion engine changes the tone and strength of the warning depending on the user's stress level.

[1388] Step 9:

[1389] The terminal displays a warning message on the display of the smart glasses. The input is a warning message with adjusted intensity, and the output is a visually displayed warning message. Specifically, the warning "This may be fake news" is displayed on the display in a softer or normal form depending on the user's emotion.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] [Fourth embodiment]

[1394] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1395] 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.

[1396] 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).

[1397] 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.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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."

[1407] overview

[1408] This system analyzes information content viewed by users on social media applications or web browsers in real time and displays a warning if the content is suspected to be fake news. In particular, it uses a database of trusted primary information sites to collate the information content and identify fake news.

[1409] Specific Embodiments

[1410] 1. User installs the app

[1411] Users install a dedicated application on their smartphone, and when they launch the app for the first time, it requests necessary permissions, including permission to access data from social networking applications and web browsers.

[1412] 2. Monitoring social media content

[1413] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[1414] 3. Sending to the server for content analysis

[1415] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata.

[1416] 4. AI analysis on the server

[1417] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[1418] 5. Identifying fake news

[1419] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[1420] 6. Warning Display

[1421] Based on the result of the judgment received from the server, the device displays a warning to the user, which is displayed as a message saying "This may be fake news."

[1422] Specific examples of processing

[1423] Example 1: A user views a news article on a social networking site

[1424] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[1425] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[1426] The terminal transmits the acquired text content to the server.

[1427] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[1428] The server inputs the received content into the AI ​​analysis module for analysis.

[1429] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[1430] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[1431] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[1432] Example 2: Disseminating disaster information during emergencies

[1433] A user views disaster information on a social networking application.

[1434] The terminal acquires the text content of the disaster information and sends it to the server.

[1435] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[1436] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[1437] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[1438] This provides a system that reduces the risk of users believing incorrect information and enables them to act based on accurate information. This system is particularly effective in times of disaster or emergency, and contributes to improving the reliability of society as a whole.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] A user installs an app on their smartphone. When the app is launched for the first time, it requests necessary permissions (such as access to the data folder of a social networking application or web browser).

[1442] Step 2:

[1443] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[1444] Step 3:

[1445] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[1446] Step 4:

[1447] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[1448] Step 5:

[1449] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[1450] Step 6:

[1451] The server passes the received information content to the analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[1452] Step 7:

[1453] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[1454] Step 8:

[1455] The server generates a judgment result and returns it to the device, which includes a flag indicating whether the news is likely to be fake.

[1456] Step 9:

[1457] The device analyzes the results received from the server. If the news is likely to be fake, the device displays a warning message to the user. The warning message clearly states, "This may be fake news."

[1458] Step 10:

[1459] Users check the warning message and reassess the reliability of the information, thereby reducing the risk of believing incorrect information.

[1460] Example 1

[1461] 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."

[1462] In modern society, fake news and false information are increasingly being spread via the Internet. This puts users at risk of believing incorrect information, which can have serious consequences, especially during emergencies and disasters. However, it is difficult for users to verify the veracity of this information themselves. There is also a need for a quick means to make decisions based on reliable information. Therefore, the present invention provides a system that analyzes the information content viewed by users in real time and displays a warning if the content is likely to be fake news. This reduces the risk of users believing incorrect information and enables them to make decisions based on accurate information.

[1463] 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.

[1464] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for comparing the information content with reliable primary information sites, means for the server to transmit the fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for monitoring temporary files of SNS applications and web browsers, and means for automatically transmitting the analyzed information content and processing it in the background. This makes it possible to quickly analyze the authenticity of information content viewed by a user in real time and issue a warning about information that is likely to be fake news.

[1465] "User" refers to a person who uses this system to view information content using a social networking application or web browser.

[1466] "Information content" refers to digital information such as text, images, and videos that users view through social networking applications or web browsers.

[1467] "Means of analysis" refers to the techniques and methods used to analyze acquired information content and understand its contents.

[1468] "Server" refers to a central processing unit that analyzes received information content and evaluates and determines whether it is fake news.

[1469] "Means for transmitting" refers to the technology or method for transmitting the analyzed information content from the terminal to the server.

[1470] "Fake news" refers to news or information that is false or misleading and does not correspond to accurate and reliable primary sources.

[1471] A "trusted primary information site" refers to a government agency, public institution, or other trusted source of information.

[1472] "Means of matching" refers to the technology or method used to compare the analyzed information content with the database of a trusted primary information site to confirm the degree of consistency.

[1473] "Determination result" refers to the evaluation result of whether the information content is fake news, obtained as a result of analysis and comparison by the server.

[1474] "Means for displaying a warning" refers to technologies and methods for notifying users that the information content they are viewing may be fake news.

[1475] "SNS Application" refers to software that provides a social networking service that enables users to share information with other users.

[1476] A "web browser" refers to software that allows users to view web pages on the Internet.

[1477] "Temporary files" refer to data files used by social networking applications and web browsers for short periods of time.

[1478] "Means processed in the background" refers to techniques and methods for automatically processing data within the system without interfering with user operations.

[1479] MODE FOR CARRYING OUT THE INVENTION

[1480] overview

[1481] This invention is a system that analyzes information content viewed by users in social media applications or web browsers in real time and displays a warning if there is a possibility of fake news. This system collates information content using a database of reliable primary information sites to identify fake news.

