system

Abstract

An object of a system according to an embodiment is to verify the authenticity of information on an SNS and correct erroneous information.SOLUTION: A system includes an information collection unit, an analysis unit, a verification unit, and a correction unit. The information collection unit collects posts on the SNS. The analysis unit analyzes the posts collected by the information collection unit. The verification unit verifies the authenticity of the information analyzed by the analysis unit. The correction unit corrects the information determined to be the erroneous information by the verification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently detect and correct false or defamatory information on social media.

[0005] The system according to the embodiment aims to verify the authenticity of information on SNS and correct false information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a verification unit, and a correction unit. The information collection unit collects posts on SNS. The analysis unit analyzes the posts collected by the information collection unit. The verification unit verifies the authenticity of the information analyzed by the analysis unit. The correction unit corrects information determined to be false by the verification unit. [Effects of the Invention]

[0007] The system according to the embodiment can verify the authenticity of information on SNS and correct false information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information verification system according to an embodiment of the present invention is a system in which AI verifies the authenticity of information and corrects false information posted on social media. This enables the information verification system to quickly and accurately respond to false information and slander posted on social media, and spread correct information.

[0029] An information verification system according to an embodiment includes an information collection unit, an analysis unit, a verification unit, and a correction unit. The information collection unit collects posts on social media. For example, the information collection unit collects posts containing specific keywords or hashtags. The information collection unit can also collect posts on social media using an API. The information collection unit can also collect posts on social media using scraping technology. The analysis unit analyzes the collected posts. For example, the analysis unit can analyze the content of the posts using natural language processing technology. The analysis unit can also analyze the content of images and videos included in the posts using image analysis technology. The analysis unit can also analyze the emotions of posters using an emotion estimation function to preferentially detect emotionally extreme posts. The verification unit verifies the authenticity of the analyzed information. For example, the verification unit can verify the accuracy of the information by referring to reliable information sources such as official news sites, official company announcements, and academic papers. The verification unit can also cross-reference multiple reliable information sources to verify the accuracy of the information from multiple angles. The verification unit can also refer to a database updated in real time. The correction unit corrects information determined to be false information. For example, the correction unit posts a correction on a social networking site to spread correct information. The correction unit can also work with a company's public relations department to provide official corrected information. After making a correction, the correction unit can monitor the extent to which the correction has been accepted and make additional corrections or explanations as necessary. This allows the information verification system according to the embodiment to quickly and accurately respond to rumors and slander on social networking sites and spread correct information. For example, the information verification system can protect a company's credibility by detecting rumors and slander on social networking sites and providing correct information. The information verification system can also conduct awareness-raising activities to prevent the spread of rumors and provide users with methods for verifying correct information. This can improve the reliability of information throughout society.

[0030] The information collection unit can collect posts that include specific keywords and hashtags. The information collection unit, for example, collects posts that include specific keywords and hashtags. For example, AI collects posts on social media and analyzes the poster's emotions using an emotion estimation function. For example, posts that contain strong emotions such as anger or hatred are preferentially detected. The emotion estimation function is also used to calculate the poster's emotion score and filter out emotionally extreme posts. For example, posts with an emotion score above a certain level are preferentially analyzed. The emotion estimation function is also used to analyze the type and intensity of emotion in the posts collected by AI, and particularly emotionally extreme posts are preferentially detected. This makes it possible to efficiently collect posts that include specific keywords and hashtags.

[0031] The verification unit can verify the accuracy of information by referring to reliable sources such as official news sites, official company announcements, and academic papers. The verification unit can verify the accuracy of information by referring to reliable sources such as official news sites, official company announcements, and academic papers. For example, the verification unit uses an emotion estimation function to evaluate the reliability of information sources referenced by the AI, and prioritizes the use of emotionally neutral sources. For example, it selects information sources with low emotion scores. Furthermore, it uses the emotion estimation function to evaluate the emotional bias of information sources, and prioritizes the use of neutral sources. For example, it selects emotionally neutral news sites. Furthermore, it builds a system that uses the emotion estimation function to evaluate the reliability of information sources referenced by the AI, and prioritizes the use of emotionally neutral sources. For example, it filters information sources based on emotion scores. This makes it possible to verify the accuracy of information by referring to reliable sources.

[0032] The correction unit can post corrections on social media to spread correct information. The correction unit, for example, posts corrections on social media to spread correct information. For example, when the AI ​​corrects misinformation, it uses an emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it adjusts the correction method based on the emotion score. Furthermore, it uses the emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it makes corrections using positive expressions. Furthermore, when the AI ​​corrects misinformation, it uses the emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it adjusts the corrections based on the emotion score. This allows correct information to be spread on social media.

[0033] The information collection unit can also collect information from online platforms other than SNS. For example, the information collection unit collects information from online platforms other than SNS. For example, AI collects information from online platforms other than SNS and analyzes a wide range of data sources. For example, blog and forum posts are collected and analyzed. The information collected from online platforms is also integrated and analyzed for the possibility of rumors or slander. For example, SNS and blog posts are analyzed in an integrated manner. In addition, in order to analyze a wide range of data sources, information is collected from online platforms other than SNS and the possibility of rumors or slander is determined with high accuracy. For example, forum posts are collected and analyzed. This allows information to be collected from online platforms other than SNS.

[0034] The analysis unit can also use image analysis technology to analyze the content of images and videos included in posts, and detect non-textual hoaxes and slander. For example, the analysis unit can use image analysis technology to analyze the content of images and videos included in posts, and detect non-textual hoaxes and slander. For example, it can analyze images and videos included in posts collected by AI to detect the possibility of hoaxes and slander. For example, it can use image recognition technology to detect defamatory images. It can also use image analysis technology to convert the content of images and videos included in posts into text data and analyze the possibility of hoaxes and slander. For example, it can extract and analyze text from images. It can also analyze images and videos included in posts collected by AI to detect non-textual hoaxes and slander. For example, it can analyze the audio in videos to detect defamatory remarks. This makes it possible to detect non-textual hoaxes and slander.

[0035] The verification unit can cross-reference multiple highly reliable information sources and verify the accuracy of information from multiple angles. The verification unit, for example, cross-references multiple highly reliable information sources and verify the accuracy of information from multiple angles. For example, when AI verifies the authenticity of information, it cross-references multiple highly reliable information sources and verify the accuracy of the information from multiple angles. For example, it refers to news sites and academic papers. In addition, a system is built that cross-references multiple information sources and verify the accuracy of information. For example, it compares official announcements with news articles. In addition, when AI verifies the authenticity of information, it cross-references multiple highly reliable information sources and verify the accuracy of the information from multiple angles. For example, it integrates and analyzes data from different sources. This allows the accuracy of the information to be verified from multiple angles.

