system

Abstract

The system according to this embodiment aims to detect false information on social media in real time and provide users with accurate information. [Solution] The system according to the embodiment comprises a detection unit, a notification unit, and a provision unit. The detection unit monitors posts on social media in real time and detects information that may be false. The notification unit notifies the user of the false information detected by the detection unit. The provision unit provides accurate information based on the false information notified by the notification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to distinguish the authenticity of information flowing on SNS, and there is a risk that accurate information will be buried due to the spread of false information.

[0005] The system according to the embodiment aims to detect false information on SNS in real time and provide accurate information to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, a notification unit, and a provision unit. The detection unit monitors posts on social media in real time and detects information that may be false. The notification unit notifies the user of the false information detected by the detection unit. The provision unit provides accurate information based on the false information notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect false information on social media in real time and provide users with accurate information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The hoax detection system according to an embodiment of the present invention is a system that monitors posts on social media in real time, immediately detects potentially false information, and notifies users to provide accurate information. The hoax detection system monitors posts on social media in real time and detects potentially false information. If false information is detected, it notifies the user and provides accurate information. This mechanism improves the reliability of information and prevents the spread of false information. The hoax detection system monitors posts on social media in real time. In this process, the AI ​​analyzes the content of the posts and identifies potentially false information. For example, when detecting false information about elections, the accuracy of the citation can be verified by comparing it with a database of past statements. This allows for the immediate detection of potentially false information. If false information is detected, it notifies the user and provides accurate information. For example, if false information is detected, the reliability of the information can be improved by immediately notifying the user and presenting accurate information. This prevents the spread of false information and prevents accurate information from being buried. This mechanism improves the reliability of information and prevents the spread of false information. For example, by detecting election-related misinformation in real time and providing users with accurate information, it can help voters make informed decisions. It can also address the challenge of discerning the truthfulness of information circulating on social media and provide a means to quickly obtain accurate information. Thus, using a misinformation checker AI improves the reliability of information on social media, prevents the spread of misinformation, and enables the rapid acquisition of accurate information. In this way, the misinformation detection system can improve the reliability of information by detecting misinformation on social media in real time and providing users with accurate information.

[0029] The misinformation detection system according to this embodiment comprises a detection unit, a notification unit, and a provision unit. The detection unit monitors posts on social media in real time and detects information that may be misinformation. The detection unit analyzes the content of posts using AI, for example, to identify information that may be misinformation. For example, when the detection unit detects misinformation about an election, it can verify the accuracy of the quote by comparing it with a database of past statements. The detection unit can also analyze the context of the post and identify keywords that are likely to be misinformation. Furthermore, the detection unit can analyze the content of images and videos in posts and evaluate the likelihood of them being misinformation by comparing them with text information. The notification unit notifies the user of the misinformation detected by the detection unit. The notification unit notifies the user immediately when misinformation is detected, for example. The notification unit can also estimate the user's emotions and adjust the way the notification is expressed based on the estimated emotions of the user. Furthermore, the notification unit can adjust the urgency of the notification based on the impact of the misinformation. The provision unit provides accurate information based on the misinformation notified by the notification unit. The information provider unit, for example, presents accurate information to the user. The information provider unit may also include a reliability evaluation unit that evaluates the reliability of the accurate information. Furthermore, the information provider unit can estimate the user's emotions and adjust the method of providing accurate information based on the estimated user emotions. As a result, the misinformation detection system according to this embodiment can improve the reliability of information by detecting misinformation on social media in real time and providing accurate information to the user.

[0030] The detection unit monitors posts on social media in real time and detects potentially false information. Specifically, it uses AI to analyze the content of posts and identify potentially false information. The AI ​​utilizes natural language processing technology to analyze the text of posts and detect contextual and keyword patterns. For example, when detecting false information about elections, it can verify the accuracy of quotations by comparing them with a database of past statements. The AI ​​searches the database of past statements for relevant statements and compares them with the content of the post to determine whether the quotation is accurate. It can also analyze the context of the post and identify keywords that are likely to be false. For example, if keywords such as "election fraud" or "conspiracy theory" are included, it is judged to be highly likely to be false. Furthermore, the detection unit can also analyze the content of images and videos in posts and evaluate the likelihood of them being false by comparing them with the text information. Using image recognition technology, it analyzes the content of posted images and videos and checks whether they match the text information. For example, if an image showing election fraud is posted, it checks whether the image is from a past election and evaluates the likelihood of it being false. As a result, the detection unit can detect misinformation with high accuracy through multifaceted analysis of text, images, and videos.

[0031] The notification unit notifies users of false information detected by the detection unit. Specifically, it immediately notifies users when false information is detected. Notifications are displayed as pop-up or push notifications on the SNS platform. The notification unit can also estimate the user's emotions and adjust the way the notification is presented based on the estimated emotions. For example, if the user is feeling angry or anxious, the notification will be worded more gently and display a message encouraging a calm response. Emotion estimation uses AI technology to analyze the user's past posts and reactions to estimate their emotional state. Furthermore, the notification unit can adjust the urgency of the notification based on the impact of the false information. For example, if the false information is widely disseminated or has a significant social impact, it will issue a high-urgency notification to encourage a quick response from the user. In this way, the notification unit can notify users of false information at the appropriate time and in the appropriate manner, supporting a quick and calm response.

