system

Abstract

The system according to the embodiment aims to evaluate the reliability of online news and social media posts in real time and prevent the spread of false information. [Solution] A system according to an embodiment includes an evaluation unit, a prevention unit, a detection unit, a protection unit, a detection unit, and a notification unit. The evaluation unit evaluates the reliability of online news or social media posts in real time. The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The detection unit analyzes videos and images in real time to detect signs of editing. The protection unit protects against fraud and personal reputation based on the signs of editing detected by the detection unit. The detection unit detects unauthorized online data collection and tracking. The notification unit notifies users in real time of unauthorized data collection and tracking detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately evaluate the reliability of online news and social media posts in real time and prevent the spread of false information, so there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the reliability of online news and social media posts in real time and prevent the spread of false information. [Means for solving the problem]

[0006] The system according to the embodiment includes an evaluation unit, a prevention unit, a detection unit, a protection unit, a detection unit, and a notification unit. The evaluation unit evaluates the reliability of online news or social media posts in real time. The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The detection unit analyzes videos and images in real time to detect signs of editing. The protection unit protects against fraud and personal reputation based on the signs of editing detected by the detection unit. The detection unit detects unauthorized online data collection and tracking. The notification unit notifies users in real time of unauthorized data collection and tracking detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the reliability of online news and social media posts in real time and prevent the spread of false information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information accuracy assurance system according to an embodiment of the present invention evaluates the reliability of online news and social media posts in real time, prevents the spread of false information, analyzes videos and images in real time to detect traces of editing, and detects and notifies users of unauthorized online data collection and tracking. The information accuracy assurance system evaluates the reliability of online news and social media posts in real time and prevents the spread of false information. The information accuracy assurance system also analyzes videos and images in real time to detect traces of editing. Furthermore, the information accuracy assurance system detects unauthorized online data collection and tracking and notifies users in real time. For example, the information accuracy assurance system uses AI to analyze posted content and detect unreliable information. For example, the AI ​​verifies the source and citation of the posted content to evaluate its reliability. Furthermore, the AI ​​also considers past posting history and the reliability of the poster to achieve more accurate evaluations. Next, the information accuracy assurance system uses AI to analyze each frame of video and detect unnatural editing. For example, the AI ​​detects inconsistencies in video metadata and pixels to identify traces of editing. Furthermore, the AI ​​compares the content with past video data to achieve more accurate detection. Furthermore, the information accuracy assurance system uses AI to analyze website tracking codes and detect fraudulent data collection. For example, AI analyzes website scripts and cookies to identify fraudulent data collection. It also notifies users in real time, enabling a prompt response. This allows the information accuracy assurance system to realize a highly reliable information environment. This allows the information accuracy assurance system to realize a highly reliable information environment. For example, it can prevent the spread of false information and deter social unrest. It can also protect fraud and individual reputations. It also protects users' privacy.

[0029] An information accuracy assurance system according to an embodiment includes an evaluation unit, a prevention unit, a detection unit, a protection unit, a detection unit, and a notification unit. The evaluation unit evaluates the reliability of online news and social media posts in real time. The evaluation unit, for example, uses AI to verify the source and citation of the posted content and evaluate the reliability. The evaluation unit can also take into account past posting history and the reliability of the poster when making the evaluation. For example, AI cross-checks the source of the posted content and prioritizes posts with highly reliable sources. AI can also evaluate the reliability of citation sources and rate posts with less reliable sources less highly reliable. AI can also consider the update frequency and past reliability of the source and reflect this in the evaluation. The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The prevention unit, for example, automatically filters unreliable information using AI. The prevention unit can also display a warning message to prevent the spread of false information. The prevention unit can also notify users not to spread unreliable information. The detection unit analyzes videos and images in real time to detect traces of editing. The detection unit, for example, uses AI to analyze each frame of video and detect unnatural edits. The detection unit can also detect inconsistencies in video metadata and pixels. For example, AI can analyze video metadata and detect inconsistencies. AI can also analyze video pixels and detect inconsistencies. AI can also comprehensively analyze video metadata and pixel inconsistencies to identify traces of editing. The protection unit protects fraud and personal reputation based on the traces of editing detected by the detection unit. The protection unit, for example, uses AI to take measures to prevent fraud. The protection unit can also take measures to protect personal reputation. The protection unit can also display warning messages to prevent fraud and protect personal reputation. The detection unit detects unauthorized online data collection and tracking. For example, the detection unit uses AI to analyze website tracking codes to detect unauthorized data collection. The detection unit can also analyze website scripts and cookies to identify unauthorized data collection.For example, AI may analyze website scripts to identify unauthorized data collection. AI may also analyze website cookies to identify unauthorized data collection. AI may also comprehensively analyze website scripts and cookies to identify unauthorized data collection. The notification unit notifies the user of unauthorized data collection or tracking detected by the detection unit in real time. For example, if the notification unit detects unauthorized data collection using AI, it notifies the user in real time. The notification unit may also notify the user in real time if it detects unauthorized tracking. The notification unit may also send a warning message to the user in real time if it detects unauthorized data collection or tracking. As a result, the information accuracy assurance system according to the embodiment can realize a highly reliable information environment.