[0036] The verification unit can explain in detail the information sources used and their evaluation criteria when presenting the verification results to the user. For example, when presenting the verification results to the user, the verification unit explains in detail the information sources used and their evaluation criteria. For example, when the AI ​​presents the verification results to the user, it explains in detail the information sources used and their evaluation criteria. For example, it displays a list of the news sites and academic papers referenced. Furthermore, to ensure transparency in the verification process, a system is built that explains in detail the information sources used and their evaluation criteria. For example, it displays a score based on the evaluation criteria. Furthermore, when the AI ​​presents the verification results to the user, it explains in detail the information sources used and their evaluation criteria. For example, it displays the reliability score of the information sources. This ensures transparency in the verification process.

[0037] The verification unit can refer to a database that is updated in real time. The verification unit, for example, refers to a database that is updated in real time. For example, breaking news that is updated in real time may be added to the information sources referenced by the AI ​​to verify the authenticity of the information. For example, the latest news articles may be referenced. The accuracy of the information may also be confirmed by referencing a database of academic papers that is updated in real time. For example, information may be verified based on the latest research results. The verification unit may also add a database that is updated in real time to the information sources referenced by the AI ​​to build a system that verifies the authenticity of information. For example, breaking news and the latest information on academic papers may be integrated. This makes it possible to confirm the accuracy of the information based on the latest information.

[0038] The verification unit can collect opinions of experts in different industries and fields and verify the authenticity of information from multiple angles. The verification unit, for example, collects opinions of experts in different industries and fields and verifies the authenticity of information from multiple angles. For example, the verification unit collects opinions of experts in different industries and fields and builds a system that verifies the authenticity of information from multiple angles. For example, the verification unit refers to the opinions of experts in medicine, law, technology, etc., and also confirms the accuracy of information based on the expert opinions. For example, information in the medical field is verified based on the opinions of doctors. Also, the verification unit collects opinions of experts in different industries and fields and verifies the authenticity of information from multiple angles. For example, information in the technical field is verified based on the opinions of engineers. This makes it possible to verify the authenticity of information from multiple angles based on the opinions of experts in different industries and fields.

[0039] The correction unit can generate infographics and videos to make the corrections visually easy to understand. The correction unit generates infographics and videos to make the corrections visually easy to understand, for example. For example, when AI corrects misinformation, it generates visually easy-to-understand infographics. For example, it shows the corrections in diagrams and graphs. Furthermore, AI generates videos to make the corrections visually easy to understand. For example, it explains the corrections using animations. Furthermore, a system is constructed to generate visually easy-to-understand infographics and videos when AI corrects misinformation. For example, a tool is provided that visually displays the corrections. This makes it possible to provide the corrections in a visually easy-to-understand manner.

[0040] After making a correction, the correction unit monitors how well the correction is accepted and can make additional corrections or explanations as necessary. For example, after making a correction, the correction unit monitors how well the correction is accepted and can make additional corrections or explanations as necessary. For example, after AI corrects misinformation, it monitors how well the correction is accepted. For example, it analyzes user reactions and comments. It also builds a system that monitors whether the correction is accepted and makes additional corrections or explanations as necessary. For example, it makes additional corrections based on user feedback. It also monitors how well the correction is accepted after AI makes a correction and can make additional corrections or explanations as necessary. For example, it analyzes user reactions to the corrections in real time. This makes it possible to monitor whether the correction is accepted and make additional corrections or explanations as necessary.

[0041] The correction unit can simultaneously post correction information on different social media platforms, providing correct information to a wide range of users. The correction unit, for example, simultaneously posts correction information on different social media platforms, providing correct information to a wide range of users. For example, when AI corrects misinformation, it posts the correction information on different social media platforms simultaneously. For example, it posts simultaneously on Twitter, Facebook, Instagram, etc. In addition, a system can be built that simultaneously posts correction information on different social media platforms, providing correct information to a wide range of users. For example, a tool can be provided that automatically posts on multiple platforms. In addition, when AI corrects misinformation, it posts the correction information on different social media platforms simultaneously, providing correct information to a wide range of users. For example, information can be shared between social media platforms. This allows correction information to be simultaneously posted on different social media platforms, providing correct information to a wide range of users.

[0042] The correction unit provides correction information in multiple languages, making it possible to accommodate international users. The correction unit, for example, provides correction information in multiple languages, making it possible to accommodate international users. For example, when AI corrects misinformation, the correction information is provided in multiple languages. For example, correction information is posted in multiple languages, such as English, French, and Chinese. In addition, a system is built that provides correction information in multiple languages ​​and accommodates international users. For example, a correction information posting tool with a translation function is provided. In addition, when AI corrects misinformation, the correction information is provided in multiple languages, making it possible to accommodate international users. For example, correction information in different languages ​​is posted simultaneously. This makes it possible to provide correction information in multiple languages, making it possible to accommodate international users.

[0043] The verification unit can cross-reference multiple reliable information sources, such as official company announcements and product review sites, to verify the authenticity of slander. For example, when AI verifies the authenticity of slander, it cross-references multiple reliable information sources, such as official company announcements and product review sites. For example, it compares information from official announcements and review sites. In addition, a system can be built that cross-references multiple reliable information sources to verify the authenticity of slander. For example, it confirms the authenticity of slander based on official announcements and news articles. In addition, when AI verifies the authenticity of slander, it cross-references multiple reliable information sources, such as official company announcements and product review sites. For example, it integrates and analyzes data from different sources. This allows the authenticity of slander to be verified from multiple angles.

[0044] The corrections department can work with a company's public relations department to provide official corrections. The corrections department, for example, can work with a company's public relations department to provide official corrections. For example, when AI makes a correction to defamatory content, it can work with the company's public relations department to provide official corrections. For example, it can post the corrections provided by the public relations department on social media. In addition, it can work with a company's public relations department to build a system that provides official corrections. For example, it can automatically collect information from the public relations department and post it as corrections. In addition, when AI makes a correction to defamatory content, it can work with a company's public relations department to provide official corrections. For example, it can work with the public relations department to create corrections and post them on social media. This allows it to work with a company's public relations department to provide official corrections.