[0032] The information provider unit provides accurate information based on the misinformation notified by the notification unit. Specifically, it presents accurate information to users. The information provider unit provides accurate information against misinformation based on data collected from reliable sources. For example, if misinformation regarding an election is detected, it presents users with accurate information provided by the election commission or other public institutions. The information provider unit may also include a reliability evaluation unit that assesses the reliability of accurate information. The reliability evaluation unit assesses the reliability of the information provided based on the reliability of the information source and past performance, and guarantees the quality of the information presented to users. Furthermore, the information provider unit can estimate the user's emotions and adjust the method of providing accurate information based on the estimated user emotions. For example, if a user is feeling anxious, it may provide information in a reassuring manner to encourage a calm response. In this way, the information provider unit can provide users with accurate and reliable information and minimize confusion caused by misinformation.

[0033] The detection unit can verify the accuracy of quotations by comparing them with a database of past statements. For example, the detection unit can use the database of past statements to verify the accuracy of quotations in posts. For example, the detection unit can evaluate the reliability of the source and verify the accuracy of the quotation. The detection unit can also evaluate the degree of agreement with the original text and verify the accuracy of the quotation. Furthermore, the detection unit can use the database of past statements to evaluate the reliability of the source. This allows the detection unit to verify the accuracy of quotations and improve the accuracy of detecting misinformation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the database of past statements into a generating AI and have the generating AI perform the verification of the accuracy of quotations.

[0034] The notification unit can immediately notify the user when false information is detected. For example, the notification unit will immediately notify the user when false information is detected. For example, the notification unit will notify within a few seconds when false information is detected. The notification unit can also notify within a few minutes when false information is detected. Furthermore, the notification unit can build a system to immediately notify when false information is detected. This allows the user to quickly obtain accurate information by immediately notifying when false information is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use an AI model to immediately notify when false information is detected.

[0035] The service provider can present accurate information to users. For example, the service provider can provide accurate information to users. For example, the service provider can provide information from reliable sources. The service provider can also provide verified information. Furthermore, the service provider can build a system for providing accurate information. This allows the service provider to improve the reliability of information by presenting accurate information to users. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model to provide accurate information.

[0036] The detection unit can detect election-related misinformation in real time. For example, the detection unit can detect election-related misinformation in real time. For example, the detection unit can detect false candidate information related to elections. The detection unit can also detect incorrect voting methods related to elections. Furthermore, the detection unit can build a system for detecting election-related misinformation in real time. As a result, the detection unit can provide accurate information related to elections by detecting election-related misinformation in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can use an AI model for detecting election-related misinformation in real time.

[0037] The information provider may include a reliability evaluation unit that evaluates the reliability of accurate information. For example, the information provider may evaluate the reliability of information sources. The information provider may also evaluate past performance. Furthermore, the information provider may build a system for evaluating the reliability of accurate information. This allows the information provider to improve the reliability of the information it provides by evaluating the reliability of accurate information. Some or all of the above-described processes in the information provider may be performed using AI, for example, or without AI. For example, the information provider may use an AI model for evaluating the reliability of accurate information.

[0038] The detection unit can analyze the context of a post and identify keywords that are likely to be misinformation. For example, the detection unit can analyze the context of a post and detect specific keywords related to elections. The detection unit can also analyze the context of a post and identify misinformation about specific people or organizations. Furthermore, the detection unit can analyze the context of a post and identify misinformation about specific events. In this way, the detection unit can improve the accuracy of misinformation detection by identifying keywords that are likely to be misinformation by analyzing the context of a post. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input contextual data of a post into a generating AI and have the generating AI identify keywords that are likely to be misinformation.

[0039] The detection unit can analyze the content of images and videos in posts and evaluate the likelihood of them being misinformation by comparing them with text information. For example, the detection unit can analyze images in posts and check if they match the text information. The detection unit can also analyze videos in posts and check if they match the text information. Furthermore, the detection unit can analyze the content of images and videos and evaluate the likelihood of them being misinformation. In this way, the detection unit can improve detection accuracy by analyzing the content of images and videos and evaluating the likelihood of them being misinformation by comparing them with text information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image and video data into a generating AI and have the generating AI perform the evaluation of the likelihood of them being misinformation.

[0040] The detection unit can prioritize the detection of region-specific misinformation by considering the geographical information of the posts. For example, the detection unit analyzes the geographical information of the posts and prioritizes the detection of misinformation related to a specific region. The detection unit can also consider geographical information in order to detect region-specific misinformation. Furthermore, the detection unit can analyze geographical information in order to prioritize the detection of region-specific misinformation. As a result, by considering geographical information, the detection unit can prioritize the detection of region-specific misinformation and provide accurate information specific to the region. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical information into a generating AI and have the generating AI perform the detection of region-specific misinformation.

[0041] The detection unit can detect misinformation in multiple languages, taking into account the language and cultural background of the posts. For example, the detection unit can analyze the language of the posts and detect misinformation in multiple languages. The detection unit can also detect misinformation in multiple languages, taking into account the cultural background of the posts. Furthermore, the detection unit can construct a system for detecting misinformation in multiple languages, taking into account language and cultural background. As a result, the detection unit can detect misinformation in multiple languages ​​by taking into account language and cultural background, thereby improving the reliability of global information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input language and cultural background data into a generating AI and have the generating AI perform multilingual misinformation detection.

