system

Abstract

The system according to this embodiment aims to quickly and accurately evaluate and display the credibility of news articles and information from social media. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a display unit. The collection unit collects news articles and social media posts. The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. The display unit displays the credibility evaluation results from the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly and accurately evaluate the credibility of news articles and SNS information, and there is a problem that the spread of false reports and fake news cannot be prevented.

[0005] The system according to the embodiment aims to quickly and accurately evaluate and display the credibility of news articles and SNS information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a display unit. The collection unit collects news articles and SNS posts. The analysis unit analyzes the information collected by the collection unit and evaluates the credibility. The display unit displays the result of the credibility evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately evaluate and display the credibility of news articles and information from social media. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The news credibility evaluation system according to an embodiment of the present invention is a system that automatically verifies the credibility of news articles and information on social media using AI, thereby preventing the spread of misinformation and fake news. When a user views a news article or social media post, the information is automatically verified by the AI. The AI ​​utilizes natural language processing technology to analyze news articles and social media posts and evaluate their credibility. For example, it analyzes the content of news articles and the context of posts, and evaluates them by comparing them with reliable sources and historical data. Next, the reliability evaluation result from the AI ​​is immediately displayed on the user's screen. For example, the reliability evaluation result is displayed at the top of the news article, allowing the user to see at a glance whether the article is trustworthy. Similarly, the reliability evaluation result is displayed for social media posts. This system allows users to access accurate information without being misled by misinformation or fake news. For example, when a user is browsing a news site, the reliability of each article and related social media post is automatically evaluated, and the reliability evaluation result is displayed at the top of the article. This allows users to easily identify misinformation and fake news. Furthermore, this system uses cloud computing technology to efficiently analyze large amounts of data and evaluate the credibility of news in real time. This will allow the system to reach 10 million users within a year of its release, and increase revenue through advertising and premium features. Thus, the system, which uses AI to automatically verify the credibility of news articles and information on social media, aims to prevent the spread of misinformation and fake news by providing users with access to reliable information. In short, the news credibility evaluation system can provide users with access to reliable information and prevent the spread of fake news.

[0029] The news credibility evaluation system according to this embodiment comprises a collection unit, an analysis unit, and a display unit. The collection unit collects news articles and SNS posts. The collection unit collects information using APIs from news sites and SNS platforms, for example. The collection unit can also automatically collect news articles and SNS posts using web scraping technology. Furthermore, the collection unit can also collect information based on URLs and keywords provided by users. For example, the collection unit subscribes to RSS feeds of news sites and automatically collects the latest news articles. The collection unit can also collect posts related to specific hashtags or keywords using APIs of SNS platforms. The collection unit collects information from news sites and SNS platforms using web scraping technology and stores it in a database. The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. The analysis unit analyzes news articles and SNS posts using natural language processing technology, for example. The analysis unit can perform morphological analysis to analyze the structure of sentences. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of sentences. Furthermore, the analysis unit can perform semantic analysis, understand the meaning of text, and evaluate its credibility. For example, the analysis unit can analyze the content of a news article and evaluate its degree of consistency with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with past data. The analysis unit utilizes natural language processing technology to evaluate the credibility of news articles and social media posts with high accuracy. The display unit displays the credibility results evaluated by the analysis unit. For example, the display unit can display the credibility evaluation results at the top of a news article. The display unit can also display the credibility evaluation results for social media posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of news articles and social media posts so that users can check the credibility at a glance.As a result, the news credibility evaluation system according to the embodiment can automatically evaluate the credibility of news articles and social media posts, and prevent the spread of misinformation and fake news.

[0030] The data collection unit collects news articles and social media posts. For example, it uses APIs to collect information from news sites and social media platforms. Specifically, it uses news site APIs to periodically retrieve the latest news articles and store them in a database. It can also use social media platform APIs to collect posts related to specific hashtags or keywords in real time. Furthermore, the data collection unit can automatically collect news articles and social media posts using web scraping technology. Web scraping technology makes it possible to obtain information even from sites that do not provide APIs. For example, it can analyze the HTML structure of a specific news site and extract information such as article titles, body text, and publication dates. The data collection unit can also collect information based on URLs and keywords provided by users. If a user provides a URL for a specific news article, it accesses that URL, retrieves the article's content, and stores it in the database. In keyword-based collection, it searches for and collects related news articles and social media posts. For example, the data collection unit subscribes to news site RSS feeds and automatically collects the latest news articles. Using RSS feeds allows for the rapid acquisition of new articles whenever a news site is updated. The data collection unit can also use SNS platform APIs to collect posts related to specific hashtags or keywords. This allows the data collection unit to gather diverse data from a wide range of sources and provide foundational data for credibility assessment.