[0030] The evaluation unit can check the source and citation of the posted content and evaluate its reliability. The evaluation unit can, for example, use AI to cross-check the source of the posted content and prioritize evaluation of posts with highly reliable sources. The evaluation unit can also use AI to evaluate the reliability of citation sources and give a low rating to posts with less reliable sources. The evaluation unit can also use AI to consider the update frequency and past reliability of the source and reflect this in the evaluation. This can increase the reliability of the posted content. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the source and citation of the posted content into AI and have the AI ​​perform an evaluation of reliability.

[0031] The evaluation unit can make an evaluation based on the poster's past posting history or the poster's reliability. The evaluation unit, for example, uses AI to analyze the poster's past posting history and prioritizes evaluation of information from more reliable posters. The evaluation unit can also use AI to consider the poster's past reliability score and reflect it in the evaluation. The evaluation unit can also use AI to check whether the content matches the poster's past postings and reflect it in the evaluation. This makes it possible to make an evaluation that takes the poster's reliability into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the poster's past posting history into AI and have the AI ​​perform a reliability evaluation.

[0032] The evaluation unit can analyze the context of the posted content and reflect it in the evaluation of reliability. The evaluation unit can, for example, use AI to analyze the context of the posted content and prioritize evaluation of posts with highly reliable context. The evaluation unit can also use AI to check the degree of agreement between the context of the posted content and the source and reflect this in the evaluation. The evaluation unit can also use AI to compare the context of the posted content with past reliability scores and reflect this in the evaluation. This enables evaluation that takes the context of the posted content into consideration. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the context of the posted content into AI and have the AI ​​perform the reliability evaluation.

[0033] The prevention unit can prevent the spread of false information based on the information evaluated by the evaluation unit. The prevention unit, for example, uses AI to automatically filter unreliable information based on the evaluation results of the evaluation unit. The prevention unit can also use AI to display a warning message to prevent the spread of false information. The prevention unit can also use AI to notify users not to spread unreliable information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed, for example, using AI or without AI. For example, the prevention unit can input the evaluation results of the evaluation unit into AI and have the AI ​​prevent the spread of false information.

[0034] The detection unit can analyze each frame of the video and detect unnatural edited parts. The detection unit can, for example, use AI to analyze each frame of the video and detect unnatural edited parts. The detection unit can also use AI to analyze each frame of the video and identify traces of editing. The detection unit can also use AI to analyze each frame of the video and detect editing inconsistencies. This makes it possible to detect traces of editing in the video with high accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input data for each frame of the video into AI and have the AI ​​detect the edited parts.

[0035] The detection unit can detect inconsistencies in video metadata and pixels. For example, the detection unit can use AI to analyze video metadata and detect inconsistencies. The detection unit can also use AI to analyze video pixels and detect inconsistencies. The detection unit can also use AI to comprehensively analyze inconsistencies between video metadata and pixels and identify traces of editing. This makes it possible to detect traces of editing in video with high accuracy. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input video metadata and pixel data into AI and have the AI ​​detect edited areas.