[0042] The notification unit can adjust the urgency of notifications based on the impact of misinformation. For example, if the impact of misinformation is high, the notification unit will issue a high-urgency notification. If the impact of misinformation is moderate, the notification unit can issue a notification with normal urgency. Furthermore, if the impact of misinformation is low, the notification unit can issue a low-urgency notification. In this way, the notification unit can provide users with notifications of appropriate urgency by adjusting the urgency of notifications based on the impact of misinformation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input misinformation impact data into a generating AI and have the generating AI perform the adjustment of notification urgency.

[0043] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit can refer to the user's past notification history and select the optimal notification method. The notification unit can also prioritize selecting notification methods that the user has preferred to use in the past. Furthermore, the notification unit can analyze the user's past notification history and select the optimal notification method. As a result, the notification unit can select the optimal notification method by referring to the user's past notification history and provide the user with appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0044] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. It can also send an email notification if the user is using a tablet. Furthermore, if the user is using a desktop, it can send a browser notification. This allows the notification unit to select the optimal notification method and deliver appropriate notifications to the user by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0045] The notification unit can analyze a user's social media activity and notify them of relevant misinformation. For example, the notification unit can analyze a user's social media activity and notify them of relevant misinformation. The notification unit can also notify them of misinformation related to topics the user is interested in. Furthermore, the notification unit can notify them of relevant misinformation based on the user's social media activity. In this way, the notification unit can analyze a user's social media activity, notify them of relevant misinformation, and provide the user with appropriate information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute notifications of relevant misinformation.

[0046] The information provider can adjust the level of detail of the information it provides based on the impact of the misinformation. For example, if the impact of the misinformation is high, the provider will provide detailed information. If the impact of the misinformation is moderate, the provider can also provide information at a normal level of detail. Furthermore, if the impact of the misinformation is low, the provider can provide concise information. In this way, the provider can provide users with appropriate information by adjusting the level of detail of the information provided based on the impact of the misinformation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input misinformation impact data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0047] The information delivery unit can select the optimal information delivery method by referring to the user's past information delivery history. For example, the information delivery unit can refer to the user's past information delivery history and select the optimal information delivery method. The information delivery unit can also prioritize selecting information delivery methods that the user has preferred to use in the past. Furthermore, the information delivery unit can analyze the user's past information delivery history and select the optimal information delivery method. As a result, the information delivery unit can select the optimal information delivery method by referring to the user's past information delivery history and provide the user with appropriate information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past information delivery history data into a generating AI and have the generating AI perform the selection of the optimal information delivery method.

[0048] The service provider can provide accurate, region-specific information by taking into account the user's geographical information. For example, the service provider can analyze the user's geographical information and provide accurate, region-specific information. The service provider can also consider geographical information in order to provide region-specific information. Furthermore, the service provider can analyze geographical information and provide accurate, region-specific information. In this way, by considering geographical information, the service provider can provide accurate, region-specific information and provide appropriate information to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical information into a generating AI and have the generating AI perform the task of providing accurate, region-specific information.

[0049] The service provider can analyze a user's social media activity and provide relevant and accurate information. For example, the service provider can analyze a user's social media activity and provide relevant and accurate information. Furthermore, the service provider can provide relevant and accurate information related to topics of interest to the user. In addition, the service provider can provide relevant and accurate information based on the user's social media activity. Thus, by analyzing a user's social media activity, the service provider can provide relevant and accurate information and provide the user with appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant and accurate information.

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

[0051] The hoax detection system may also include a history analysis unit that analyzes the user's past behavior history. For example, the history analysis unit can analyze what kind of information the user has trusted in the past, thereby improving the accuracy of hoax detection. Furthermore, the history analysis unit can identify what kind of hoax the user has been influenced by in the past and prioritize the detection of similar hoaxes. In addition, the history analysis unit can optimize the hoax detection algorithm based on the user's past behavior history. As a result, the hoax detection system can detect hoaxes with greater accuracy by considering the user's past behavior history.

[0052] The hoax detection system may further include a geographic information analysis unit that prioritizes the detection of region-specific hoaxes by considering the user's geographical information. For example, the geographic information analysis unit prioritizes the detection of hoaxes related to a specific region and notifies users in that region. The geographic information analysis unit can also analyze geographical information to detect region-specific hoaxes. Furthermore, the geographic information analysis unit can optimize algorithms for prioritizing the detection of region-specific hoaxes. As a result, the hoax detection system can prioritize the detection of region-specific hoaxes by considering geographical information and provide accurate, region-specific information.

[0053] The misinformation detection system may further include a social media analysis unit that analyzes the user's social media activity and prioritizes the detection of relevant misinformation. For example, the social media analysis unit prioritizes the detection of misinformation related to topics the user is interested in. It can also identify relevant misinformation based on the user's social media activity. Furthermore, the social media analysis unit can analyze the user's social media activity and optimize the misinformation detection algorithm. As a result, the misinformation detection system can prioritize the detection of relevant misinformation by considering the user's social media activity and provide the user with appropriate information.

[0054] The hoax detection system may also include a device information analysis unit that selects the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the device information analysis unit can send a push notification. It can also send an email notification if the user is using a tablet. Furthermore, if the user is using a desktop computer, it can send a browser notification. This allows the hoax detection system to select the optimal notification method and deliver appropriate notifications to the user by considering the user's device information.