[0031] The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. For example, the analysis unit uses natural language processing techniques to analyze news articles and social media posts. Specifically, it can perform morphological analysis to analyze the structure of a text. Morphological analysis divides the text into words and identifies the part of speech and meaning of each word. This allows for an understanding of the basic structure of the text and lays the foundation for more detailed analysis. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of the text. Grammatical analysis evaluates whether the text is constructed according to correct grammar, and texts with many grammatical errors can be judged as having low credibility. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of the text and evaluate its credibility. Semantic analysis involves understanding the content of the text and checking whether specific keywords or phrases are included. For example, the analysis unit analyzes the content of a news article and evaluates its degree of agreement with reliable sources. Reliable sources include official news sites and announcements from government agencies. If there is a lot of agreement with these sources, the credibility of the news article is evaluated as high. Furthermore, the analysis unit can analyze the context of social media posts and compare them with past data. By comparing with past data, it can verify whether the same content is being disseminated from multiple reliable sources and evaluate its credibility. The analysis unit utilizes natural language processing technology to evaluate the credibility of news articles and social media posts with high accuracy. As a result, the analysis unit can quickly and accurately evaluate the credibility of collected information and prevent the spread of misinformation and fake news.

[0032] The display unit shows the credibility evaluation results assessed by the analysis unit. For example, the display unit shows the credibility evaluation results at the top of a news article. Specifically, it displays the credibility evaluation results below the news article title, allowing users to check the credibility before reading the article. The display unit can also display the credibility evaluation results on social media posts. For example, it displays the credibility evaluation results below a social media post, allowing users to check the credibility of the post at a glance. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit displays a green icon for highly reliable information and a red icon for less reliable information. This allows users to judge the credibility of information at a glance. The display unit can also display the evaluation results as a pop-up window or notification. For example, when a user clicks on a news article, the credibility evaluation results are displayed in a pop-up window. Similarly, the credibility evaluation results are displayed as a notification for social media posts. This makes it easier for users to check the credibility of information. The display unit displays the evaluation results at the top of news articles and social media posts, allowing users to check the credibility at a glance. Furthermore, the display unit can also provide detailed information about the evaluation results. For example, it can display explanations of the credibility evaluation criteria and evaluation process to make it easier for users to understand the evaluation results. As a result, the news credibility evaluation system according to the embodiment can automatically evaluate the credibility of news articles and social media posts, and prevent the spread of misinformation and fake news.

[0033] The data collection unit can collect news articles and social media posts. For example, it can collect information from news sites and social media platforms using APIs. It can also automatically collect news articles and social media posts using web scraping techniques. Furthermore, it can collect information based on URLs and keywords provided by users. For example, it can subscribe to RSS feeds from news sites and automatically collect the latest news articles. It can also use social media platform APIs to collect posts related to specific hashtags or keywords. The data collection unit uses web scraping techniques to collect information from news sites and social media platforms and store it in a database. This allows for efficient collection of news articles and social media posts. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information collected from news sites and social media platforms into a generating AI and have the generating AI perform the information collection.

[0034] The analysis unit can analyze news articles and social media posts using natural language processing techniques and evaluate their credibility. For example, the analysis unit can perform morphological analysis to analyze the structure of a text. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of a text. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of a text and evaluate its credibility. For example, the analysis unit can analyze the content of a news article and evaluate its degree of agreement with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with past data. The analysis unit makes full use of natural language processing techniques to evaluate the credibility of news articles and social media posts with high accuracy. As a result, by using natural language processing techniques, the credibility of news articles and social media posts can be evaluated with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input news articles and social media posts into a generative AI and have the generative AI perform the credibility evaluation.