[0036] The detection unit can analyze the tracking code of a website and detect fraudulent data collection. The detection unit can, for example, use AI to analyze the tracking code of a website and detect fraudulent data collection. The detection unit can also use AI to analyze the tracking code of a website and detect inconsistencies in data collection. The detection unit can also use AI to analyze the tracking code of a website and identify traces of fraudulent data collection. This allows for highly accurate detection of fraudulent data collection. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input the tracking code of a website into AI and have the AI ​​detect fraudulent data collection.

[0037] The detection unit can analyze website scripts and cookies to identify unauthorized data collection. The detection unit can, for example, use AI to analyze website scripts and identify unauthorized data collection. The detection unit can also use AI to analyze website cookies and identify unauthorized data collection. The detection unit can also use AI to comprehensively analyze website scripts and cookies to identify unauthorized data collection. This makes it possible to identify unauthorized data collection with high accuracy. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input website script and cookie data into AI and have the AI ​​identify unauthorized data collection.

[0038] The notification unit can notify the user of unauthorized data collection or tracking detected by the detection unit in real time. For example, if the notification unit detects unauthorized data collection using AI, it notifies the user in real time. Furthermore, if the notification unit detects unauthorized tracking using AI, it can also notify the user in real time. Furthermore, if the notification unit detects unauthorized data collection or tracking using AI, it can send a warning message to the user in real time. This makes it possible to notify the user of unauthorized data collection or tracking in real time. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input information about unauthorized data collection or tracking detected by the detection unit into AI and have the AI ​​execute real-time notification.

[0039] The evaluation unit can customize the evaluation criteria based on the language and region of the posted content. For example, the evaluation unit can use AI to analyze the language of the posted content and apply evaluation criteria appropriate for that language. The evaluation unit can also use AI to analyze regional information of the posted content and apply evaluation criteria appropriate for that region. The evaluation unit can also use AI to set optimal evaluation criteria based on a combination of language and region. This makes it possible to provide appropriate evaluation criteria according to the language and region. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the language and regional information of the posted content into AI and have the AI ​​customize the evaluation criteria.

[0040] The evaluation unit can change the evaluation algorithm depending on the category of the posted content. For example, the evaluation unit can use AI to analyze the category of the posted content and apply a strict evaluation algorithm to the news category. The evaluation unit can also use AI to apply a flexible evaluation algorithm to posts in the entertainment category. The evaluation unit can also use AI to set different evaluation criteria for each category and perform optimal evaluation. This makes it possible to provide an appropriate evaluation algorithm according to the category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input category information of the posted content into AI and have the AI ​​change the evaluation algorithm.

[0041] The evaluation unit can analyze images and videos of the posted content and evaluate them in combination with text information. The evaluation unit can, for example, use AI to analyze images of the posted content and check whether they match the text information. The evaluation unit can also use AI to analyze videos of the posted content and check whether they match the text information. The evaluation unit can also use AI to combine the results of image and video analysis with text information to perform a comprehensive evaluation. This enables a comprehensive evaluation that includes images and videos. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, or can be performed without using AI. For example, the evaluation unit can input image and video data of the posted content into AI and have the AI ​​perform an evaluation in combination with the text information.

[0042] The prevention unit can automatically filter related posts to prevent the spread of false information. The prevention unit can, for example, use AI to automatically detect and filter posts related to false information. The prevention unit can also, for example, use AI to automatically delete related posts to prevent the spread of false information. The prevention unit can also, for example, use AI to automatically hide posts related to false information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit can be performed, for example, using AI or without using AI. For example, the prevention unit can input post data related to false information into AI and have the AI ​​perform the filtering.

[0043] The prevention unit can send a warning message to the user to prevent the spread of false information. For example, if the prevention unit detects the spread of false information using AI, it sends the warning message to the user. The prevention unit can also display a warning message to the user to prevent the spread of false information using AI. The prevention unit can also send a warning message to the user by email to prevent the spread of false information using AI. This makes it possible to provide a warning message to prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, or may be performed without using AI. For example, the prevention unit can input data on the spread of false information into AI and cause the AI ​​to send a warning message.