[0045] The analysis unit can also monitor different social media platforms and online forums when detecting slander against company names or product names. For example, when the AI ​​detects slander against company names or product names, it also monitors different social media platforms and online forums. For example, it monitors Twitter, Facebook, Reddit, etc. A system can also be built that monitors different social media platforms and online forums to detect slander. For example, it can collect and analyze information from multiple platforms. Furthermore, when the AI ​​detects slander against company names or product names, it can also monitor different social media platforms and online forums. For example, it can collect and analyze forum posts. By monitoring different social media platforms and online forums, it is possible to collect a wide range of information and detect slander.

[0046] The corrections department can also post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. The corrections department can, for example, post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, AI can post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be posted on the official website. A system can also be built that posts corrections to the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be automatically added to newsletters. AI can also post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be posted on the official website and in newsletters simultaneously. This allows corrections to be posted on the company's official website and in newsletters, thereby providing correct information to a wide range of users.

[0047] The analysis unit can monitor trends and buzzwords on social media in real time to detect the spread of rumors early. The analysis unit, for example, monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, AI monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it monitors hashtags that are trending rapidly. In addition, a system is built that monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it analyzes the frequency of appearance of trending words. In addition, AI monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it analyzes the appearance patterns of buzzwords. This makes it possible to monitor trends and buzzwords on social media in real time and detect the spread of rumors early.

[0048] The analysis unit can generate visually easy-to-understand infographics and videos when conducting awareness-raising activities to prevent the spread of rumors. The analysis unit generates visually easy-to-understand infographics and videos, for example, when conducting awareness-raising activities to prevent the spread of rumors. For example, when AI conducts awareness-raising activities to prevent the spread of rumors, it generates visually easy-to-understand infographics. For example, it illustrates the impact of rumors and how to confirm correct information using diagrams. Furthermore, when conducting awareness-raising activities, AI generates visually easy-to-understand videos. For example, it explains the steps to prevent the spread of rumors using animations. Furthermore, a system is constructed that generates visually easy-to-understand infographics and videos when AI conducts awareness-raising activities to prevent the spread of rumors. For example, a tool is provided that visually displays the awareness content. This makes it possible to provide awareness-raising activities to prevent the spread of rumors in a visually easy-to-understand manner.

[0049] The analysis unit can also monitor different social media platforms and online forums to prevent the spread of rumors. For example, the analysis unit can monitor different social media platforms and online forums to prevent the spread of rumors. For example, the analysis unit can monitor different social media platforms and online forums to prevent the spread of rumors using AI. For example, it can monitor Twitter, Facebook, Reddit, etc. In addition, a system can be built to monitor different social media platforms and online forums to prevent the spread of rumors. For example, it can collect and analyze information from multiple platforms. In addition, in order to prevent the spread of rumors using AI, it can collect and analyze forum posts. By monitoring different social media platforms and online forums, a wide range of information can be collected and the spread of rumors can be prevented.

[0050] The analysis unit can provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. The analysis unit, for example, can provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. For example, AI can provide awareness-raising activities to prevent the spread of rumors in multiple languages. For example, awareness information can be disseminated in multiple languages, such as English, French, and Chinese. A system can also be built to provide multilingual awareness-raising activities and accommodate international users. For example, an awareness-raising information dissemination tool with a translation function can be provided. AI can also provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. For example, awareness information can be disseminated in different languages ​​simultaneously. This allows awareness-raising activities to prevent the spread of rumors to be provided in multiple languages, and can also accommodate international users.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The information gathering unit can not only collect posts on social media, but also collect users' browsing history and search history. For example, the information gathering unit can collect websites visited by users in the past and keywords searched by users, and analyze the possibility of hoaxes or slander based on this data. The information gathering unit can also analyze users' browsing history and, if a large amount of information from a particular website is included, evaluate the reliability of that information source. Furthermore, the information gathering unit can detect the possibility of hoaxes or slander on a specific topic early on based on the user's search history. This allows the system to monitor not only posts on social media but also the user's entire online activity, improving the accuracy of detecting hoaxes and slander.

[0053] The verification department can verify the accuracy of information not only by referring to reliable sources such as official news sites, official company announcements, and academic papers, but also by utilizing crowdsourcing. For example, the verification department can collect feedback from experts and general users through a crowdsourcing platform and evaluate the accuracy of the information. The verification department can also use crowdsourcing to gather expert opinions on specific topics and verify the accuracy of the information from multiple perspectives. Furthermore, the verification department can collect information updated in real time through crowdsourcing, allowing for early detection of possible rumors and slander. This allows the accuracy of information to be verified not only through reliable sources but also through crowdsourcing, thereby improving the accuracy of detecting rumors and slander.

[0054] The correction unit can not only post corrections on social media but also provide users with correction information via push notifications. For example, when false information spreads, the correction unit can send correction information to relevant users via push notifications, quickly providing correct information. The correction unit can also provide individually customized correction information based on the user's interests and the accounts they follow. Furthermore, the correction unit can monitor the reception status of the correction information and user reactions through push notifications, and make additional corrections or explanations as necessary. This makes it possible to quickly provide correct information to a wide range of users by utilizing push notifications in addition to posting corrections on social media, preventing the spread of rumors and slander.

[0055] The information collection unit can not only collect information from online platforms other than social media, but also collect data from offline sources. For example, the information collection unit can collect information from offline media such as newspapers, magazines, and television news, and analyze it for possible rumors and slander. The information collection unit can also collect information presented at public events, conferences, seminars, etc., and use it to detect rumors and slander. Furthermore, the information collection unit can integrate data collected from offline sources with online information and perform comprehensive analysis. This allows the system to collect data not only from social media and online platforms, but also from offline sources, thereby improving the accuracy of detecting rumors and slander.

[0056] The verification unit can verify the accuracy of information not only by cross-referencing multiple reliable information sources but also by utilizing blockchain technology. For example, the verification unit can refer to information recorded on the blockchain to prevent information tampering or fraud. The verification unit can also use blockchain technology to track the source and history of information and evaluate the reliability of the information. Furthermore, the verification unit can build a database that is updated in real time based on the information recorded on the blockchain, allowing for early detection of possible rumors and slander. This allows the accuracy of information to be verified not only by multiple reliable information sources but also by utilizing blockchain technology, improving the accuracy of detecting rumors and slander.