[0035] The display unit can display the evaluation results at the top of the news article. For example, the display unit can display the reliability evaluation results at the top of the news article. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of the news article so that users can check the credibility at a glance. This allows the user to immediately receive the credibility evaluation results of the news article. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input the evaluation results to be displayed at the top of the news article into a generating AI and have the generating AI execute the display method.

[0036] The display unit can display evaluation results in SNS posts. For example, the display unit can display reliability evaluation results in SNS posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display evaluation results as a pop-up window or notification. The display unit displays evaluation results in SNS posts so that users can check the credibility at a glance. This allows the user to immediately receive the credibility evaluation results of SNS posts. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the evaluation results to be displayed in SNS posts into a generating AI and have the generating AI execute the display method.

[0037] The analysis unit can perform evaluations by comparing them with reliable sources and historical data. For example, the analysis unit can analyze the content of a news article and evaluate its degree of agreement with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with historical data. By comparing the analysis unit with reliable sources and historical data, the accuracy of the credibility assessment improves. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input news articles or social media posts into a generative AI and have the generative AI perform the credibility assessment.

[0038] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information from news sites that the user has frequently visited in the past. The data collection unit can also prioritize collecting information on specific topics from the user's past browsing history. The data collection unit can also analyze the user's past browsing history and prioritize collecting information from reliable sources. This enables optimal information collection based on the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter news articles and social media posts based on the user's current areas of interest. For example, the data collection unit can prioritize collecting news articles related to topics the user is currently interested in. The data collection unit can also filter social media posts based on the user's current areas of interest to collect highly relevant information. The data collection unit can also prioritize collecting news articles and social media posts containing specific keywords based on the user's areas of interest. This allows for the collection of highly relevant information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting news articles and social media posts. For example, the data collection unit can prioritize the collection of news articles related to the user's current location. The data collection unit can also filter local social media posts based on the user's geographical location to collect highly relevant information. The data collection unit can also prioritize the collection of information on important local news and events by considering the user's location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The collection unit can analyze a user's social media activity and collect relevant information when collecting news articles and social media posts. For example, the collection unit can prioritize collecting posts from accounts that the user follows. The collection unit can also collect information related to topics of interest from the user's social media activity. The collection unit can also analyze a user's social media activity history and collect highly relevant news articles and posts. This allows for the collection of highly relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important news articles. For general news articles, the analysis unit can perform a concise analysis. For information of high urgency, the analysis unit can perform a rapid analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit may apply a specific analysis algorithm to political news. The analysis unit may also apply a different analysis algorithm to economic news. The analysis unit may also apply yet another analysis algorithm to entertainment news. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the latest news articles. The analysis unit may also prioritize the analysis of information with high urgency. The analysis unit may also postpone the analysis of older information. This allows for efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information related to the user's areas of interest. The analysis unit may also prioritize the analysis of highly relevant information based on the user's past browsing history. The analysis unit may also prioritize the analysis of highly relevant information based on the user's current location information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize providing display methods that the user has previously preferred. The display unit can also suggest the optimal display layout based on the user's past operation history. The display unit can also analyze the user's past operation history and provide a display method with high visibility. This allows the display unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0047] The display unit can adjust the level of detail of the display based on the reliability of the information during display. For example, the display unit will display highly reliable information in detail. The display unit can also display less reliable information in a concise manner. The display unit can also adjust the level of detail of the display based on the reliability evaluation results. This allows the information to be displayed with an appropriate level of detail based on its reliability. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the reliability of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0048] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0049] The display unit can adjust the display order based on the relevance of the information during display. For example, the display unit may prioritize displaying information related to the user's areas of interest. The display unit may also prioritize displaying highly relevant information based on the user's past browsing history. The display unit may also prioritize displaying highly relevant information based on the user's current location information. This allows information to be displayed in an appropriate order based on its relevance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the display order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The news credibility evaluation system can further analyze the user's past browsing history and select the optimal collection method. For example, it can prioritize collecting information from news sites that the user has frequently visited in the past. It can also prioritize collecting information on specific topics based on the user's past browsing history. By analyzing the user's past browsing history, it can also prioritize collecting information from reliable sources. This enables optimal information collection based on the user's past browsing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past browsing history into a generating AI and have the generating AI select the optimal collection method.