[0044] The prevention unit can cooperate with social media platforms to prevent the spread of false information. For example, the prevention unit can cooperate with social media platforms using AI to perform filtering to prevent the spread of false information. The prevention unit can also cooperate with social media platforms using AI to display warning messages to prevent the spread of false information. The prevention unit can also cooperate with social media platforms using AI to delete posts to prevent the spread of false information. This makes it possible to prevent the spread of false information in cooperation with social media platforms. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input data from social media platforms into AI and have the AI ​​prevent the spread of false information.

[0045] The prevention unit can automatically display relevant news articles to prevent the spread of false information. For example, the prevention unit can automatically display relevant, reliable news articles to prevent the spread of false information using AI. The prevention unit can also notify users of relevant news articles to prevent the spread of false information using AI. The prevention unit can also display relevant news articles on social media platforms to prevent the spread of false information using AI. In this way, the spread of false information can be prevented by automatically displaying relevant news articles. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input data of relevant news articles into AI and have the AI ​​perform the display.

[0046] The prevention unit can improve the prevention measures by reflecting user feedback in order to prevent the spread of false information. The prevention unit can, for example, use AI to collect user feedback and reflect it in improving the prevention measures. The prevention unit can also use AI to adjust the measures to prevent the spread of false information based on user feedback. The prevention unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the prevention measures. In this way, the prevention measures can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the prevention unit can be performed, for example, using AI or without using AI. For example, the prevention unit can input user feedback data into AI and have the AI ​​improve the prevention measures.

[0047] The detection unit can analyze the content of the video or image and identify traces of editing by comparing it with past data. The detection unit can, for example, use AI to analyze the content of the video or image and identify traces of editing by comparing it with past data. The detection unit can also use AI to analyze the content of the video or image and detect parts that do not match past data. The detection unit can also use AI to analyze the content of the video or image and identify unnatural edited parts by comparing it with past data. This makes it possible to identify traces of editing by comparing it with past data. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input video or image data into AI and have the AI ​​compare it with past data.

[0048] The detection unit can change the detection algorithm depending on the video or image format. For example, the detection unit can use AI to apply the optimal detection algorithm depending on the video format (MP4, AVI, etc.). The detection unit can also use AI to apply the optimal detection algorithm depending on the image format (JPEG, PNG, etc.). The detection unit can also use AI to select the optimal detection algorithm based on the video or image format. This makes it possible to provide an appropriate detection algorithm depending on the format. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video or image format information to AI and have the AI ​​change the detection algorithm.

[0049] The detection unit can improve detection accuracy based on the location and time of video or image capture. The detection unit can, for example, use AI to analyze the location of video or image capture and improve detection accuracy. The detection unit can also use AI to analyze the time of video or image capture and improve detection accuracy. The detection unit can also use AI to comprehensively analyze the location and time of video or image capture and improve detection accuracy. This makes it possible to improve detection accuracy based on the location and time of capture. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input data on the location and time of video or image capture into AI and have the AI ​​improve detection accuracy.

[0050] The detection unit can analyze audio data of video or images and perform detection by combining it with the video content. The detection unit can, for example, use AI to analyze the audio data of the video and check whether it matches the video content. The detection unit can also use AI to analyze the audio data of the image and check whether it matches the video content. The detection unit can also use AI to analyze the audio data of the video or images and perform detection by combining it with the video content. This makes it possible to analyze audio data and combine it with the video content. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the audio data of the video or image into AI and have the AI ​​perform detection by combining it with the video content.

[0051] The protection unit can protect fraudulent acts and personal reputation based on the traces of editing detected by the detection unit. The protection unit can, for example, use AI to take measures to prevent fraudulent acts based on the detection results of the detection unit. The protection unit can also use AI to take measures to protect personal reputation. The protection unit can also use AI to display a warning message to protect fraudulent acts and personal reputation based on the detection results of the detection unit. This makes it possible to protect fraudulent acts and personal reputation based on the detected traces of editing. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without AI. For example, the protection unit can input the detection results of the detection unit into AI and have the AI ​​perform protection of fraudulent acts and personal reputation.