[0055] The misinformation detection system may also include an information provision history analysis unit that selects the optimal information provision method by referring to the user's past information provision history. For example, the information provision history analysis unit can refer to the user's past information provision history and select the optimal information provision method. Furthermore, the information provision history analysis unit can prioritize the selection of information provision methods that the user has preferred to use in the past. In addition, the information provision history analysis unit can analyze the user's past information provision history and select the optimal information provision method. As a result, the misinformation detection system can select the optimal information provision method by referring to the user's past information provision history and provide the user with appropriate information.

[0056] The misinformation detection system may further include a social media information provision unit that analyzes the user's social media activity and provides relevant and accurate information. The social media information provision unit can, for example, analyze the user's social media activity and provide relevant and accurate information. It can also provide accurate information related to topics the user is interested in. Furthermore, the social media information provision unit can provide relevant and accurate information based on the user's social media activity. This allows the misinformation detection system to provide relevant and accurate information by analyzing the user's social media activity, thereby providing the user with appropriate information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The detection unit monitors posts on social media in real time and detects potentially false information. The detection unit uses AI to analyze the content of posts and identify potentially false information. For example, when detecting false information about elections, it can verify the accuracy of quotes by comparing them with a database of past statements. It can also analyze the context of posts and identify keywords that are likely to be false. Furthermore, it can analyze the content of images and videos in posts and evaluate the likelihood of them being false by comparing them with text information. Step 2: The notification unit notifies the user of the false information detected by the detection unit. The user is notified immediately when false information is detected. The system can also estimate the user's emotions and adjust the way the notification is expressed based on those emotions. Furthermore, the urgency of the notification can be adjusted based on the impact of the false information. Step 3: The providing unit provides accurate information based on the misinformation notified by the notification unit. It presents accurate information to the user. It may also include a reliability evaluation unit that assesses the reliability of the accurate information. Furthermore, it may estimate the user's sentiment and adjust the method of providing accurate information based on the estimated user sentiment.

[0059] (Example of form 2) The hoax detection system according to an embodiment of the present invention is a system that monitors posts on social media in real time, immediately detects potentially false information, and notifies users to provide accurate information. The hoax detection system monitors posts on social media in real time and detects potentially false information. If false information is detected, it notifies the user and provides accurate information. This mechanism improves the reliability of information and prevents the spread of false information. The hoax detection system monitors posts on social media in real time. In this process, the AI ​​analyzes the content of the posts and identifies potentially false information. For example, when detecting false information about elections, the accuracy of the citation can be verified by comparing it with a database of past statements. This allows for the immediate detection of potentially false information. If false information is detected, it notifies the user and provides accurate information. For example, if false information is detected, the reliability of the information can be improved by immediately notifying the user and presenting accurate information. This prevents the spread of false information and prevents accurate information from being buried. This mechanism improves the reliability of information and prevents the spread of false information. For example, by detecting election-related misinformation in real time and providing users with accurate information, it can help voters make informed decisions. It can also address the challenge of discerning the truthfulness of information circulating on social media and provide a means to quickly obtain accurate information. Thus, using a misinformation checker AI improves the reliability of information on social media, prevents the spread of misinformation, and enables the rapid acquisition of accurate information. In this way, the misinformation detection system can improve the reliability of information by detecting misinformation on social media in real time and providing users with accurate information.

[0060] The misinformation detection system according to this embodiment comprises a detection unit, a notification unit, and a provision unit. The detection unit monitors posts on social media in real time and detects information that may be misinformation. The detection unit analyzes the content of posts using AI, for example, to identify information that may be misinformation. For example, when the detection unit detects misinformation about an election, it can verify the accuracy of the quote by comparing it with a database of past statements. The detection unit can also analyze the context of the post and identify keywords that are likely to be misinformation. Furthermore, the detection unit can analyze the content of images and videos in posts and evaluate the likelihood of them being misinformation by comparing them with text information. The notification unit notifies the user of the misinformation detected by the detection unit. The notification unit notifies the user immediately when misinformation is detected, for example. The notification unit can also estimate the user's emotions and adjust the way the notification is expressed based on the estimated emotions of the user. Furthermore, the notification unit can adjust the urgency of the notification based on the impact of the misinformation. The provision unit provides accurate information based on the misinformation notified by the notification unit. The information provider unit, for example, presents accurate information to the user. The information provider unit may also include a reliability evaluation unit that evaluates the reliability of the accurate information. Furthermore, the information provider unit can estimate the user's emotions and adjust the method of providing accurate information based on the estimated user emotions. As a result, the misinformation detection system according to this embodiment can improve the reliability of information by detecting misinformation on social media in real time and providing accurate information to the user.

[0061] The detection unit monitors posts on social media in real time and detects potentially false information. Specifically, it uses AI to analyze the content of posts and identify potentially false information. The AI ​​utilizes natural language processing technology to analyze the text of posts and detect contextual and keyword patterns. For example, when detecting false information about elections, it can verify the accuracy of quotations by comparing them with a database of past statements. The AI ​​searches the database of past statements for relevant statements and compares them with the content of the post to determine whether the quotation is accurate. It can also analyze the context of the post and identify keywords that are likely to be false. For example, if keywords such as "election fraud" or "conspiracy theory" are included, it is judged to be highly likely to be false. Furthermore, the detection unit can also analyze the content of images and videos in posts and evaluate the likelihood of them being false by comparing them with the text information. Using image recognition technology, it analyzes the content of posted images and videos and checks whether they match the text information. For example, if an image showing election fraud is posted, it checks whether the image is from a past election and evaluates the likelihood of it being false. As a result, the detection unit can detect misinformation with high accuracy through multifaceted analysis of text, images, and videos.