[0052] The news credibility evaluation system can further prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize the collection of news articles related to the user's current location. It can also filter local social media posts based on the user's geographical location to collect highly relevant information. It can also prioritize the collection of information on important local news and events by considering the user's location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0053] The news credibility evaluation system can further analyze the user's social media activity and collect relevant information. For example, it can prioritize collecting posts from accounts the user follows. It can also collect information related to topics of interest from the user's social media activity. It can analyze the user's social media activity history and collect highly relevant news articles and posts. This allows for the collection of highly relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0054] The news credibility evaluation system can further adjust the level of detail of its analysis based on the importance of the information. For example, it can perform a detailed analysis on important news articles, a concise analysis on general news articles, and a rapid analysis on urgent information. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0055] The news credibility evaluation system can further apply different analysis algorithms depending on the category of information. For example, a specific analysis algorithm may be applied to political news. A different analysis algorithm may be applied to economic news. Yet another analysis algorithm may be applied to entertainment news. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects news articles and social media posts. The data collection unit collects information using APIs from news sites and social media platforms, for example. The data collection unit can also automatically collect news articles and social media posts using web scraping technology. Furthermore, the data collection unit can collect information based on URLs and keywords provided by users. For example, the data collection unit subscribes to RSS feeds of news sites and automatically collects the latest news articles. The data collection unit can also use APIs of social media platforms to collect posts related to specific hashtags or keywords. The data collection unit uses web scraping technology to collect information from news sites and social media platforms and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. The analysis unit analyzes news articles and social media posts, for example, using natural language processing techniques. The analysis unit can perform morphological analysis to analyze the structure of sentences. It can also perform grammatical analysis to evaluate the grammatical accuracy of sentences. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of sentences and evaluate their credibility. For example, the analysis unit analyzes the content of news articles and evaluates the degree of agreement with reliable sources. The analysis unit can also analyze the context of social media posts and compare them with past data. The analysis unit uses natural language processing techniques to evaluate the credibility of news articles and social media posts with high accuracy. Step 3: The display unit displays the credibility results evaluated by the analysis unit. For example, the display unit displays the credibility evaluation results at the top of a news article. The display unit can also display the credibility evaluation results on social media posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit displays a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of news articles and social media posts so that users can check the credibility at a glance.

[0058] (Example of form 2) The news credibility evaluation system according to an embodiment of the present invention is a system that automatically verifies the credibility of news articles and information on social media using AI, thereby preventing the spread of misinformation and fake news. When a user views a news article or social media post, the information is automatically verified by the AI. The AI ​​utilizes natural language processing technology to analyze news articles and social media posts and evaluate their credibility. For example, it analyzes the content of news articles and the context of posts, and evaluates them by comparing them with reliable sources and historical data. Next, the reliability evaluation result from the AI ​​is immediately displayed on the user's screen. For example, the reliability evaluation result is displayed at the top of the news article, allowing the user to see at a glance whether the article is trustworthy. Similarly, the reliability evaluation result is displayed for social media posts. This system allows users to access accurate information without being misled by misinformation or fake news. For example, when a user is browsing a news site, the reliability of each article and related social media post is automatically evaluated, and the reliability evaluation result is displayed at the top of the article. This allows users to easily identify misinformation and fake news. Furthermore, this system uses cloud computing technology to efficiently analyze large amounts of data and evaluate the credibility of news in real time. This will allow the system to reach 10 million users within a year of its release, and increase revenue through advertising and premium features. Thus, the system, which uses AI to automatically verify the credibility of news articles and information on social media, aims to prevent the spread of misinformation and fake news by providing users with access to reliable information. In short, the news credibility evaluation system can provide users with access to reliable information and prevent the spread of fake news.