[1482] The user installs the app

[1483] Users install a dedicated fake news detection application on their smartphone. When the application is launched for the first time, it requests the necessary permissions to access data from social media applications and web browsers, allowing the application to access the necessary data.

[1484] The device monitors social media content

[1485] The device monitors temporary files generated by social media applications and web browsers in real time. Specifically, it monitors specific folders and cache data on the file system, and detects new files as content to be analyzed when they are created. This function detects content such as text, images, and videos viewed by the user in real time.

[1486] The device sends the content to the server

[1487] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and is uninterrupted during user operations. Specifically, metadata such as text content, URLs, and article titles are sent to the server.

[1488] Data reception on the server and AI analysis

[1489] The server receives the information content sent from the device and passes the received data to the AI ​​analysis module. During this process, the data is verified and organized. For example, invalid or incomplete data is removed and formatted for analysis. The server then uses the AI ​​analysis module to analyze the information content. A pre-trained generative AI model is used for the analysis to assess the likelihood of fake news. During this process, the reliability of the information is assessed by comparing it with a database of trusted primary information sites.

[1490] The server determines fake news

[1491] The server determines the possibility of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high possibility of fake news, it returns the result to the device as a warning notification. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[1492] The device displays a warning to the user

[1493] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user can confirm the message and reduce the risk of believing false information.

[1494] Specific examples of processing and prompt statements

[1495] Example 1: When a user browses a news article on a social networking site

[1496] When a user clicks on a news article link in a social networking application, the browser opens the page of the link.

[1497] An application on the device monitors the browser folder and automatically retrieves the text content of opened news articles.

[1498] The device sends the acquired text content and related metadata (title, URL, etc.) to the server.

[1499] The server passes the received data to the AI ​​analysis module for analysis.

[1500] Specifically, a prompt such as "Is this news true?" is fed into the generative AI model, which then matches it with matching primary information sites.

[1501] If the server determines that the news is likely fake, it sends the result of the determination and the reason (e.g., "This news article does not match the official information site") to the device.

[1502] Based on the results received, the device displays a warning message to the user saying, "This may be fake news."

[1503] Example 2: Viewing disaster information during an emergency

[1504] A user views disaster information on a social networking application.

[1505] The device acquires the text content of the disaster information and related metadata and transmits it to the server.

[1506] The server passes the received information to an AI analysis module, which compares it with reliable primary information sites (e.g., the official website of the Japan Meteorological Agency or the official website of a local government).

[1507] If the server determines that the information is incorrect, it returns the determination result and reason (e.g., "Does not match official information") to the terminal.

[1508] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[1509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1510] Step 1:

[1511] Users install the fake news detection application on their smartphones and grant the necessary permissions when they first launch it, including permission to access data from social media applications and web browsers.

[1512] Input: User operation, first launch of application

[1513] Output: Application gets required permissions

[1514] Step 2:

[1515] The device monitors temporary files generated by social networking applications and web browsers in real time. This monitoring detects the information content (text, images, videos, etc.) viewed by the user. Specifically, it monitors designated folders and cache data, and retrieves the contents of new files as soon as they are created.

[1516] Input: Temporary files generated by social networking applications and web browsers

[1517] Output: Detected information content (text, images, videos, etc.)

[1518] Step 3:

[1519] The device automatically transmits the detected information content to the server. This transmission process occurs in the background and does not interfere with the user's operations. Specifically, metadata such as text content, URLs, and article titles are formatted and sent to the server.

[1520] Input: Detected information content (text, images, videos, etc.), associated metadata (title, URL, etc.)

[1521] Output: Formatted data sent to the server

[1522] Step 4:

[1523] The server receives the information content sent from the device, verifies and organizes the data, removing invalid or incomplete data and formatting it for analysis, before passing the received data to the AI ​​analysis module.

[1524] Input: Formatted data sent from the terminal

[1525] Output: Organized data passed to the AI ​​analysis module

[1526] Step 5:

[1527] The server uses an AI analysis module to analyze the information content. It then uses a generative AI model to evaluate the reliability of the information. This analysis process involves matching the information with a database of trusted primary information sites. Specifically, a prompt such as "Is this news true?" is input into the generative AI model to calculate the degree of match.

[1528] Input: Organized data, database of reliable primary information sites

[1529] Output: AI analysis results (score, evaluation reason)

[1530] Step 6:

[1531] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, it returns the result and the reason to the device. Specifically, it generates a judgment result that includes the score obtained from the AI ​​analysis module and the reason for the evaluation.

[1532] Input: AI analysis results (score, evaluation reason)

[1533] Output: The result of the judgment sent to the device

[1534] Step 7:

[1535] The device displays a warning to the user based on the judgment result received from the server. The warning is displayed on the user's screen as a message saying "This may be fake news." The user confirms the message, reducing the risk of believing false information.

[1536] Input: Verification result from the server

[1537] Output: A warning message that is displayed to the user.

[1538] Through the above processing steps, users can quickly analyze the authenticity of the information content they are viewing in real time and receive warnings about information that is likely to be fake news.

[1539] (Application example 1)

[1540] 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."

[1541] Traditionally, a lot of information has been distributed through social media applications and web browsers, and this information often contains fake news. This fake news can mislead and confuse users. Furthermore, in emergencies and disasters, the rapid spread of false information can have a significant impact on society as a whole. Conventional fake news countermeasures have tended to be reactive, making it difficult to provide users with fake news warnings in real time. Therefore, there is a need for a system that can analyze the information content viewed by users and warn them of possible fake news in real time.