[0062] The notification unit notifies users of false information detected by the detection unit. Specifically, it immediately notifies users when false information is detected. Notifications are displayed as pop-up or push notifications on the SNS platform. The notification unit can also estimate the user's emotions and adjust the way the notification is presented based on the estimated emotions. For example, if the user is feeling angry or anxious, the notification will be worded more gently and display a message encouraging a calm response. Emotion estimation uses AI technology to analyze the user's past posts and reactions to estimate their emotional state. Furthermore, the notification unit can adjust the urgency of the notification based on the impact of the false information. For example, if the false information is widely disseminated or has a significant social impact, it will issue a high-urgency notification to encourage a quick response from the user. In this way, the notification unit can notify users of false information at the appropriate time and in the appropriate manner, supporting a quick and calm response.

[0063] The information provider unit provides accurate information based on the misinformation notified by the notification unit. Specifically, it presents accurate information to users. The information provider unit provides accurate information against misinformation based on data collected from reliable sources. For example, if misinformation regarding an election is detected, it presents users with accurate information provided by the election commission or other public institutions. The information provider unit may also include a reliability evaluation unit that assesses the reliability of accurate information. The reliability evaluation unit assesses the reliability of the information provided based on the reliability of the information source and past performance, and guarantees the quality of the information presented to users. Furthermore, the information provider unit can estimate the user's emotions and adjust the method of providing accurate information based on the estimated user emotions. For example, if a user is feeling anxious, it may provide information in a reassuring manner to encourage a calm response. In this way, the information provider unit can provide users with accurate and reliable information and minimize confusion caused by misinformation.

[0064] The detection unit can verify the accuracy of quotations by comparing them with a database of past statements. For example, the detection unit can use the database of past statements to verify the accuracy of quotations in posts. For example, the detection unit can evaluate the reliability of the source and verify the accuracy of the quotation. The detection unit can also evaluate the degree of agreement with the original text and verify the accuracy of the quotation. Furthermore, the detection unit can use the database of past statements to evaluate the reliability of the source. This allows the detection unit to verify the accuracy of quotations and improve the accuracy of detecting misinformation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the database of past statements into a generating AI and have the generating AI perform the verification of the accuracy of quotations.

[0065] The notification unit can immediately notify the user when false information is detected. For example, the notification unit will immediately notify the user when false information is detected. For example, the notification unit will notify within a few seconds when false information is detected. The notification unit can also notify within a few minutes when false information is detected. Furthermore, the notification unit can build a system to immediately notify when false information is detected. This allows the user to quickly obtain accurate information by immediately notifying when false information is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use an AI model to immediately notify when false information is detected.

[0066] The service provider can present accurate information to users. For example, the service provider can provide accurate information to users. For example, the service provider can provide information from reliable sources. The service provider can also provide verified information. Furthermore, the service provider can build a system for providing accurate information. This allows the service provider to improve the reliability of information by presenting accurate information to users. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model to provide accurate information.

[0067] The detection unit can detect election-related misinformation in real time. For example, the detection unit can detect election-related misinformation in real time. For example, the detection unit can detect false candidate information related to elections. The detection unit can also detect incorrect voting methods related to elections. Furthermore, the detection unit can build a system for detecting election-related misinformation in real time. As a result, the detection unit can provide accurate information related to elections by detecting election-related misinformation in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can use an AI model for detecting election-related misinformation in real time.

[0068] The information provider may include a reliability evaluation unit that evaluates the reliability of accurate information. For example, the information provider may evaluate the reliability of information sources. The information provider may also evaluate past performance. Furthermore, the information provider may build a system for evaluating the reliability of accurate information. This allows the information provider to improve the reliability of the information it provides by evaluating the reliability of accurate information. Some or all of the above-described processes in the information provider may be performed using AI, for example, or without AI. For example, the information provider may use an AI model for evaluating the reliability of accurate information.

[0069] The detection unit can estimate the user's emotions and adjust the accuracy of misinformation detection based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can increase the accuracy of misinformation detection and perform more rigorous detection. Furthermore, if the user is relaxed, the detection unit can maintain a normal level of detection accuracy and avoid excessive notifications. Additionally, if the user is agitated, the detection unit can adjust the detection accuracy to reduce false positives. This allows the detection unit to detect misinformation more appropriately by adjusting the accuracy of misinformation detection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The detection unit can analyze the context of a post and identify keywords that are likely to be misinformation. For example, the detection unit can analyze the context of a post and detect specific keywords related to elections. The detection unit can also analyze the context of a post and identify misinformation about specific people or organizations. Furthermore, the detection unit can analyze the context of a post and identify misinformation about specific events. In this way, the detection unit can improve the accuracy of misinformation detection by identifying keywords that are likely to be misinformation by analyzing the context of a post. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input contextual data of a post into a generating AI and have the generating AI identify keywords that are likely to be misinformation.