[0059] The news credibility evaluation system according to this embodiment comprises a collection unit, an analysis unit, and a display unit. The collection unit collects news articles and SNS posts. The collection unit collects information using APIs from news sites and SNS platforms, for example. The collection unit can also automatically collect news articles and SNS posts using web scraping technology. Furthermore, the collection unit can also collect information based on URLs and keywords provided by users. For example, the collection unit subscribes to RSS feeds of news sites and automatically collects the latest news articles. The collection unit can also collect posts related to specific hashtags or keywords using APIs of SNS platforms. The collection unit collects information from news sites and SNS platforms using web scraping technology and stores it in a database. The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. The analysis unit analyzes news articles and SNS posts using natural language processing technology, for example. The analysis unit can perform morphological analysis to analyze the structure of sentences. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of sentences. Furthermore, the analysis unit can perform semantic analysis, understand the meaning of text, and evaluate its credibility. For example, the analysis unit can analyze the content of a news article and evaluate its degree of consistency with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with past data. The analysis unit utilizes natural language processing technology to evaluate the credibility of news articles and social media posts with high accuracy. The display unit displays the credibility results evaluated by the analysis unit. For example, the display unit can display the credibility evaluation results at the top of a news article. The display unit can also display the credibility evaluation results for social media posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of news articles and social media posts so that users can check the credibility at a glance.As a result, the news credibility evaluation system according to the embodiment can automatically evaluate the credibility of news articles and social media posts, and prevent the spread of misinformation and fake news.

[0060] The data collection unit collects news articles and social media posts. For example, it uses APIs to collect information from news sites and social media platforms. Specifically, it uses news site APIs to periodically retrieve the latest news articles and store them in a database. It can also use social media platform APIs to collect posts related to specific hashtags or keywords in real time. Furthermore, the data collection unit can automatically collect news articles and social media posts using web scraping technology. Web scraping technology makes it possible to obtain information even from sites that do not provide APIs. For example, it can analyze the HTML structure of a specific news site and extract information such as article titles, body text, and publication dates. The data collection unit can also collect information based on URLs and keywords provided by users. If a user provides a URL for a specific news article, it accesses that URL, retrieves the article's content, and stores it in the database. In keyword-based collection, it searches for and collects related news articles and social media posts. For example, the data collection unit subscribes to news site RSS feeds and automatically collects the latest news articles. Using RSS feeds allows for the rapid acquisition of new articles whenever a news site is updated. The data collection unit can also use SNS platform APIs to collect posts related to specific hashtags or keywords. This allows the data collection unit to gather diverse data from a wide range of sources and provide foundational data for credibility assessment.

[0061] The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. For example, the analysis unit uses natural language processing techniques to analyze news articles and social media posts. Specifically, it can perform morphological analysis to analyze the structure of a text. Morphological analysis divides the text into words and identifies the part of speech and meaning of each word. This allows for an understanding of the basic structure of the text and lays the foundation for more detailed analysis. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of the text. Grammatical analysis evaluates whether the text is constructed according to correct grammar, and texts with many grammatical errors can be judged as having low credibility. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of the text and evaluate its credibility. Semantic analysis involves understanding the content of the text and checking whether specific keywords or phrases are included. For example, the analysis unit analyzes the content of a news article and evaluates its degree of agreement with reliable sources. Reliable sources include official news sites and announcements from government agencies. If there is a lot of agreement with these sources, the credibility of the news article is evaluated as high. Furthermore, the analysis unit can analyze the context of social media posts and compare them with past data. By comparing with past data, it can verify whether the same content is being disseminated from multiple reliable sources and evaluate its credibility. The analysis unit utilizes natural language processing technology to evaluate the credibility of news articles and social media posts with high accuracy. As a result, the analysis unit can quickly and accurately evaluate the credibility of collected information and prevent the spread of misinformation and fake news.

[0062] The display unit shows the credibility evaluation results assessed by the analysis unit. For example, the display unit shows the credibility evaluation results at the top of a news article. Specifically, it displays the credibility evaluation results below the news article title, allowing users to check the credibility before reading the article. The display unit can also display the credibility evaluation results on social media posts. For example, it displays the credibility evaluation results below a social media post, allowing users to check the credibility of the post at a glance. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit displays a green icon for highly reliable information and a red icon for less reliable information. This allows users to judge the credibility of information at a glance. The display unit can also display the evaluation results as a pop-up window or notification. For example, when a user clicks on a news article, the credibility evaluation results are displayed in a pop-up window. Similarly, the credibility evaluation results are displayed as a notification for social media posts. This makes it easier for users to check the credibility of information. The display unit displays the evaluation results at the top of news articles and social media posts, allowing users to check the credibility at a glance. Furthermore, the display unit can also provide detailed information about the evaluation results. For example, it can display explanations of the credibility evaluation criteria and evaluation process to make it easier for users to understand the evaluation results. As a result, the news credibility evaluation system according to the embodiment can automatically evaluate the credibility of news articles and social media posts, and prevent the spread of misinformation and fake news.