[0052] The protection unit can automatically collect relevant evidence to protect fraudulent acts and personal reputation. For example, the protection unit can use AI to automatically collect evidence of fraudulent acts and take protective measures. The protection unit can also use AI to automatically collect evidence to protect personal reputation and take measures. The protection unit can also use AI to automatically collect evidence to protect fraudulent acts and personal reputation and display a warning message. This makes it possible to automatically collect relevant evidence to protect fraudulent acts and personal reputation. Some or all of the above-mentioned processing in the protection unit can be performed, for example, using AI or without AI. For example, the protection unit can input evidential data related to fraudulent acts and personal reputation into AI and have the AI ​​collect evidence.

[0053] The protection unit can send a warning message to the user to protect against fraudulent acts or personal reputation. For example, if the protection unit detects fraudulent acts using AI, it sends a warning message to the user. The protection unit can also display a warning message to the user to protect against personal reputation using AI. The protection unit can also send a warning message to the user by email to protect against fraudulent acts or personal reputation using AI. In this way, a warning message to protect against fraudulent acts or personal reputation can be provided. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input data related to fraudulent acts or personal reputation into AI and have the AI ​​send a warning message.

[0054] The protection unit can cooperate with legal authorities to protect against fraudulent acts and personal honor. For example, if the protection unit detects fraudulent acts using AI, it can cooperate with legal authorities to take measures. The protection unit can also cooperate with legal authorities to take measures to protect personal honor using AI. The protection unit can also cooperate with legal authorities to display a warning message to protect against fraudulent acts and personal honor using AI. This makes it possible to protect against fraud and personal honor in cooperation with legal authorities. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or can be performed without using AI. For example, the protection unit can input data related to fraudulent acts and personal honor into AI and have the AI ​​cooperate with legal authorities.

[0055] The protection unit may automatically display relevant news articles to prevent fraud and protect individual reputation. For example, the protection unit may use AI to automatically display relevant, reliable news articles to prevent fraud. The protection unit may also use AI to notify users of relevant news articles to protect individual reputation. The protection unit may also use AI to display relevant news articles on social media platforms to protect fraud and individual reputation. This makes it possible to protect against fraud and individual reputation by automatically displaying relevant news articles. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit may input data of relevant news articles into AI and have the AI ​​perform the display.

[0056] The protection unit can improve protection measures by reflecting user feedback to protect against fraudulent activities and personal reputation. For example, the protection unit can use AI to collect user feedback and reflect it in improving the protection measures. The protection unit can also use AI to adjust measures to protect against fraudulent activities and personal reputation based on user feedback. The protection unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the protection measures. In this way, the protection measures can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without using AI. For example, the protection unit can input user feedback data into AI and have the AI ​​improve the protection measures.

[0057] The detection unit can integrate multiple data sources to detect fraudulent online data collection and tracking. The detection unit can, for example, use AI to integrate the multiple data sources and detect fraudulent data collection. The detection unit can also use AI to integrate the multiple data sources and detect tracking discrepancies. The detection unit can also use AI to integrate the multiple data sources and identify traces of fraudulent data collection and tracking. This makes it possible to integrate the multiple data sources and detect fraudulent data collection and tracking. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input data from multiple data sources into AI and have the AI ​​perform integrated analysis.

[0058] The detection unit can change the detection algorithm depending on the type and category of the website. For example, the detection unit can use AI to apply an optimal detection algorithm depending on the type of website (news site, shopping site, etc.). The detection unit can also use AI to apply an optimal detection algorithm depending on the category of the website (entertainment, education, etc.). The detection unit can also use AI to select an optimal detection algorithm based on the type and category of the website. This makes it possible to provide an appropriate detection algorithm depending on the type and category. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without using AI. For example, the detection unit can input website type and category information into AI and have the AI ​​change the detection algorithm.