[0057] The correction unit can not only post corrections on SNS but also provide users with correction information in an interactive format. For example, the correction unit can provide quiz-style or interactive simulations to help users understand the correction information. The correction unit can also build an interface that allows users to provide feedback on the correction information and improve the correction content based on user reactions. Furthermore, the correction unit can monitor the reception status of the correction information provided in an interactive format and user reactions, and make additional corrections or explanations as necessary. This makes it possible to not only post corrections on SNS but also provide correct information to a wide range of users in an interactive format, thereby preventing the spread of rumors and slander.

[0058] When presenting the verification results to users, the verification department not only provides detailed explanations of the information sources used and their evaluation criteria, but can also publish the verification process in real time to increase transparency. For example, the verification department can publish the verification process through live streaming or a real-time update feed, allowing users to check the progress of the verification. The verification department can also provide detailed explanations of the tools, technologies, and data sources used in the verification process to ensure transparency to users. Furthermore, the verification department can accept questions and feedback from users in real time and incorporate them into the verification process. This not only increases the presentation of verification results but also increases the transparency of the verification process, thereby gaining user trust.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The information collection unit collects posts on social media. For example, the information collection unit collects posts that include specific keywords or hashtags. The information collection unit can also collect posts on social media using an API. Furthermore, the information collection unit can also collect posts on social media using scraping technology. Step 2: The analysis unit analyzes the collected posts. For example, the analysis unit analyzes the content of the posts using natural language processing technology. The analysis unit can also analyze the content of images and videos included in the posts using image analysis technology. Furthermore, the analysis unit can analyze the emotions of the poster using an emotion estimation function and preferentially detect emotionally extreme posts. Step 3: The verification department verifies the authenticity of the analyzed information. For example, the verification department verifies the accuracy of the information by referring to reliable sources such as official news sites, official company announcements, and academic papers. The verification department can also cross-reference multiple reliable sources to verify the accuracy of the information from multiple angles. Furthermore, the verification department can refer to a database that is updated in real time. Step 4: The Corrections Department corrects any information determined to be false. For example, the Corrections Department may post a correction on social media to spread the correct information. The Corrections Department may also work with the company's public relations department to provide official corrections. After making a correction, the Corrections Department may monitor how well the correction has been received and make additional corrections or clarifications as necessary.

[0061] (Example 2) The information verification system according to an embodiment of the present invention is a system in which AI verifies the authenticity of information and corrects false information posted on social media. This enables the information verification system to quickly and accurately respond to false information and slander posted on social media, and spread correct information.

[0062] An information verification system according to an embodiment includes an information collection unit, an analysis unit, a verification unit, and a correction unit. The information collection unit collects posts on social media. For example, the information collection unit collects posts containing specific keywords or hashtags. The information collection unit can also collect posts on social media using an API. The information collection unit can also collect posts on social media using scraping technology. The analysis unit analyzes the collected posts. For example, the analysis unit can analyze the content of the posts using natural language processing technology. The analysis unit can also analyze the content of images and videos included in the posts using image analysis technology. The analysis unit can also analyze the emotions of posters using an emotion estimation function to preferentially detect emotionally extreme posts. The verification unit verifies the authenticity of the analyzed information. For example, the verification unit can verify the accuracy of the information by referring to reliable information sources such as official news sites, official company announcements, and academic papers. The verification unit can also cross-reference multiple reliable information sources to verify the accuracy of the information from multiple angles. The verification unit can also refer to a database updated in real time. The correction unit corrects information determined to be false information. For example, the correction unit posts a correction on a social networking site to spread correct information. The correction unit can also work with a company's public relations department to provide official corrected information. After making a correction, the correction unit can monitor the extent to which the correction has been accepted and make additional corrections or explanations as necessary. This allows the information verification system according to the embodiment to quickly and accurately respond to rumors and slander on social networking sites and spread correct information. For example, the information verification system can protect a company's credibility by detecting rumors and slander on social networking sites and providing correct information. The information verification system can also conduct awareness-raising activities to prevent the spread of rumors and provide users with methods for verifying correct information. This can improve the reliability of information throughout society.

[0063] The information collection unit can collect posts that include specific keywords and hashtags. The information collection unit, for example, collects posts that include specific keywords and hashtags. For example, AI collects posts on social media and analyzes the poster's emotions using an emotion estimation function. For example, posts that contain strong emotions such as anger or hatred are preferentially detected. The emotion estimation function is also used to calculate the poster's emotion score and filter out emotionally extreme posts. For example, posts with an emotion score above a certain level are preferentially analyzed. The emotion estimation function is also used to analyze the type and intensity of emotion in the posts collected by AI, and particularly emotionally extreme posts are preferentially detected. This makes it possible to efficiently collect posts that include specific keywords and hashtags.

[0064] The analysis unit can use the emotion estimation function to analyze the emotions of the poster and prioritize detecting emotionally extreme posts. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of the poster and prioritize detecting emotionally extreme posts. For example, AI analyzes the poster's past posting history to detect a tendency for rumoring or slander. For example, it prioritizes analyzing posts by posters who have a history of spreading rumor. It also analyzes the reactions of followers to evaluate the credibility of the post. For example, it determines that posts that receive a lot of negative reactions from followers are likely to be rumored or slanderous. It also comprehensively analyzes the poster's past posting history and the reactions of followers to accurately determine the possibility of rumoring or slander. For example, it calculates a credibility score based on the past posting history and the reactions of followers. This makes it possible to prioritize detecting emotionally extreme posts.

[0065] The verification unit can verify the accuracy of information by referring to reliable sources such as official news sites, official company announcements, and academic papers. The verification unit can verify the accuracy of information by referring to reliable sources such as official news sites, official company announcements, and academic papers. For example, the verification unit uses an emotion estimation function to evaluate the reliability of information sources referenced by the AI, and prioritizes the use of emotionally neutral sources. For example, it selects information sources with low emotion scores. Furthermore, it uses the emotion estimation function to evaluate the emotional bias of information sources, and prioritizes the use of neutral sources. For example, it selects emotionally neutral news sites. Furthermore, it builds a system that uses the emotion estimation function to evaluate the reliability of information sources referenced by the AI, and prioritizes the use of emotionally neutral sources. For example, it filters information sources based on emotion scores. This makes it possible to verify the accuracy of information by referring to reliable sources.