[0063] The data collection unit can collect news articles and social media posts. For example, it can collect information from news sites and social media platforms using APIs. It can also automatically collect news articles and social media posts using web scraping techniques. Furthermore, it can collect information based on URLs and keywords provided by users. For example, it can subscribe to RSS feeds from news sites and automatically collect the latest news articles. It can also use social media platform APIs to collect posts related to specific hashtags or keywords. The data collection unit uses web scraping techniques to collect information from news sites and social media platforms and store it in a database. This allows for efficient collection of news articles and social media posts. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information collected from news sites and social media platforms into a generating AI and have the generating AI perform the information collection.

[0064] The analysis unit can analyze news articles and social media posts using natural language processing techniques and evaluate their credibility. For example, the analysis unit can perform morphological analysis to analyze the structure of a text. The analysis unit can also perform grammatical analysis to evaluate the grammatical accuracy of a text. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of a text and evaluate its credibility. For example, the analysis unit can analyze the content of a news article and evaluate its degree of agreement with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with past data. The analysis unit makes full use of natural language processing techniques to evaluate the credibility of news articles and social media posts with high accuracy. As a result, by using natural language processing techniques, the credibility of news articles and social media posts can be evaluated with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input news articles and social media posts into a generative AI and have the generative AI perform the credibility evaluation.

[0065] The display unit can display the evaluation results at the top of the news article. For example, the display unit can display the reliability evaluation results at the top of the news article. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of the news article so that users can check the credibility at a glance. This allows the user to immediately receive the credibility evaluation results of the news article. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input the evaluation results to be displayed at the top of the news article into a generating AI and have the generating AI execute the display method.

[0066] The display unit can display evaluation results in SNS posts. For example, the display unit can display reliability evaluation results in SNS posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit can display a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display evaluation results as a pop-up window or notification. The display unit displays evaluation results in SNS posts so that users can check the credibility at a glance. This allows the user to immediately receive the credibility evaluation results of SNS posts. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the evaluation results to be displayed in SNS posts into a generating AI and have the generating AI execute the display method.