[0059] The detection unit can analyze website access history and compare it with past data to identify unauthorized data collection. The detection unit can, for example, use AI to analyze website access history and compare it with past data to identify unauthorized data collection. The detection unit can also use AI to analyze website access history and detect abnormal patterns. The detection unit can also use AI to analyze website access history and identify parts that do not match past data. This makes it possible to identify unauthorized data collection by comparing it with past data. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input website access history data into AI and have the AI ​​compare it with past data.

[0060] The detection unit can analyze the website user interface and identify fraudulent data collection methods. The detection unit can, for example, use AI to analyze the website user interface and identify fraudulent data collection methods. The detection unit can also use AI to analyze the website user interface and detect inconsistencies in data collection. The detection unit can also use AI to analyze the website user interface and identify traces of fraudulent data collection. This makes it possible to analyze the user interface and identify fraudulent data collection methods. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input website user interface data into AI and have the AI ​​identify fraudulent data collection methods.

[0061] The notification unit can customize the notification content based on the user's past behavioral history. The notification unit can, for example, use AI to analyze the user's past behavioral history and provide optimal notification content. The notification unit can also use AI to provide relevant notification content based on the user's past behavioral history. The notification unit can also use AI to provide customized notification content by referring to the user's past behavioral history. This makes it possible to provide customized notification content based on the user's past behavioral history. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's past behavioral history data into AI and have the AI ​​customize the notification content.

[0062] The notification unit can optimize the notification content based on the user's device information. The notification unit can, for example, use AI to analyze the user's device information and provide optimal notification content. The notification unit can also use AI to optimize the notification content based on the user's device information. The notification unit can also use AI to provide optimal notification content by referring to the user's device information. This makes it possible to provide optimized notification content based on the user's device information. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​optimize the notification content.

[0063] The notification unit can make the notification content multilingual according to the user's language setting. The notification unit can, for example, use AI to analyze the user's language setting and provide the notification content in the most appropriate language. The notification unit can also use AI to make the notification content multilingual based on the user's language setting. The notification unit can also use AI to provide the notification content in the most appropriate language, taking the user's language setting into consideration. This makes it possible to provide multilingual notification content according to the user's language setting. Some or all of the above-described processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the user's language setting data into AI and have the AI ​​perform multilingual notification content.

[0064] The notification unit can adjust the notification content based on the user's current activity status. The notification unit can, for example, use AI to analyze the user's current activity status and provide optimal notification content. The notification unit can also use AI to adjust the notification content based on the user's current activity status. The notification unit can also use AI to provide optimal notification content by referring to the user's current activity status. This makes it possible to provide appropriate notification content based on the user's current activity status. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the user's current activity status data into AI and have the AI ​​adjust the notification content.

[0065] The notification unit can improve the notification content by reflecting user feedback. The notification unit can, for example, use AI to collect user feedback and reflect it in improving the notification content. The notification unit can also use AI to adjust the notification content based on user feedback. The notification unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the notification content. This makes it possible to improve the notification content by reflecting user feedback. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can input user feedback data into AI and have the AI ​​improve the notification content.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The evaluation unit can customize the evaluation criteria based on the language and region of the posted content. For example, the evaluation unit can use AI to analyze the language of the posted content and apply evaluation criteria appropriate for that language. The evaluation unit can also use AI to analyze regional information of the posted content and apply evaluation criteria appropriate for that region. The evaluation unit can also use AI to set optimal evaluation criteria based on a combination of language and region. This makes it possible to provide appropriate evaluation criteria according to the language and region. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the language and regional information of the posted content into AI and have the AI ​​customize the evaluation criteria.

[0068] The evaluation unit can change the evaluation algorithm depending on the category of the posted content. For example, the evaluation unit can use AI to analyze the category of the posted content and apply a strict evaluation algorithm to the news category. The evaluation unit can also use AI to apply a flexible evaluation algorithm to posts in the entertainment category. The evaluation unit can also use AI to set different evaluation criteria for each category and perform optimal evaluation. This makes it possible to provide an appropriate evaluation algorithm according to the category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input category information of the posted content into AI and have the AI ​​change the evaluation algorithm.