[0066] The correction unit can post corrections on social media to spread correct information. The correction unit, for example, posts corrections on social media to spread correct information. For example, when the AI ​​corrects misinformation, it uses an emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it adjusts the correction method based on the emotion score. Furthermore, it uses the emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it makes corrections using positive expressions. Furthermore, when the AI ​​corrects misinformation, it uses the emotion estimation function to evaluate the emotional impact that the corrections have on the user and selects a correction method that elicits positive emotions. For example, it adjusts the corrections based on the emotion score. This allows correct information to be spread on social media.

[0067] The information collection unit can also collect information from online platforms other than SNS. For example, the information collection unit collects information from online platforms other than SNS. For example, AI collects information from online platforms other than SNS and analyzes a wide range of data sources. For example, blog and forum posts are collected and analyzed. The information collected from online platforms is also integrated and analyzed for the possibility of rumors or slander. For example, SNS and blog posts are analyzed in an integrated manner. In addition, in order to analyze a wide range of data sources, information is collected from online platforms other than SNS and the possibility of rumors or slander is determined with high accuracy. For example, forum posts are collected and analyzed. This allows information to be collected from online platforms other than SNS.

[0068] The analysis unit can also use image analysis technology to analyze the content of images and videos included in posts, and detect non-textual hoaxes and slander. For example, the analysis unit can use image analysis technology to analyze the content of images and videos included in posts, and detect non-textual hoaxes and slander. For example, it can analyze images and videos included in posts collected by AI to detect the possibility of hoaxes and slander. For example, it can use image recognition technology to detect defamatory images. It can also use image analysis technology to convert the content of images and videos included in posts into text data and analyze the possibility of hoaxes and slander. For example, it can extract and analyze text from images. It can also analyze images and videos included in posts collected by AI to detect non-textual hoaxes and slander. For example, it can analyze the audio in videos to detect defamatory remarks. This makes it possible to detect non-textual hoaxes and slander.

[0069] The verification unit can cross-reference multiple highly reliable information sources and verify the accuracy of information from multiple angles. The verification unit, for example, cross-references multiple highly reliable information sources and verify the accuracy of information from multiple angles. For example, when AI verifies the authenticity of information, it cross-references multiple highly reliable information sources and verify the accuracy of the information from multiple angles. For example, it refers to news sites and academic papers. In addition, a system is built that cross-references multiple information sources and verify the accuracy of information. For example, it compares official announcements with news articles. In addition, when AI verifies the authenticity of information, it cross-references multiple highly reliable information sources and verify the accuracy of the information from multiple angles. For example, it integrates and analyzes data from different sources. This allows the accuracy of the information to be verified from multiple angles.

[0070] The verification unit can explain in detail the information sources used and their evaluation criteria when presenting the verification results to the user. For example, when presenting the verification results to the user, the verification unit explains in detail the information sources used and their evaluation criteria. For example, when the AI ​​presents the verification results to the user, it explains in detail the information sources used and their evaluation criteria. For example, it displays a list of the news sites and academic papers referenced. Furthermore, to ensure transparency in the verification process, a system is built that explains in detail the information sources used and their evaluation criteria. For example, it displays a score based on the evaluation criteria. Furthermore, when the AI ​​presents the verification results to the user, it explains in detail the information sources used and their evaluation criteria. For example, it displays the reliability score of the information sources. This ensures transparency in the verification process.

[0071] The verification unit can refer to a database that is updated in real time. The verification unit, for example, refers to a database that is updated in real time. For example, breaking news that is updated in real time may be added to the information sources referenced by the AI ​​to verify the authenticity of the information. For example, the latest news articles may be referenced. The accuracy of the information may also be confirmed by referencing a database of academic papers that is updated in real time. For example, information may be verified based on the latest research results. The verification unit may also add a database that is updated in real time to the information sources referenced by the AI ​​to build a system that verifies the authenticity of information. For example, breaking news and the latest information on academic papers may be integrated. This makes it possible to confirm the accuracy of the information based on the latest information.

[0072] The verification unit can collect opinions of experts in different industries and fields and verify the authenticity of information from multiple angles. The verification unit, for example, collects opinions of experts in different industries and fields and verifies the authenticity of information from multiple angles. For example, the verification unit collects opinions of experts in different industries and fields and builds a system that verifies the authenticity of information from multiple angles. For example, the verification unit refers to the opinions of experts in medicine, law, technology, etc., and also confirms the accuracy of information based on the expert opinions. For example, information in the medical field is verified based on the opinions of doctors. Also, the verification unit collects opinions of experts in different industries and fields and verifies the authenticity of information from multiple angles. For example, information in the technical field is verified based on the opinions of engineers. This makes it possible to verify the authenticity of information from multiple angles based on the opinions of experts in different industries and fields.

[0073] The verification unit can use the emotion estimation function to identify the information source that the user trusts most and prioritize the use of that information source. The verification unit, for example, can use the emotion estimation function to identify the information source that the user trusts most and prioritize the use of that information source. For example, the emotion estimation function can be used to identify the information source that the user trusts most and prioritize the use of that information source. For example, the verification unit can select a highly reliable information source based on the user's emotion score. Also, a system can be constructed that analyzes the user's emotional response and identifies the most trusted information source. For example, information sources with a large number of positive emotional responses can be used preferentially. Also, the emotion estimation function can be used to identify the information source that the user trusts most and prioritize the use of that information source. For example, information sources can be filtered based on the emotion score. This allows the information source that the user trusts most to be used preferentially.

[0074] The correction unit can use the emotion estimation function to evaluate the emotional impact of the correction content on the user and select a correction method that elicits positive emotions. The correction unit, for example, uses the emotion estimation function to evaluate the emotional impact of the correction content on the user and selects a correction method that elicits positive emotions. For example, when the AI ​​corrects misinformation, it uses the emotion estimation function to evaluate the emotional impact of the correction content on the user and selects a correction method that elicits positive emotions. For example, it adjusts the correction method based on the emotion score. Furthermore, it uses the emotion estimation function to evaluate the emotional impact of the correction content on the user and selects a correction method that elicits positive emotions. For example, it performs a correction using positive expressions. Furthermore, when the AI ​​corrects misinformation, it uses the emotion estimation function to evaluate the emotional impact of the correction content on the user and selects a correction method that elicits positive emotions. For example, it adjusts the correction content based on the emotion score. This makes it possible to select a correction method that elicits positive emotions.