[0067] The analysis unit can perform evaluations by comparing them with reliable sources and historical data. For example, the analysis unit can analyze the content of a news article and evaluate its degree of agreement with reliable sources. The analysis unit can also analyze the context of a social media post and compare it with historical data. By comparing the analysis unit with reliable sources and historical data, the accuracy of the credibility assessment improves. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input news articles or social media posts into a generative AI and have the generative AI perform the credibility assessment.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of collecting news articles and social media posts based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect only important information. If the user is relaxed, the data collection unit can increase the collection frequency and provide a wider variety of information. If the user is excited, the data collection unit can adjust the collection frequency and prioritize the collection of highly reliable information. This allows for more appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0069] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information from news sites that the user has frequently visited in the past. The data collection unit can also prioritize collecting information on specific topics from the user's past browsing history. The data collection unit can also analyze the user's past browsing history and prioritize collecting information from reliable sources. This enables optimal information collection based on the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter news articles and social media posts based on the user's current areas of interest. For example, the data collection unit can prioritize collecting news articles related to topics the user is currently interested in. The data collection unit can also filter social media posts based on the user's current areas of interest to collect highly relevant information. The data collection unit can also prioritize collecting news articles and social media posts containing specific keywords based on the user's areas of interest. This allows for the collection of highly relevant information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting reliable information. If the user is excited, the data collection unit may also prioritize collecting the latest news articles. If the user is relaxed, the data collection unit may also prioritize collecting information on interesting topics. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting news articles and social media posts. For example, the data collection unit can prioritize the collection of news articles related to the user's current location. The data collection unit can also filter local social media posts based on the user's geographical location to collect highly relevant information. The data collection unit can also prioritize the collection of information on important local news and events by considering the user's location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0073] The collection unit can analyze a user's social media activity and collect relevant information when collecting news articles and social media posts. For example, the collection unit can prioritize collecting posts from accounts that the user follows. The collection unit can also collect information related to topics of interest from the user's social media activity. The collection unit can also analyze a user's social media activity history and collect highly relevant news articles and posts. This allows for the collection of highly relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and clear analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important news articles. For general news articles, the analysis unit can perform a concise analysis. For information of high urgency, the analysis unit can perform a rapid analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit may apply a specific analysis algorithm to political news. The analysis unit may also apply a different analysis algorithm to economic news. The analysis unit may also apply yet another analysis algorithm to entertainment news. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the latest news articles. The analysis unit may also prioritize the analysis of information with high urgency. The analysis unit may also postpone the analysis of older information. This allows for efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission into a generating AI and have the generating AI determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information related to the user's areas of interest. The analysis unit may also prioritize the analysis of highly relevant information based on the user's past browsing history. The analysis unit may also prioritize the analysis of highly relevant information based on the user's current location information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is nervous, the display unit can provide a simple and highly visible display method. If the user is relaxed, the display unit can also provide a display method that includes detailed information. If the user is in a hurry, the display unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0081] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize providing display methods that the user has previously preferred. The display unit can also suggest the optimal display layout based on the user's past operation history. The display unit can also analyze the user's past operation history and provide a display method with high visibility. This allows the display unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0082] The display unit can adjust the level of detail of the display based on the reliability of the information during display. For example, the display unit will display highly reliable information in detail. The display unit can also display less reliable information in a concise manner. The display unit can also adjust the level of detail of the display based on the reliability evaluation results. This allows the information to be displayed with an appropriate level of detail based on its reliability. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the reliability of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0083] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is feeling anxious, the display unit will prioritize displaying reliable information. If the user is excited, the display unit may also prioritize displaying the latest news articles. If the user is relaxed, the display unit may also prioritize displaying information on interesting topics. This allows for more appropriate information to be provided by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0084] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0085] The display unit can adjust the display order based on the relevance of the information during display. For example, the display unit may prioritize displaying information related to the user's areas of interest. The display unit may also prioritize displaying highly relevant information based on the user's past browsing history. The display unit may also prioritize displaying highly relevant information based on the user's current location information. This allows information to be displayed in an appropriate order based on its relevance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the display order.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The news credibility rating system can further estimate the user's emotions and adjust how it displays the credibility rating results for news articles and social media posts based on those estimated emotions. For example, if the user is feeling anxious, a simple and clear rating result can be displayed. If the user is relaxed, a detailed rating result can be displayed. If the user is excited, a visually appealing rating result can be displayed. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI adjust the display method.

[0088] The news credibility evaluation system can further analyze the user's past browsing history and select the optimal collection method. For example, it can prioritize collecting information from news sites that the user has frequently visited in the past. It can also prioritize collecting information on specific topics based on the user's past browsing history. By analyzing the user's past browsing history, it can also prioritize collecting information from reliable sources. This enables optimal information collection based on the user's past browsing history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past browsing history into a generating AI and have the generating AI select the optimal collection method.

[0089] The news credibility evaluation system can further estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is feeling anxious, reliable information can be prioritized. If the user is excited, the latest news articles can be prioritized. If the user is relaxed, information on interesting topics can be prioritized. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of information.