[1542] 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.

[1543] In this invention, the server includes means for analyzing information content viewed by a user, means for transmitting the analyzed information content to the server, means for the server to analyze the possibility of fake news based on the information content, means for the server to transmit a fake news determination result to the terminal, means for the terminal to display a warning to the user based on the determination result, means for referencing a database of reliable information sources and comparing the information content, means for using a generative AI model for fake news determination, means for generating a prompt sentence for the generative AI model, means for the terminal to obtain temporary files from a monitored application, and means for transmitting and analyzing data in the background. This enables accurate analysis in real time and enables immediate evaluation of the reliability of the information viewed by the user.

[1544] "Information content viewed by users" refers to digital information such as articles, posts, images, and videos that users view through social media applications or web browsers.

[1545] "Analysis tools" refers to the software or algorithms used to analyze information content and understand its content and structure.

[1546] "Means for transmitting to the server" refers to the network communication protocol or interface for transferring data from the user's terminal to the server.

[1547] "Measures for analyzing potential fake news" refers to algorithms or AI models used to assess whether information content is false or not.

[1548] "Means for transmitting fake news determination results to the terminal" refers to a communication protocol for informing the terminal of the analysis results from the server.

[1549] "Means for displaying a warning to the user" refers to an interface or notification system for displaying a warning message to the user when there is a high possibility of fake news.

[1550] A "database of trusted sources" is a database that compiles information from official institutions and reliable information sources.

[1551] A "generative AI model" is an AI model that is trained using large datasets and optimized to perform a specific task.

[1552] A "prompt" is an input text given to a generative AI model to perform a specific analysis or judgment.

[1553] "Temporary files" are temporary data files generated while using a social networking application or web browser.

[1554] "Means for transmitting and analyzing data in the background" refers to a system or process that automatically transmits and analyzes data in the background while the user is working.

[1555] System Overview

[1556] The present invention is a system that monitors information content viewed by users through social media applications and web browsers in real time and displays a warning when there is a high possibility that the content is fake news. The system has the following main functions:

[1557] 1. User installs the app

[1558] The user installs a dedicated application on their smartphone, and upon first launch, the application requests necessary permissions, including permission to access data from social networking applications and web browsers.

[1559] 2. Acquiring and monitoring information content

[1560] The user's device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[1561] 3. Data transmission

[1562] The device then transmits the detected information content to the server. This transmission process occurs automatically in the background and does not interfere with the user's operations. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[1563] 4. Analysis on the server

[1564] The server analyzes the received information content using a generative AI model, which assesses the reliability of the information by checking it against a database of pre-trusted sources.

[1565] 5. Fake news detection and notification

[1566] If the server determines that the news is likely fake, it sends the result to the device, which then displays a warning message to the user, stating, "This may be fake news."

[1567] Hardware and Software Used

[1568] Hardware: Smartphone (Android or iOS)

[1569] Software: Python, TensorFlow, Firebase

[1570] Database: A database of trusted sources

[1571] Data processing and calculation

[1572] The server analyzes the information content sent by the user in the following steps and generates an appropriate warning.

[1573] 1. Data Acquisition:

[1574] Monitors temporary files from social media applications and web browsers and captures text data and metadata.

[1575] 2. Data transmission and analysis:

[1576] Send the acquired data to the server (Firebase).

[1577] The data is analyzed using an AI analysis module (using TensorFlow) on the server and compared with a database of trusted information sources.

[1578] 3. Sending the results and warnings:

[1579] If there is a high possibility that the news is fake, the analysis results are sent to the user's device and a warning message is displayed on the device.

[1580] Specific examples

[1581] For example, if a user is viewing a news article on social media that says "A large typhoon will hit tonight," they would follow these steps:

[1582] The application monitors the text of news articles and sends it to a server.

[1583] The AI ​​model on the server analyzes the news article and compares it with information from trusted sources (e.g., meteorological agencies).

[1584] If the app determines that the news is likely to be fake, it will warn the user, saying, "This may be fake news."

[1585] Prompt Sentence Examples

[1586] content = "A large typhoon will hit tonight"

[1587] metadata = {"title": "Typhoon News", "url": "example.com / typhoon_news"}

[1588] result = check_fakenews(content, metadata)

[1589] This sentence shows an example of inputting the text and metadata of a news article into an AI model, and then determining whether it is fake news based on the output.

[1590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1591] Step 1:

[1592] A user views information content through a social networking application or a web browser.

[1593] Input: Information content (news articles, posts, images, videos, etc.)

[1594] Output: None

[1595] How it works: When a user browses information using a social networking application or web browser on their smartphone, the content of this information is monitored by the application.

[1596] Step 2:

[1597] The device monitors temporary files of social networking applications and web browsers and obtains text data and metadata of information content.

[1598] Input: Temporary file (text data, article title, URL, etc.)

[1599] Output: Retrieved information content and metadata

[1600] How it works: An application on the device monitors temporary files in real time and automatically extracts text data and related metadata from the information content viewed.

[1601] Step 3:

[1602] The terminal transmits the acquired information content and metadata to the server.