[0071] The detection unit can analyze the content of images and videos in posts and evaluate the likelihood of them being misinformation by comparing them with text information. For example, the detection unit can analyze images in posts and check if they match the text information. The detection unit can also analyze videos in posts and check if they match the text information. Furthermore, the detection unit can analyze the content of images and videos and evaluate the likelihood of them being misinformation. In this way, the detection unit can improve detection accuracy by analyzing the content of images and videos and evaluating the likelihood of them being misinformation by comparing them with text information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image and video data into a generating AI and have the generating AI perform the evaluation of the likelihood of them being misinformation.

[0072] The detection unit can estimate the user's emotions and determine the detection priority of misinformation based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit will increase the detection priority of misinformation. The detection unit can also maintain the detection priority at a normal level if the user is relaxed. Furthermore, the detection unit can adjust the detection priority if the user is agitated. In this way, the detection unit can prioritize the detection of more important misinformation by determining the detection priority of misinformation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The detection unit can prioritize the detection of region-specific misinformation by considering the geographical information of the posts. For example, the detection unit analyzes the geographical information of the posts and prioritizes the detection of misinformation related to a specific region. The detection unit can also consider geographical information in order to detect region-specific misinformation. Furthermore, the detection unit can analyze geographical information in order to prioritize the detection of region-specific misinformation. As a result, by considering geographical information, the detection unit can prioritize the detection of region-specific misinformation and provide accurate information specific to the region. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical information into a generating AI and have the generating AI perform the detection of region-specific misinformation.

[0074] The detection unit can detect misinformation in multiple languages, taking into account the language and cultural background of the posts. For example, the detection unit can analyze the language of the posts and detect misinformation in multiple languages. The detection unit can also detect misinformation in multiple languages, taking into account the cultural background of the posts. Furthermore, the detection unit can construct a system for detecting misinformation in multiple languages, taking into account language and cultural background. As a result, the detection unit can detect misinformation in multiple languages ​​by taking into account language and cultural background, thereby improving the reliability of global information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input language and cultural background data into a generating AI and have the generating AI perform multilingual misinformation detection.

[0075] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will use a calm expression. It can also use a normal expression if the user is relaxed. Furthermore, if the user is excited, the notification unit can use a concise expression. This allows the notification unit to provide more appropriate notifications by adjusting the way notifications are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The notification unit can adjust the urgency of notifications based on the impact of misinformation. For example, if the impact of misinformation is high, the notification unit will issue a high-urgency notification. If the impact of misinformation is moderate, the notification unit can issue a notification with normal urgency. Furthermore, if the impact of misinformation is low, the notification unit can issue a low-urgency notification. In this way, the notification unit can provide users with notifications of appropriate urgency by adjusting the urgency of notifications based on the impact of misinformation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input misinformation impact data into a generating AI and have the generating AI perform the adjustment of notification urgency.

[0077] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit can refer to the user's past notification history and select the optimal notification method. The notification unit can also prioritize selecting notification methods that the user has preferred to use in the past. Furthermore, the notification unit can analyze the user's past notification history and select the optimal notification method. As a result, the notification unit can select the optimal notification method by referring to the user's past notification history and provide the user with appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0078] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will send an immediate notification. It can also send a notification at a normal time if the user is relaxed. Furthermore, if the user is excited, the notification unit can send a notification at an appropriate time. This allows the notification unit to send notifications at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. It can also send an email notification if the user is using a tablet. Furthermore, if the user is using a desktop, it can send a browser notification. This allows the notification unit to select the optimal notification method and deliver appropriate notifications to the user by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0080] The notification unit can analyze a user's social media activity and notify them of relevant misinformation. For example, the notification unit can analyze a user's social media activity and notify them of relevant misinformation. The notification unit can also notify them of misinformation related to topics the user is interested in. Furthermore, the notification unit can notify them of relevant misinformation based on the user's social media activity. In this way, the notification unit can analyze a user's social media activity, notify them of relevant misinformation, and provide the user with appropriate information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute notifications of relevant misinformation.

[0081] The service provider can estimate the user's emotions and adjust the method of providing accurate information based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide detailed information. If the user is relaxed, the service provider can provide normal information. Furthermore, if the user is excited, the service provider can provide concise information. This allows the service provider to provide more appropriate information by adjusting the method of providing accurate information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The information provider can adjust the level of detail of the information it provides based on the impact of the misinformation. For example, if the impact of the misinformation is high, the provider will provide detailed information. If the impact of the misinformation is moderate, the provider can also provide information at a normal level of detail. Furthermore, if the impact of the misinformation is low, the provider can provide concise information. In this way, the provider can provide users with appropriate information by adjusting the level of detail of the information provided based on the impact of the misinformation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input misinformation impact data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0083] The information delivery unit can select the optimal information delivery method by referring to the user's past information delivery history. For example, the information delivery unit can refer to the user's past information delivery history and select the optimal information delivery method. The information delivery unit can also prioritize selecting information delivery methods that the user has preferred to use in the past. Furthermore, the information delivery unit can analyze the user's past information delivery history and select the optimal information delivery method. As a result, the information delivery unit can select the optimal information delivery method by referring to the user's past information delivery history and provide the user with appropriate information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past information delivery history data into a generating AI and have the generating AI perform the selection of the optimal information delivery method.