[0090] The news credibility evaluation system can further prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize the collection of news articles related to the user's current location. It can also filter local social media posts based on the user's geographical location to collect highly relevant information. It can also prioritize the collection of information on important local news and events by considering the user's location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0091] The news credibility evaluation system can further estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, it can provide simple and clear analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is excited, it can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0092] The news credibility evaluation system can further analyze the user's social media activity and collect relevant information. For example, it can prioritize collecting posts from accounts the user follows. It can also collect information related to topics of interest from the user's social media activity. It can analyze the user's social media activity history and collect highly relevant news articles and posts. This allows for the collection of highly relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0093] The news credibility evaluation system can further estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. If the user is excited, it can provide a visually appealing analysis. By adjusting the length of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0094] The news credibility evaluation system can further adjust the level of detail of its analysis based on the importance of the information. For example, it can perform a detailed analysis on important news articles, a concise analysis on general news articles, and a rapid analysis on urgent information. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0095] The news credibility evaluation system can further apply different analysis algorithms depending on the category of information. For example, a specific analysis algorithm may be applied to political news. A different analysis algorithm may be applied to economic news. Yet another analysis algorithm may be applied to entertainment news. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0096] The news credibility evaluation system can further estimate the user's emotions and determine display priorities based on those emotions. For example, if the user is feeling anxious, reliable information can be displayed preferentially. If the user is excited, the latest news articles can be displayed preferentially. If the user is relaxed, information on interesting topics can be displayed preferentially. This allows for more appropriate information to be provided by determining display priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI determine the display priorities.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects news articles and social media posts. The data collection unit collects information using APIs from news sites and social media platforms, for example. The data collection unit can also automatically collect news articles and social media posts using web scraping technology. Furthermore, the data collection unit can collect information based on URLs and keywords provided by users. For example, the data collection unit subscribes to RSS feeds of news sites and automatically collects the latest news articles. The data collection unit can also use APIs of social media platforms to collect posts related to specific hashtags or keywords. The data collection unit uses web scraping technology to collect information from news sites and social media platforms and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates its credibility. The analysis unit analyzes news articles and social media posts, for example, using natural language processing techniques. The analysis unit can perform morphological analysis to analyze the structure of sentences. It can also perform grammatical analysis to evaluate the grammatical accuracy of sentences. Furthermore, the analysis unit can perform semantic analysis to understand the meaning of sentences and evaluate their credibility. For example, the analysis unit analyzes the content of news articles and evaluates the degree of agreement with reliable sources. The analysis unit can also analyze the context of social media posts and compare them with past data. The analysis unit uses natural language processing techniques to evaluate the credibility of news articles and social media posts with high accuracy. Step 3: The display unit displays the credibility results evaluated by the analysis unit. For example, the display unit displays the credibility evaluation results at the top of a news article. The display unit can also display the credibility evaluation results on social media posts. The display unit can also use icons and color coding to make the evaluation results visually easy to understand. For example, the display unit displays a green icon for highly reliable information and a red icon for less reliable information. The display unit can also display the evaluation results as a pop-up window or notification. The display unit displays the evaluation results at the top of news articles and social media posts so that users can check the credibility at a glance.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information from news sites and SNS platforms using the communication I / F 44 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology and evaluates its credibility. The display unit displays the evaluation results to the user using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information from news sites and SNS platforms using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology and evaluates its credibility. The display unit displays the evaluation results to the user using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information from news sites and SNS platforms using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology and evaluates its credibility. The display unit displays the evaluation results to the user using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information from news sites and social networking platforms using the robot 414's communication interface 44. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information using natural language processing technology and evaluates its credibility. The display unit displays the evaluation results to the user using the robot 414's display. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The collection department collects news articles and social media posts, An analysis unit analyzes the information collected by the aforementioned collection unit and evaluates its credibility, The system includes a display unit that displays the results of the credibility evaluation performed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect news articles and social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We use natural language processing techniques to analyze news articles and social media posts and evaluate their credibility. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display the evaluation results at the top of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Display evaluation results in social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The evaluation is conducted by comparing it with reliable sources and historical data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of news article and social media post collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past browsing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting news articles and social media posts, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting news articles and social media posts, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting news articles and social media posts, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying information, adjust the level of detail based on the reliability of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying information, adjust the display order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects news articles and social media posts, An analysis unit analyzes the information collected by the aforementioned collection unit and evaluates its credibility, The system includes a display unit that displays the results of the credibility evaluation performed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect news articles and social media posts. The system according to feature 1.

3. The aforementioned analysis unit, We use natural language processing techniques to analyze news articles and social media posts and evaluate their credibility. The system according to feature 1.

4. The aforementioned display unit is Display the evaluation results at the top of the news article. The system according to feature 1.

5. The aforementioned display unit is Display evaluation results in social media posts. The system according to feature 1.

6. The aforementioned analysis unit, The evaluation is conducted by comparing it with reliable sources and historical data. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of news article and social media post collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past browsing history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting news articles and social media posts, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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