[1603] Input: Information content and metadata

[1604] Output: Send data to the server

[1605] How it works: The device sends information content and metadata to the server in the background, without interfering with the user's operations.

[1606] Step 4:

[1607] The server analyzes the received information content using a generative AI model.

[1608] Input: Submitted information content and metadata

[1609] Output: Analysis results (possibility of fake news)

[1610] How it works: The server inputs the received data into an AI analysis module (using TensorFlow), which evaluates the reliability of the information based on a generative AI model, compares it with a database of trusted sources, and calculates the likelihood of it being fake news.

[1611] Step 5:

[1612] The server transmits the determination result to the user's terminal.

[1613] Input: Analysis results (possible fake news)

[1614] Output: Send the result to the user's device

[1615] How it works: Based on the results of AI analysis, the server sends the fake news judgment result to the device, and this data is immediately notified to the user.

[1616] Step 6:

[1617] The terminal displays a warning message to the user based on the determination result received from the server.

[1618] Input: Verification result (high probability of fake news)

[1619] Output: Display a warning message

[1620] Specific operation: The device displays the warning message received from the server to the user, informing them of the warning content, such as "This may be fake news."

[1621] Operation in a specific example

[1622] For example, if a user sees a news article on social media that says, "A large typhoon will hit tonight,"

[1623] Step 1: User browses to a news article.

[1624] Step 2: The device retrieves the text data, article title, and URL.

[1625] Step 3: The device sends this data to the server.

[1626] Step 4: The server uses the generative AI model to analyze the news article and assess its likelihood of being fake news.

[1627] Step 5: The server determines that the news is likely fake and sends the result to the device.

[1628] Step 6: The device displays a warning message to alert the user.

[1629] 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.

[1630] overview

[1631] The present invention is a system that analyzes information content viewed by users in social networking applications or web browsers in real time and displays a warning when there is a possibility of fake news. It also combines an emotion engine that recognizes the user's emotions and adjusts the strength of the warning based on the user's emotions.

[1632] Specific Embodiments

[1633] 1. User installs the app

[1634] The user installs a dedicated application on their smartphone. When the application is launched for the first time, it requests necessary permissions (permission to access data folders of social networking applications and web browsers, and permission to use the emotion engine).

[1635] 2. Monitoring social media content

[1636] The device monitors temporary files generated by social networking applications and web browsers, detecting the information content (text, images, videos, etc.) viewed by the user in real time.

[1637] 3. Sending to the server for content analysis

[1638] The device transmits the detected information content to the server. This transmission process occurs automatically in the background. The transmitted data includes the text content of the information being viewed and associated metadata (e.g., article title and URL).

[1639] 4. AI analysis on the server

[1640] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses pre-trained models to assess the reliability of the information, which includes cross-checking it with a database of trusted primary information sites.

[1641] 5. Identifying fake news

[1642] The server determines the likelihood of fake news based on the evaluation results from the AI ​​analysis module. If the result indicates a high probability of fake news, the server sends a warning notification back to the device.

[1643] 6. Emotion Recognition by Emotion Engine

[1644] The device uses an emotion engine to recognize the user's emotions in real time, for example, by analyzing the user's facial expressions and tone of voice using the smartphone's camera and microphone to evaluate the user's emotional state.

[1645] 7. Adjusting warnings based on emotions

[1646] The device adjusts the intensity of the warning based on the emotion data from the emotion engine. For example, if the user is feeling anxious or stressed, the device displays a softer warning message. Conversely, if the user is calm, the device displays a normal warning message.

[1647] 8. Warning Display

[1648] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[1649] Specific examples of processing

[1650] Example 1: A user views a news article on a social networking site

[1651] When a user clicks on a news article link in a social networking application, the browser opens the content of the link.

[1652] An application on the device monitors the browser folder and retrieves the text content of opened news articles.

[1653] The terminal transmits the acquired text content to the server.

[1654] Specifically, it sends the text content and associated metadata (e.g., article title and URL).

[1655] The server inputs the received content into the AI ​​analysis module for analysis.

[1656] The analysis module compares content with a database of trusted primary information sites and calculates the probability of it being fake news.

[1657] If the server determines that the news is likely fake, it sends the result of that determination to the device.

[1658] The device uses an emotion engine to recognize the user's emotions, for example, assessing stress or relief from the user's facial expressions and tone of voice.

[1659] Based on the received judgment results and the evaluation results of the emotion engine, the device displays a warning message to the user saying, "This may be fake news."

[1660] If the user is feeling stressed, the message is displayed softly.

[1661] If the user is sober, the warning is displayed in the normal format.

[1662] Example 2: Disseminating disaster information during emergencies

[1663] A user views disaster information on a social networking application.

[1664] The terminal acquires the text content of the disaster information and sends it to the server.

[1665] The server analyzes the information and compares it with reliable primary information sites (for example, the official websites of the Japan Meteorological Agency or local governments).

[1666] If the server determines that the information is incorrect, it returns the determination result to the terminal.

[1667] The device recognizes the user's emotions and adjusts warning messages based on their state.

[1668] For example, in an emergency, if the user is already panicking, the warning will be calming.

[1669] The device will display a warning message to the user saying "This may be incorrect information" so that the user can make a decision based on accurate information.

[1670] This allows the user to reduce the risk of believing false information and act based on accurate information. Furthermore, by combining it with an emotion engine, it is possible to provide appropriate warnings tailored to the user's emotional state and adjust how information is received.