[0084] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize providing important information. If the user is relaxed, the service provider can also provide information with normal priority. Furthermore, if the user is excited, the service provider can provide information with appropriate priority. This allows the service provider to prioritize more important information by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The service provider can provide accurate, region-specific information by taking into account the user's geographical information. For example, the service provider can analyze the user's geographical information and provide accurate, region-specific information. The service provider can also consider geographical information in order to provide region-specific information. Furthermore, the service provider can analyze geographical information and provide accurate, region-specific information. In this way, by considering geographical information, the service provider can provide accurate, region-specific information and provide appropriate information to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical information into a generating AI and have the generating AI perform the task of providing accurate, region-specific information.

[0086] The service provider can analyze a user's social media activity and provide relevant and accurate information. For example, the service provider can analyze a user's social media activity and provide relevant and accurate information. Furthermore, the service provider can provide relevant and accurate information related to topics of interest to the user. In addition, the service provider can provide relevant and accurate information based on the user's social media activity. Thus, by analyzing a user's social media activity, the service provider can provide relevant and accurate information and provide the user with appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant and accurate information.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The hoax detection system may also include a history analysis unit that analyzes the user's past behavior history. For example, the history analysis unit can analyze what kind of information the user has trusted in the past, thereby improving the accuracy of hoax detection. Furthermore, the history analysis unit can identify what kind of hoax the user has been influenced by in the past and prioritize the detection of similar hoaxes. In addition, the history analysis unit can optimize the hoax detection algorithm based on the user's past behavior history. As a result, the hoax detection system can detect hoaxes with greater accuracy by considering the user's past behavior history.

[0089] The hoax detection system may further include an impact assessment unit that estimates the user's emotions and evaluates the impact of the hoax based on the estimated emotions. For example, if the user is feeling anxious, the impact assessment unit will rate the hoax higher and respond quickly. The impact assessment unit can also maintain the impact at a normal level if the user is relaxed. Furthermore, if the user is agitated, the impact assessment unit can adjust the impact and take an appropriate response. This allows the hoax detection system to evaluate the impact of hoax based on the user's emotions and take a more appropriate response.

[0090] The hoax detection system may further include a geographic information analysis unit that prioritizes the detection of region-specific hoaxes by considering the user's geographical information. For example, the geographic information analysis unit prioritizes the detection of hoaxes related to a specific region and notifies users in that region. The geographic information analysis unit can also analyze geographical information to detect region-specific hoaxes. Furthermore, the geographic information analysis unit can optimize algorithms for prioritizing the detection of region-specific hoaxes. As a result, the hoax detection system can prioritize the detection of region-specific hoaxes by considering geographical information and provide accurate, region-specific information.

[0091] The misinformation detection system may further include a social media analysis unit that analyzes the user's social media activity and prioritizes the detection of relevant misinformation. For example, the social media analysis unit prioritizes the detection of misinformation related to topics the user is interested in. It can also identify relevant misinformation based on the user's social media activity. Furthermore, the social media analysis unit can analyze the user's social media activity and optimize the misinformation detection algorithm. As a result, the misinformation detection system can prioritize the detection of relevant misinformation by considering the user's social media activity and provide the user with appropriate information.

[0092] The hoax detection system may further include a notification timing adjustment unit that estimates the user's emotions and adjusts the timing of notifications based on those emotions. For example, if the user is feeling anxious, the notification timing adjustment unit may immediately send a notification. It can also send a notification at a normal time if the user is relaxed. Furthermore, if the user is excited, the notification timing adjustment unit can send a notification at an appropriate time. In this way, the hoax detection system can send notifications at a more appropriate time by adjusting the timing of notifications based on the user's emotions.

[0093] The hoax detection system may also include a device information analysis unit that selects the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the device information analysis unit can send a push notification. It can also send an email notification if the user is using a tablet. Furthermore, if the user is using a desktop computer, it can send a browser notification. This allows the hoax detection system to select the optimal notification method and deliver appropriate notifications to the user by considering the user's device information.

[0094] The misinformation detection system may further include an information prioritization unit that estimates the user's emotions and determines the priority of information to be provided based on those emotions. For example, if the user is feeling anxious, the information prioritization unit will provide important information first. If the user is relaxed, the information prioritization unit can also provide information with normal priority. Furthermore, if the user is agitated, the information prioritization unit can provide information with appropriate priority. In this way, the misinformation detection system can prioritize providing more important information by determining the priority of information to be provided based on the user's emotions.

[0095] The misinformation detection system may also include an information provision history analysis unit that selects the optimal information provision method by referring to the user's past information provision history. For example, the information provision history analysis unit can refer to the user's past information provision history and select the optimal information provision method. Furthermore, the information provision history analysis unit can prioritize the selection of information provision methods that the user has preferred to use in the past. In addition, the information provision history analysis unit can analyze the user's past information provision history and select the optimal information provision method. As a result, the misinformation detection system can select the optimal information provision method by referring to the user's past information provision history and provide the user with appropriate information.

[0096] The misinformation detection system may also include an information delivery adjustment unit that estimates the user's emotions and adjusts the method of providing accurate information based on those emotions. For example, if the user is feeling anxious, the information delivery adjustment unit may provide detailed information. It may also provide normal information if the user is relaxed. Furthermore, if the user is agitated, the information delivery adjustment unit may provide concise information. In this way, the misinformation detection system can provide more appropriate information by adjusting the method of providing accurate information based on the user's emotions.