[0075] The correction unit can generate infographics and videos to make the corrections visually easy to understand. The correction unit generates infographics and videos to make the corrections visually easy to understand, for example. For example, when AI corrects misinformation, it generates visually easy-to-understand infographics. For example, it shows the corrections in diagrams and graphs. Furthermore, AI generates videos to make the corrections visually easy to understand. For example, it explains the corrections using animations. Furthermore, a system is constructed to generate visually easy-to-understand infographics and videos when AI corrects misinformation. For example, a tool is provided that visually displays the corrections. This makes it possible to provide the corrections visually easy to understand.

[0076] After making a correction, the correction unit monitors how well the correction is accepted and can make additional corrections or explanations as necessary. For example, after making a correction, the correction unit monitors how well the correction is accepted and can make additional corrections or explanations as necessary. For example, after AI corrects misinformation, it monitors how well the correction is accepted. For example, it analyzes user reactions and comments. It also builds a system that monitors whether the correction is accepted and makes additional corrections or explanations as necessary. For example, it makes additional corrections based on user feedback. It also monitors how well the correction is accepted after AI makes a correction and can make additional corrections or explanations as necessary. For example, it analyzes user reactions to the corrections in real time. This makes it possible to monitor whether the correction is accepted and make additional corrections or explanations as necessary.

[0077] The correction unit can simultaneously post correction information on different social media platforms, providing correct information to a wide range of users. The correction unit, for example, simultaneously posts correction information on different social media platforms, providing correct information to a wide range of users. For example, when AI corrects misinformation, it posts the correction information on different social media platforms simultaneously. For example, it posts simultaneously on Twitter, Facebook, Instagram, etc. In addition, a system can be built that simultaneously posts correction information on different social media platforms, providing correct information to a wide range of users. For example, a tool can be provided that automatically posts on multiple platforms. In addition, when AI corrects misinformation, it posts the correction information on different social media platforms simultaneously, providing correct information to a wide range of users. For example, information can be shared between social media platforms. This allows correction information to be simultaneously posted on different social media platforms, providing correct information to a wide range of users.

[0078] The correction unit provides correction information in multiple languages, making it possible to accommodate international users. The correction unit, for example, provides correction information in multiple languages, making it possible to accommodate international users. For example, when AI corrects misinformation, the correction information is provided in multiple languages. For example, correction information is posted in multiple languages, such as English, French, and Chinese. In addition, a system is built that provides correction information in multiple languages ​​and accommodates international users. For example, a correction information posting tool with a translation function is provided. In addition, when AI corrects misinformation, the correction information is provided in multiple languages, making it possible to accommodate international users. For example, correction information in different languages ​​is posted simultaneously. This makes it possible to provide correction information in multiple languages, making it possible to accommodate international users.

[0079] The correction unit can use the emotion estimation function to monitor the user's emotional reaction to the correction information in real time and continuously search for an optimal correction method. The correction unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the correction information in real time and continuously search for an optimal correction method. For example, the emotion estimation function is used to monitor the user's emotional reaction to the correction information in real time. For example, the user's facial expressions and comments are analyzed. Furthermore, a system is constructed that monitors the user's emotional reaction to the correction information in real time and continuously searches for an optimal correction method. For example, the correction method is adjusted based on the emotion score. Furthermore, the emotion estimation function is used to monitor the user's emotional reaction to the correction information in real time and continuously search for an optimal correction method. For example, the correction content is adjusted based on the user's emotional reaction. In this way, the user's emotional reaction to the correction information can be monitored in real time and continuously search for an optimal correction method.

[0080] When detecting slander against a company name or product name, the analysis unit can use the emotion estimation function to evaluate the emotional intensity of the slander and prioritize responding to particularly strong slander. For example, when detecting slander against a company name or product name, the analysis unit can use the emotion estimation function to evaluate the emotional intensity of the slander and prioritize responding to particularly strong slander. For example, when AI detects slander against a company name or product name, the emotion estimation function can be used to evaluate the emotional intensity of the slander and prioritize responding to particularly strong slander. For example, slander with a high emotion score can be prioritized for analysis. Furthermore, the emotion estimation function can be used to evaluate the emotional intensity of slander and prioritize responding to particularly strong slander. For example, the priority of slander can be determined based on the emotion score. Furthermore, when AI detects slander against a company name or product name, the emotion estimation function can be used to evaluate the emotional intensity of the slander and prioritize responding to particularly strong slander. For example, the response method for slander can be adjusted based on the emotion score. This makes it possible to prioritize responding to particularly strong slander.

[0081] The verification unit can cross-reference multiple reliable information sources, such as official company announcements and product review sites, to verify the authenticity of slander. For example, when AI verifies the authenticity of slander, it cross-references multiple reliable information sources, such as official company announcements and product review sites. For example, it compares information from official announcements and review sites. In addition, a system can be built that cross-references multiple reliable information sources to verify the authenticity of slander. For example, it confirms the authenticity of slander based on official announcements and news articles. In addition, when AI verifies the authenticity of slander, it cross-references multiple reliable information sources, such as official company announcements and product review sites. For example, it integrates and analyzes data from different sources. This allows the authenticity of slander to be verified from multiple angles.

[0082] The corrections department can work with a company's public relations department to provide official corrections. The corrections department, for example, can work with a company's public relations department to provide official corrections. For example, when AI makes a correction to defamatory content, it can work with the company's public relations department to provide official corrections. For example, it can post the corrections provided by the public relations department on social media. In addition, it can work with a company's public relations department to build a system that provides official corrections. For example, it can automatically collect information from the public relations department and post it as corrections. In addition, when AI makes a correction to defamatory content, it can work with a company's public relations department to provide official corrections. For example, it can work with the public relations department to create corrections and post them on social media. This allows it to work with a company's public relations department to provide official corrections.

[0083] The analysis unit can also monitor different social media platforms and online forums when detecting slander against company names or product names. For example, when the AI ​​detects slander against company names or product names, it also monitors different social media platforms and online forums. For example, it monitors Twitter, Facebook, Reddit, etc. A system can also be built that monitors different social media platforms and online forums to detect slander. For example, it can collect and analyze information from multiple platforms. Furthermore, when the AI ​​detects slander against company names or product names, it can also monitor different social media platforms and online forums. For example, it can collect and analyze forum posts. By monitoring different social media platforms and online forums, it is possible to collect a wide range of information and detect slander.