[1671] The processing flow will be explained below.

[1672] Step 1:

[1673] The user installs the app on their smartphone. When the app is launched for the first time, it requests the necessary permissions (permission to access the data folder of the social networking application and web browser, and permission to use the emotion engine).

[1674] Step 2:

[1675] The device monitors the data folders of social media applications and web browsers, detecting changes to and new creation of temporary files in real time.

[1676] Step 3:

[1677] A user views information content in a social networking application or web browser, for example by clicking a link in a news article to view the content.

[1678] Step 4:

[1679] The device automatically retrieves information content (e.g., the text content of a news article) from temporary files of a social networking application or web browser.

[1680] Step 5:

[1681] The terminal transmits the acquired information content to the server, including the text data of the information content and associated metadata (e.g., article title and URL).

[1682] Step 6:

[1683] The server passes the received information content to an AI analysis module, which uses a pre-trained model to evaluate the reliability of the content.

[1684] Step 7:

[1685] The server compares the information content with a database of trusted primary information sites based on the output of the AI ​​analysis module, and determines the possibility of it being fake news based on the comparison results.

[1686] Step 8:

[1687] The server generates a judgment result and sends it to the device, which includes a flag indicating the possibility of fake news.

[1688] Step 9:

[1689] The device uses an emotion engine to recognize the user's emotions in real time. The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice to assess the user's emotional state.

[1690] Step 10:

[1691] Based on the judgment results received by the device and the evaluation results of the emotion engine, the warning is displayed with appropriate intensity. For example, if the user is feeling stressed, the warning message is displayed softly. If the user is calm, the normal warning message is displayed.

[1692] Step 11:

[1693] The device displays a tailored warning message to the user, including the message "This may be fake news," but presented differently depending on the user's emotional state.

[1694] Step 12:

[1695] Users can check the warning message and reassess the reliability of the information, reducing the risk of believing false information. Furthermore, alerts tailored to the user's emotional state optimize how they receive information.

[1696] Example 2

[1697] 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."

[1698] The purpose of this invention is to detect fake news contained in information viewed by users on the Internet in real time and issue appropriate warnings. Furthermore, by providing flexible warning messages that are tailored to the user's emotional state, the problem of providing truthful information while reducing user stress is solved.

[1699] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an AI analysis means, a means for maintaining a database of reliable information sources, and a means for collating information content. This makes it possible to analyze the reliability of information content viewed by a user in real time, identify information that is likely to be fake news, and further adjust the strength of a warning message based on the emotional state of the user.

[1700] "User" means an individual or end user who uses the System.

[1701] A "terminal" is a device used by a user, and includes a smartphone, tablet, PC, etc.

[1702] "Information content" refers to data such as text, images, and videos that users view via the Internet.

[1703] The "means for analyzing in real time" is a function or device for instantly analyzing the information content that the user is viewing on the spot.

[1704] A "server" is a computer system on a network for analyzing and processing information.

[1705] "AI analysis" is an analytical method that uses artificial intelligence technology to evaluate the reliability of information content.

[1706] "Reliable sources" refer to highly accurate information provided by public institutions and authoritative media.

[1707] A "database" is a collection of information that is systematically stored and made easily accessible and collated.

[1708] An "emotion engine" is software or an algorithm that recognizes and evaluates a user's emotional state from facial expressions, tone of voice, etc.

[1709] The "means for adjusting the intensity of the warning" is a function or device for changing the content and expression of the warning message according to the emotional state of the user.

[1710] MODE FOR CARRYING OUT THE INVENTION

[1711] overview

[1712] This system analyzes the information content a user browses online in real time to detect possible fake news. It also recognizes the user's emotional state and adjusts the intensity of warning messages based on that emotion. The system aims to provide accurate information while reducing user stress.

[1713] User installs the app

[1714] Users install a dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. This allows the application to access the necessary data.

[1715] Social media content monitoring

[1716] The device monitors temporary files generated by social media applications and web browsers by periodically checking the data folder for new or updated files and retrieving their contents.

[1717] Send to server for content analysis

[1718] The device encrypts the acquired information content and sends it to the server. The data sent includes the text content of the information being viewed and related metadata (e.g., article title and URL). This allows data to be sent in the background without the user's knowledge.

[1719] AI analysis on the server

[1720] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news, which uses a pre-trained generative AI model to compare the received content with a database of trusted sources to calculate the probability of it being fake news.

[1721] Fake news detection

[1722] The server determines the likelihood that the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined that the information content is likely to be fake news, the server returns the determination result to the device.

[1723] Emotion recognition by emotion engine

[1724] The device activates an emotion engine to recognize the user's emotions in real time. Specifically, it analyzes the user's facial expressions and tone of voice via the smartphone's camera and microphone to assess their emotional state.

[1725] Adjusting alerts based on emotion

[1726] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user feels anxious or stressed, the device displays the warning message in a softer tone, and if the user feels calm, the device displays the warning message in a normal tone.

[1727] Displaying warnings

[1728] The device displays a tailored warning message to the user, which may include the content "This may be fake news," but may be displayed differently depending on the user's emotional state.