[0097] The misinformation detection system may further include a social media information provision unit that analyzes the user's social media activity and provides relevant and accurate information. The social media information provision unit can, for example, analyze the user's social media activity and provide relevant and accurate information. It can also provide accurate information related to topics the user is interested in. Furthermore, the social media information provision unit can provide relevant and accurate information based on the user's social media activity. This allows the misinformation detection system to provide relevant and accurate information by analyzing the user's social media activity, thereby providing the user with appropriate information.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The detection unit monitors posts on social media in real time and detects potentially false information. The detection unit uses AI to analyze the content of posts and identify potentially false information. For example, when detecting false information about elections, it can verify the accuracy of quotes by comparing them with a database of past statements. It can also analyze the context of posts and identify keywords that are likely to be false. Furthermore, it can analyze the content of images and videos in posts and evaluate the likelihood of them being false by comparing them with text information. Step 2: The notification unit notifies the user of the false information detected by the detection unit. The user is notified immediately when false information is detected. The system can also estimate the user's emotions and adjust the way the notification is expressed based on those emotions. Furthermore, the urgency of the notification can be adjusted based on the impact of the false information. Step 3: The providing unit provides accurate information based on the misinformation notified by the notification unit. It presents accurate information to the user. It may also include a reliability evaluation unit that assesses the reliability of the accurate information. Furthermore, it may estimate the user's sentiment and adjust the method of providing accurate information based on the estimated user sentiment.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the detection unit, notification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit monitors posts on social media using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A detects information that may be false. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12 and notifies the user of the detected false information. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12 and provides accurate information to the user. The detection unit may be implemented in the identification processing unit 290 of the data processing unit 12, for example, and the notification unit and provision unit may be implemented in the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the detection unit, notification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit monitors posts on social media using the camera 42 and microphone 238 of the smart glasses 214 and detects potentially false information using the control unit 46A. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12 and notifies the user of the detected false information. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12 and provides accurate information to the user. The detection unit may be implemented in the identification processing unit 290 of the data processing unit 12, for example, and the notification unit and provision unit may be implemented in the control unit 46A of the smart glasses 214, for example. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the detection unit, notification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit monitors posts on social media using the camera 42 and microphone 238 of the headset terminal 314 and detects potentially false information using the control unit 46A. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and notifies the user of the detected false information. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides accurate information to the user. The detection unit may also be implemented, for example, by the identification processing unit 290 of the data processing unit 12, and the notification unit and provision unit may be implemented, for example, by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the detection unit, notification unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the detection unit monitors posts on social media using the camera 42 and microphone 238 of the robot 414, and the control unit 46A detects information that may be false. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and notifies the user of the detected false information. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides accurate information to the user. The detection unit may be implemented, for example, by the identification processing unit 290 of the data processing unit 12, and the notification unit and provision unit may be implemented, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

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

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A detection unit that monitors posts on social media in real time and detects information that may be false, A notification unit that notifies the user of the false information detected by the detection unit, The system includes a providing unit that provides accurate information based on the false information notified by the notification unit. A system characterized by the following features. (Note 2) The detection unit is The accuracy of the citation will be verified by comparing it with a database of past statements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Users will be immediately notified if false information is detected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, To provide users with accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is Detect election-related misinformation in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It includes a reliability evaluation unit that assesses the reliability of accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is It estimates the user's sentiment and adjusts the accuracy of misinformation detection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is Analyze the context of the post and identify keywords that are likely to be misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is We analyze the content of images and videos in posts and compare them with text information to assess the likelihood of them being misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is The system estimates user sentiment and determines the priority for detecting misinformation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is Prioritize detecting region-specific misinformation by considering the geographical information of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is Detect misinformation in multiple languages, taking into account the language and cultural background of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, The urgency of notifications is adjusted based on the impact of the misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, The system selects the most suitable notification method by referring to the user's past notification history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, The optimal notification method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, Analyze users' social media activity and notify them of relevant misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We adjust the level of detail in the information we provide based on the impact of the misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The optimal method of providing information is selected by referring to the user's past information provision history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Providing accurate, region-specific information that takes the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Analyze users' social media activity and provide relevant and accurate information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that monitors posts on social media in real time and detects information that may be false, A notification unit that notifies the user of the false information detected by the detection unit, The system includes a providing unit that provides accurate information based on the false information notified by the notification unit. A system characterized by the following features.

2. The detection unit is The accuracy of the citation will be verified by comparing it with a database of past statements. The system according to feature 1.

3. The aforementioned notification unit, Users will be immediately notified if false information is detected. The system according to feature 1.

4. The aforementioned supply unit is, To provide users with accurate information. The system according to feature 1.

5. The detection unit is Detect election-related misinformation in real time. The system according to feature 1.

6. The aforementioned supply unit is, It includes a reliability evaluation unit that assesses the reliability of accurate information. The system according to feature 1.

7. The detection unit is It estimates the user's sentiment and adjusts the accuracy of misinformation detection based on the estimated user sentiment. The system according to feature 1.

8. The detection unit is Analyze the context of the post and identify keywords that are likely to be misinformation. The system according to feature 1.

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