[0084] The corrections department can also post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. The corrections department can, for example, post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, AI can post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be posted on the official website. A system can also be built that posts corrections to the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be automatically added to newsletters. AI can also post corrections to defamatory content on the company's official website and in newsletters, thereby providing correct information to a wide range of users. For example, corrections can be posted on the official website and in newsletters simultaneously. This allows corrections to be posted on the company's official website and in newsletters, thereby providing correct information to a wide range of users.

[0085] The analysis unit can use the emotion estimation function to evaluate the emotional impact of rumors and prioritize responding to particularly emotionally intense rumors in order to prevent the spread of rumors. The analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact of rumors and prioritize responding to particularly emotionally intense rumors in order to prevent the spread of rumors. For example, in order to prevent the spread of rumors, AI uses the emotion estimation function to evaluate the emotional impact of rumors and prioritize responding to particularly emotionally intense rumors. For example, rumors with high emotion scores are prioritized for analysis. Furthermore, the emotion estimation function is used to build a system that evaluates the emotional impact of rumors and prioritize responding to particularly emotionally intense rumors. For example, the priority of rumors is determined based on the emotion score. Furthermore, in order to prevent the spread of rumors, AI uses the emotion estimation function to evaluate the emotional impact of rumors and prioritize responding to particularly emotionally intense rumors. For example, a method for responding to rumors is adjusted based on the emotion score. This makes it possible to prioritize responding to particularly emotionally intense rumors.

[0086] The analysis unit can monitor trends and buzzwords on social media in real time to detect the spread of rumors early. The analysis unit, for example, monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, AI monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it monitors hashtags that are trending rapidly. In addition, a system is built that monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it analyzes the frequency of appearance of trending words. In addition, AI monitors trends and buzzwords on social media in real time to detect the spread of rumors early. For example, it analyzes the appearance patterns of buzzwords. This makes it possible to monitor trends and buzzwords on social media in real time and detect the spread of rumors early.

[0087] The analysis unit can generate visually easy-to-understand infographics and videos when conducting awareness-raising activities to prevent the spread of rumors. The analysis unit generates visually easy-to-understand infographics and videos, for example, when conducting awareness-raising activities to prevent the spread of rumors. For example, when AI conducts awareness-raising activities to prevent the spread of rumors, it generates visually easy-to-understand infographics. For example, it illustrates the impact of rumors and how to confirm correct information using diagrams. Furthermore, when conducting awareness-raising activities, AI generates visually easy-to-understand videos. For example, it explains the steps to prevent the spread of rumors using animations. Furthermore, a system is constructed that generates visually easy-to-understand infographics and videos when AI conducts awareness-raising activities to prevent the spread of rumors. For example, a tool is provided that visually displays the awareness content. This makes it possible to provide awareness-raising activities to prevent the spread of rumors in a visually easy-to-understand manner.

[0088] The analysis unit can also monitor different social media platforms and online forums to prevent the spread of rumors. For example, the analysis unit can monitor different social media platforms and online forums to prevent the spread of rumors. For example, the analysis unit can monitor different social media platforms and online forums to prevent the spread of rumors using AI. For example, it can monitor Twitter, Facebook, Reddit, etc. In addition, a system can be built to monitor different social media platforms and online forums to prevent the spread of rumors. For example, it can collect and analyze information from multiple platforms. In addition, in order to prevent the spread of rumors using AI, it can collect and analyze forum posts. By monitoring different social media platforms and online forums, a wide range of information can be collected and the spread of rumors can be prevented.

[0089] The analysis unit can provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. The analysis unit, for example, can provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. For example, AI can provide awareness-raising activities to prevent the spread of rumors in multiple languages. For example, awareness information can be disseminated in multiple languages, such as English, French, and Chinese. A system can also be built to provide multilingual awareness-raising activities and accommodate international users. For example, an awareness-raising information dissemination tool with a translation function can be provided. AI can also provide awareness-raising activities to prevent the spread of rumors in multiple languages, and can also accommodate international users. For example, awareness information can be disseminated in different languages ​​simultaneously. This allows awareness-raising activities to prevent the spread of rumors to be provided in multiple languages, and can also accommodate international users.

[0090] The analysis unit can use the emotion estimation function to monitor users' emotional reactions to rumors in real time and continuously search for optimal awareness methods. The analysis unit, for example, uses the emotion estimation function to monitor users' emotional reactions to rumors in real time and continuously search for optimal awareness methods. For example, the emotion estimation function is used to monitor users' emotional reactions to rumors in real time. For example, the user's facial expressions and comments are analyzed. A system is also constructed that monitors users' emotional reactions to rumors in real time and continuously search for optimal awareness methods. For example, the awareness method is adjusted based on the emotion score. The emotion estimation function is also used to monitor users' emotional reactions to rumors in real time and continuously search for optimal awareness methods. For example, the awareness content is adjusted based on the user's emotional reactions. In this way, it is possible to monitor users' emotional reactions to rumors in real time and continuously search for optimal awareness methods.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information gathering unit can not only collect posts on social media, but also collect users' browsing history and search history. For example, the information gathering unit can collect websites visited by users in the past and keywords searched by users, and analyze the possibility of hoaxes or slander based on this data. The information gathering unit can also analyze users' browsing history and, if a large amount of information from a particular website is included, evaluate the reliability of that information source. Furthermore, the information gathering unit can detect the possibility of hoaxes or slander on a specific topic early on based on the user's search history. This allows the system to monitor not only posts on social media but also the user's entire online activity, improving the accuracy of detecting hoaxes and slander.

[0093] The analysis unit can not only analyze the poster's emotions using the emotion estimation function, but also analyze the context and background information of the post. For example, the analysis unit can analyze background information such as the time and location of the post and the poster's past posting history to evaluate the possibility of it being a hoax or slander. The analysis unit can also analyze the context of the post to detect trends in hoaxes and slander on specific topics. Furthermore, the analysis unit can analyze the reactions and comments of the poster's followers to evaluate the credibility of the post. This allows for comprehensive analysis of the context and background information of the post, in addition to the emotion estimation function, and improves the accuracy of detecting hoaxes and slander.

[0094] The verification department can verify the accuracy of information not only by referring to reliable sources such as official news sites, official company announcements, and academic papers, but also by utilizing crowdsourcing. For example, the verification department can collect feedback from experts and general users through a crowdsourcing platform and evaluate the accuracy of the information. The verification department can also use crowdsourcing to gather expert opinions on specific topics and verify the accuracy of the information from multiple perspectives. Furthermore, the verification department can collect information updated in real time through crowdsourcing, allowing for early detection of possible rumors and slander. This allows the accuracy of information to be verified not only through reliable sources but also through crowdsourcing, thereby improving the accuracy of detecting rumors and slander.