[1729] Specific examples

[1730] An example of a specific prompt is as follows:

[1731] "This system analyzes information content viewed on social media and web browsers in real time and displays a warning to users if it detects possible fake news. It also recognizes the user's emotional state and adjusts the strength of the warning based on their emotions. Specifically, it monitors the data folders of social media applications and web browsers, sends the detected content to a server, and an AI analysis module evaluates the possibility of it being fake news. It then adjusts and displays a warning message according to the user's emotional state."

[1732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1733] Step 1:

[1734] The user installs the dedicated application on their smartphone. When the application is launched for the first time, it requests permission to access the data folders of social networking applications and web browsers, as well as permission to use the emotion engine. Once the user grants these permissions, the application is able to access the necessary data.

[1735] Input: User installation operations, access permissions

[1736] Output: App initial setup complete, necessary permissions obtained

[1737] Step 2:

[1738] The device monitors temporary files generated by social media applications and web browsers. Applications periodically check the data folder for new or updated files and retrieve their contents. For example, the monitoring software may scan the data folder every second.

[1739] Input: Temporary files from social networking applications and web browsers

[1740] Output: New information content detected (text, images, videos, etc.)

[1741] Step 3:

[1742] The device then transmits the retrieved information content to the server. This transmission process occurs automatically in the background, and the transmitted data includes the text content of the information and associated metadata (e.g., article title and URL). Specifically, the device encrypts the data and transmits it via a secure communications protocol.

[1743] Input: Detected information content

[1744] Output: The encrypted information content is sent to the server

[1745] Step 4:

[1746] The server passes the received information content to an AI analysis module to assess its likelihood of being fake news. The AI ​​analysis module uses a pre-trained generative AI model to compare it with a database of trusted sources. Specifically, the analysis module runs an algorithm to calculate the reliability of the information.

[1747] Input: Encrypted information content

[1748] Output: Assessment result on the likelihood of fake news

[1749] Step 5:

[1750] The server determines whether the information content is fake news based on the evaluation results from the AI ​​analysis module. If it is determined to be fake news, the server sends the result of that judgment to the device. Specifically, the server analyzes the evaluation results and generates an appropriate warning message.

[1751] Input: Evaluation results from the AI ​​analysis module

[1752] Output: Fake news detection result, warning message

[1753] Step 6:

[1754] The device activates an emotion engine to recognize the user's emotions in real time. It analyzes the user's facial expressions and tone of voice through the smartphone's camera and microphone to assess their emotional state. Specifically, the emotion recognition software uses image processing and voice analysis algorithms.

[1755] Input: User's facial expressions, tone of voice

[1756] Output: User's emotional data (stress, relief, etc.)

[1757] Step 7:

[1758] The device adjusts the intensity of the warning based on the emotion data obtained from the emotion engine. If the user is feeling anxious or stressed, the warning message is displayed in a softer tone, and if the user is calm, the warning message is displayed in a normal format. Specifically, the device dynamically changes the content and expression of the warning message based on the emotion data.

[1759] Input: User emotion data, warning message

[1760] Output: Adjusted warning message

[1761] Step 8:

[1762] The device then displays a tailored warning message to the user, which includes the message "This may be fake news," but is presented differently depending on the user's emotional state. Specifically, the message appears on the screen for the user to immediately acknowledge.

[1763] Input: Adjusted warning message

[1764] Output: The warning message displayed to the user.

[1765] (Application example 2)

[1766] 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."

[1767] In today's information society, there is a high possibility that fake news is included in the information content that users visually view, and there are risks associated with believing and acting on that information. Furthermore, viewing fake news while in an emotionally unstable state can have a particularly significant impact on users. Therefore, a system is needed that analyzes the content viewed by users in real time and issues appropriate warnings when there is a possibility of fake news. Furthermore, it is necessary to ensure that warnings are appropriately accepted by adjusting the wording of the warning according to the user's emotional state.

[1768] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing information content visually viewed by the user, means for transmitting the analyzed information content to the server, and means for the server to analyze the possibility of fake news based on the information content. This makes it possible to analyze the content viewed by the user in real time and display a warning if there is a high possibility that it is fake news. In addition, the display device includes means for recognizing the user's emotions using an emotion engine and means for adjusting the strength of the warning based on the user's emotions, making it possible to issue an appropriate warning according to the user's emotional state.

[1769] "User" refers to a person who uses this system.

[1770] "Visually viewed information content" refers to digital information such as text, images, and videos that can be visually viewed by a user.

[1771] "Means for analyzing" refers to a method or apparatus for analyzing visually viewed information content.

[1772] "Server" refers to a computer system capable of receiving analyzed information content and transmitting the results of the analysis.

[1773] "Means for sending" refers to a method or apparatus for sending the parsed information content to a server.

[1774] "Fake news" refers to news that contains intentionally false information.

[1775] "Decision result" refers to the evaluation result after the server analyzes the possibility of fake news.

[1776] "Display device" refers to a device for visually displaying analysis results and warning messages to a user.

[1777] "Means for displaying a warning" refers to a method or device for warning users of possible fake news.

[1778] An "emotion engine" refers to an algorithm or software for analyzing a user's emotions.

[1779] "Means for recognizing emotions" refers to a method or device for detecting the emotional state of a user.

[1780] "Means for adjusting the intensity of an alert" refers to a method or apparatus for adjusting the wording or content of an alert based on the user's emotional state.

[1781] A "reliable source" is a source that provides accurate and reliable information.

[1782] "Source database" means a digital database for storing information collected from reliable sources.

[1783] "Means for matching" refers to a method or device for comparing the analyzed information content with a database of information sources to identify matches and discrepancies.

[1784] "Temporary files" refer to data files that are temporarily stored and used by social networking applications and web browsers.

[1785] "Means for monitoring" refers to a method or device for periodically checking the contents of temporary files and extracting necessary information.

[1786] "Optical character recognition" refers to techniques and devices for extracting text information from image data.

[1787] This invention is a system that analyzes information content visually viewed by users in real time and displays a warning when there is a possibility of fake news. In particular, it combines a function that recognizes the user's emotions using an emotion engine and adjusts the strength of the warning.

[1788] Hardware and Software Examples

[1789] Hardware

[1790] Smart Glasses: As an example, we use general-purpose smart glasses, which have a camera to capture the information content that the user visually views, and a built-in microphone to recognize the user's facial expressions.

[1791] Server: Uses sophisticated computer systems to analyze information content and assess potential fake news.

[1792] software

[1793] OCR library: Using Tesseract OCR as an example, we extract text information from image data captured by the camera in smart glasses.

[1794] Network library: Using Retrofit as an example, we send parsed text data to a server.

[1795] Emotion Recognition Library: As an example, we will use the Affectiva SDK to analyze emotions from the user's facial expressions and tone of voice.

[1796] AI analysis module: Evaluates the reliability of information content using a server-side built-in AI model, which includes pre-trained generative AI.

[1797] Process Overview

[1798] 1. Information content analysis

[1799] The camera in the smart glasses captures the information content that the user is viewing, converts the image data into text data using an OCR library, and then transmits the text data to a server using a network library.

[1800] 2. Analysis on the server

[1801] The server passes the received text data to an AI analysis module to assess the likelihood of it being fake news, comparing it with a database of trusted sources. The AI ​​analysis module uses a trained model to assess the reliability of the information and obtains a verdict.

[1802] 3. Emotional awareness and alert regulation

[1803] The server sends the result of the assessment back to the smart glasses. At the same time, the smart glasses' microphone and camera capture the user's facial expressions and tone of voice, and an emotion recognition library is used to analyze the user's emotions. The strength of the warning is adjusted based on this emotional data. For example, if the user is feeling anxious or stressed, the warning will be displayed in a more gentle manner, and if the user is calm, the warning will be displayed in a more normal manner.

[1804] 4. Warning Display

[1805] The smart glasses display a warning message saying "This may be fake news." The display format of the warning message is adjusted according to the user's emotional state.

[1806] Examples of concrete examples and prompts

[1807] Specific examples

[1808] When a user is wearing smart glasses and browsing a news site, the following happens:

[1809] 1. The displayed content of a news article is captured by the camera in the smart glasses.

[1810] 2. The captured image is converted to text using an OCR library.

[1811] 3. The converted text data is sent to a server, where an AI analysis module evaluates the likelihood of it being fake news.

[1812] 4. The smart glasses' camera and microphone capture the user's facial expressions and voice, and the emotion engine analyzes the user's emotions.

[1813] 5. Based on the fake news judgment results and emotional data, a warning message will be displayed on the smart glasses display.

[1814] Prompt Sentence Examples

[1815] Use the following prompt to ask the generative AI model to analyze a news article:

[1816] Rate the following news articles as to whether they are fake news or not.

[1817] News article title: [Title]

[1818] News article content: [Content]

[1819] Comparison with reliable primary information sites: [Comparison results]

[1820] Please assess the possibility of fake news based on the verification results.

[1821] Using this prompt, the generative AI model can analyze news articles and determine whether they are fake news.

[1822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1823] Step 1:

[1824] A user wears smart glasses and visually views information content. The camera in the smart glasses captures the information content the user is viewing. The input at this stage is the image data captured by the camera, and the output is the image data.

[1825] Step 2:

[1826] The device uses an OCR library (e.g., Tesseract OCR) to convert the captured image into text data. The input is the captured image data, and the output is the text data extracted from the image. Specifically, the OCR library detects each character or word and converts it into text format.

[1827] Step 3:

[1828] The device uses a network library (e.g., Retrofit) to send the parsed text data to the server. The input is the text data obtained by OCR, and the output is the text data sent to the server. Specifically, the network library generates an HTTP request and sends the text data as a payload to the server.

[1829] Step 4:

[1830] The server passes the received text data to an AI analysis module (e.g., a generative AI model) to evaluate the likelihood of it being fake news. The input is the text data sent to the server, and the output is the result of the fake news judgment. Specifically, the generative AI model refers to the data it has been trained on and compares it with reliable sources to calculate the probability. The following prompt sentences are used:

[1831] Rate the following news articles as to whether they are fake news or not.

[1832] News article title: [Title]

[1833] News article content: [Content]

[1834] Comparison with reliable primary information sites: [Comparison results]

[1835] Please assess the possibility of fake news based on the verification results.

[1836] Step 5:

[1837] The server sends the fake news judgment result to the terminal. The input is the judgment result from the AI ​​analysis module, and the output is the judgment result sent to the terminal. Specifically, the server returns the judgment result to the terminal as an HTTP response.

[1838] Step 6:

[1839] The device uses the camera and microphone of the smart glasses to capture the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the captured facial expression and voice data.