[0095] The correction unit can not only post corrections on social media but also provide users with correction information via push notifications. For example, when false information spreads, the correction unit can send correction information to relevant users via push notifications, quickly providing correct information. The correction unit can also provide individually customized correction information based on the user's interests and the accounts they follow. Furthermore, the correction unit can monitor the reception status of the correction information and user reactions through push notifications, and make additional corrections or explanations as necessary. This makes it possible to quickly provide correct information to a wide range of users by utilizing push notifications in addition to posting corrections on social media, preventing the spread of rumors and slander.

[0096] The information collection unit can not only collect information from online platforms other than social media, but also collect data from offline sources. For example, the information collection unit can collect information from offline media such as newspapers, magazines, and television news, and analyze it for possible rumors and slander. The information collection unit can also collect information presented at public events, conferences, seminars, etc., and use it to detect rumors and slander. Furthermore, the information collection unit can integrate data collected from offline sources with online information and perform comprehensive analysis. This allows the system to collect data not only from social media and online platforms, but also from offline sources, thereby improving the accuracy of detecting rumors and slander.

[0097] The analysis unit can not only analyze the content of images and videos included in posts using image analysis technology, but also analyze audio data included in posts using audio analysis technology. For example, the analysis unit can analyze audio data included in posts to detect the possibility of hoaxes or slander. The analysis unit can also use audio analysis technology to analyze the tone of voice and emotions of the poster to preferentially detect emotionally extreme posts. Furthermore, the analysis unit can convert audio data into text data and use text analysis technology to analyze the possibility of hoaxes or slander. This allows for the analysis of not only images and videos but also audio data, improving the accuracy of detecting hoaxes and slander.

[0098] The verification unit can verify the accuracy of information not only by cross-referencing multiple reliable information sources but also by utilizing blockchain technology. For example, the verification unit can refer to information recorded on the blockchain to prevent information tampering or fraud. The verification unit can also use blockchain technology to track the source and history of information and evaluate the reliability of the information. Furthermore, the verification unit can build a database that is updated in real time based on the information recorded on the blockchain, allowing for early detection of possible rumors and slander. This allows the accuracy of information to be verified not only by multiple reliable information sources but also by utilizing blockchain technology, improving the accuracy of detecting rumors and slander.

[0099] The correction unit can not only post corrections on SNS but also provide users with correction information in an interactive format. For example, the correction unit can provide quiz-style or interactive simulations to help users understand the correction information. The correction unit can also build an interface that allows users to provide feedback on the correction information and improve the correction content based on user reactions. Furthermore, the correction unit can monitor the reception status of the correction information provided in an interactive format and user reactions, and make additional corrections or explanations as necessary. This makes it possible to not only post corrections on SNS but also provide correct information to a wide range of users in an interactive format, thereby preventing the spread of rumors and slander.

[0100] When detecting slander against company or product names, the analysis unit can use the emotion estimation function to evaluate not only the emotional intensity of the slander, but also the influence of the poster. For example, the analysis unit can analyze the poster's number of followers and engagement rate, and prioritize responses to slander from highly influential posters. The analysis unit can also analyze the poster's past posting history to evaluate the tendency of rumors and slander to spread. Furthermore, the analysis unit can comprehensively evaluate the poster's influence and emotional intensity, and prioritize responses to particularly influential slander. This makes it possible to detect slander and determine response priorities by taking into account not only emotional intensity but also the poster's influence.

[0101] When presenting the verification results to users, the verification department not only provides detailed explanations of the information sources used and their evaluation criteria, but can also publish the verification process in real time to increase transparency. For example, the verification department can publish the verification process through live streaming or a real-time update feed, allowing users to check the progress of the verification. The verification department can also provide detailed explanations of the tools, technologies, and data sources used in the verification process to ensure transparency to users. Furthermore, the verification department can accept questions and feedback from users in real time and incorporate them into the verification process. This not only increases the presentation of verification results but also increases the transparency of the verification process, thereby gaining user trust.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The information collection unit collects posts on social media. For example, the information collection unit collects posts that include specific keywords or hashtags. The information collection unit can also collect posts on social media using an API. Furthermore, the information collection unit can also collect posts on social media using scraping technology. Step 2: The analysis unit analyzes the collected posts. For example, the analysis unit analyzes the content of the posts using natural language processing technology. The analysis unit can also analyze the content of images and videos included in the posts using image analysis technology. Furthermore, the analysis unit can analyze the emotions of the poster using an emotion estimation function and preferentially detect emotionally extreme posts. Step 3: The verification department verifies the authenticity of the analyzed information. For example, the verification department verifies the accuracy of the information by referring to reliable sources such as official news sites, official company announcements, and academic papers. The verification department can also cross-reference multiple reliable sources to verify the accuracy of the information from multiple angles. Furthermore, the verification department can refer to a database that is updated in real time. Step 4: The Corrections Department corrects any information determined to be false. For example, the Corrections Department may post a correction on social media to spread the correct information. The Corrections Department may also work with the company's public relations department to provide official corrections. After making a correction, the Corrections Department may monitor how well the correction has been received and make additional corrections or clarifications as necessary.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. An information gathering department that collects posts on social media; an analysis unit that analyzes the posts collected by the information collection unit; a verification unit that verifies the authenticity of the information analyzed by the analysis unit; a correction unit that corrects information determined to be erroneous by the verification unit. A system characterized by:

2. The information collecting unit Collect posts containing specific keywords or hashtags 2. The system of claim 1.

3. The analysis unit Analyzes posters' emotions and prioritizes detecting emotionally extreme posts 2. The system of claim 1.

4. The verification unit Verify the accuracy of information by referring to reliable sources such as official news sites, company announcements, and academic papers.

2. The system of claim 1.

5. The correction unit Post the correction on the SNS and spread the correct information.

2. The system of claim 1.

6. The information collecting unit Collect information from online platforms other than social media 2. The system of claim 1.

7. The analysis unit Image analysis technology is used to analyze the content of images and videos contained in the posts, and to detect non-textual hoaxes and slander.

2. The system of claim 1.

8. The verification unit Cross-reference multiple reliable sources of information to verify the accuracy of the information from multiple angles 2. The system of claim 1.

Images