[1840] Step 7:

[1841] The device uses an emotion recognition library (e.g., Affectiva SDK) to recognize the user's emotions from the captured facial and voice data. The input is the captured facial and voice data, and the output is the analyzed emotion data. Specifically, the emotion recognition library uses a facial expression analysis algorithm to evaluate the user's emotional state.

[1842] Step 8:

[1843] The device adjusts the strength of the warning based on the fake news judgment result and emotional data. The input is the judgment result and emotional data, and the output is a warning message with adjusted strength. Specifically, the emotion engine changes the tone and strength of the warning depending on the user's stress level.

[1844] Step 9:

[1845] The terminal displays a warning message on the display of the smart glasses. The input is a warning message with adjusted intensity, and the output is a visually displayed warning message. Specifically, the warning "This may be fake news" is displayed on the display in a softer or normal form depending on the user's emotion.

[1846] 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.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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).

[1853] 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.

[1854] 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."

[1855] 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.

[1856] 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).

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] The following is further disclosed regarding the above embodiment.

[1868] (Claim 1)

[1869] means for analyzing information content viewed by a user;

[1870] means for transmitting the parsed information content to a server;

[1871] A means for the server to analyze the possibility of fake news based on the information content;

[1872] A means for the server to transmit the fake news determination result to the terminal;

[1873] The system includes a means for the terminal to display a warning to the user based on the determination result.

[1874] (Claim 2)

[1875] a means of maintaining a database of reliable primary information sites;

[1876] 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

[1877] (Claim 3)

[1878] 10. The system of claim 1, further comprising means for monitoring temporary files of a social networking application or a web browser.

[1879] "Example 1"

[1880] (Claim 1)

[1881] means for analyzing information content viewed by a user;

[1882] means for transmitting the parsed information content to a server;

[1883] A means for the server to analyze the possibility of fake news based on the information content;

[1884] A means of cross-checking with reliable primary information sites;

[1885] A means for the server to transmit the fake news determination result to the terminal;

[1886] The system includes a means for the terminal to display a warning to the user based on the determination result.

[1887] (Claim 2)

[1888] a means of maintaining a database of reliable primary information sites;

[1889] 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

[1890] (Claim 3)

[1891] A means of monitoring temporary files from social networking applications and web browsers,

[1892] 10. The system of claim 1, further comprising means for automatically transmitting the parsed information content for background processing.

[1893] "Application Example 1"

[1894] (Claim 1)

[1895] means for analyzing information content viewed by a user;

[1896] means for transmitting the parsed information content to a server;

[1897] A means for the server to analyze the possibility of fake news based on the information content;

[1898] A means for the server to transmit the fake news determination result to the terminal;

[1899] a means for the terminal to display a warning to a user based on the determination result;

[1900] a means of verifying information content against a database of reliable sources;

[1901] A method for using generative AI models to identify fake news;

[1902] a means for generating a prompt sentence for the generative AI model;

[1903] a means for retrieving temporary files from the monitored application on the device;

[1904] A system that includes a means for background data transmission and analysis.

[1905] (Claim 2)

[1906] a means of maintaining a database of reliable primary information sites;

[1907] 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

[1908] (Claim 3)

[1909] 10. The system of claim 1, further comprising means for monitoring temporary files of a social networking application or a web browser.

[1910] "Example 2: Combining Emotion Engines"

[1911] (Claim 1)

[1912] means for analyzing information content viewed by a user in real time;

[1913] means for transmitting the parsed information content to a server;

[1914] The server uses AI to analyze the possibility of fake news based on the information content,

[1915] A means for the server to transmit the fake news determination result to the terminal;

[1916] A means for the terminal to recognize the user's emotion;

[1917] A means for the terminal to adjust the intensity of the warning based on the user's emotion;

[1918] The system includes means for the terminal to display a tailored alert to the user.

[1919] (Claim 2)

[1920] a means of maintaining a database of reliable sources of information;

[1921] 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

[1922] (Claim 3)

[1923] 10. The system of claim 1, further comprising means for monitoring temporary files of an information application or a web browser.

[1924] "Application example 2 when combining emotion engines"

[1925] (Claim 1)

[1926] means for analyzing information content visually viewed by a user;

[1927] means for transmitting the parsed information content to a server;

[1928] A means for the server to analyze the possibility of fake news based on the information content;

[1929] A means for the server to transmit the fake news determination result to a display device;

[1930] a means for displaying a warning to a user based on the determination result by the display device;

[1931] means for recognizing a user's emotion using an emotion engine;

[1932] and means for adjusting the intensity of the alert based on the user's emotion.

[1933] (Claim 2)

[1934] a means of maintaining a database of reliable sources of information;

[1935] 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

[1936] (Claim 3)

[1937] A means of monitoring temporary files from social networking applications and web browsers,

[1938] 10. The system of claim 1, further comprising: optical character recognition means for capturing visually viewed information content. [Explanation of symbols]

[1939] 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. means for analyzing information content viewed by a user; means for transmitting the parsed information content to a server; A means for the server to analyze the possibility of fake news based on the information content; A means for the server to transmit the fake news determination result to the terminal; The system includes a means for the terminal to display a warning to the user based on the determination result.

2. a means of maintaining a database of reliable primary information sites; 10. The system of claim 1, further comprising means for identifying potential fake news by matching the analyzed information content with the database.

3. The system of claim 1 , further comprising means for monitoring temporary files of a social networking application or a web browser.

Images