[0069] The evaluation unit can analyze images and videos of the posted content and perform an evaluation by combining them with text information. For example, the evaluation unit can use AI to analyze images of the posted content and check whether they match the text information. The evaluation unit can also use AI to analyze videos of the posted content and check whether they match the text information. The evaluation unit can also perform a comprehensive evaluation by combining the results of image and video analysis using AI with the text information. This enables a comprehensive evaluation that includes images and videos. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input image and video data of the posted content into AI and have the AI ​​perform an evaluation in combination with the text information.

[0070] The prevention unit can automatically filter related posts to prevent the spread of false information. For example, the prevention unit can use AI to automatically detect and filter posts related to false information. The prevention unit can also use AI to automatically delete related posts to prevent the spread of false information. The prevention unit can also use AI to automatically hide posts related to false information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input post data related to false information into AI and have the AI ​​perform the filtering.

[0071] The prevention unit can send a warning message to the user to prevent the spread of false information. For example, if the spread of false information is detected using AI, the prevention unit sends a warning message to the user. The prevention unit can also display a warning message to the user to prevent the spread of false information using AI. The prevention unit can also send a warning message to the user by email to prevent the spread of false information using AI. This makes it possible to provide a warning message to prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, for example, or may be performed without using AI. For example, the prevention unit can input data on the spread of false information into AI and cause the AI ​​to send a warning message.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The evaluation department evaluates the reliability of online news and social media posts in real time. The evaluation department uses AI to check the source and citation of the posted content and evaluate its reliability. It can also take into account past posting history and the reliability of the poster when making its evaluation. For example, AI can cross-check the source of the posted content and prioritize posts with highly reliable sources. AI can also evaluate the reliability of citations and give a lower rating to posts with less reliable sources. Furthermore, AI can take into account the update frequency and past reliability of the source and reflect this in its evaluation. Step 2: The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The prevention unit uses AI to automatically filter out unreliable information. It can also display warning messages to prevent the spread of false information. It can also notify users not to spread unreliable information. Step 3: The detection unit analyzes the video and images in real time to detect signs of editing. The detection unit uses AI to analyze each frame of the video to detect unnatural edits. It can also detect inconsistencies in video metadata and pixels. For example, AI can analyze video metadata to detect inconsistencies. AI can also analyze video pixels to detect inconsistencies. Furthermore, AI can comprehensively analyze video metadata and pixel inconsistencies to identify signs of editing. Step 4: The protection unit protects against fraud and personal reputation based on the edit traces detected by the detection unit. The protection unit uses AI to take measures to prevent fraud and can also take measures to protect personal reputation. In addition, it can display warning messages to prevent fraud and protect personal reputation. Step 5: The detection unit detects unauthorized online data collection and tracking. The detection unit uses AI to analyze website tracking codes to detect unauthorized data collection. It can also analyze website scripts and cookies to identify unauthorized data collection. For example, AI can analyze website scripts to identify unauthorized data collection. AI can also analyze website cookies to identify unauthorized data collection. AI can also comprehensively analyze website scripts and cookies to identify unauthorized data collection. Step 6: The notification unit notifies the user in real time of any unauthorized data collection or tracking detected by the detection unit. If the notification unit detects unauthorized data collection using AI, it notifies the user in real time. It can also notify the user in real time if it detects unauthorized tracking. Furthermore, if it detects unauthorized data collection or tracking, it can send a warning message to the user in real time.

[0074] (Example 2) An information accuracy assurance system according to an embodiment of the present invention evaluates the reliability of online news and social media posts in real time, prevents the spread of false information, analyzes videos and images in real time to detect traces of editing, and detects and notifies users of unauthorized online data collection and tracking. The information accuracy assurance system evaluates the reliability of online news and social media posts in real time and prevents the spread of false information. The information accuracy assurance system also analyzes videos and images in real time to detect traces of editing. Furthermore, the information accuracy assurance system detects unauthorized online data collection and tracking and notifies users in real time. For example, the information accuracy assurance system uses AI to analyze posted content and detect unreliable information. For example, the AI ​​verifies the source and citation of the posted content to evaluate its reliability. Furthermore, the AI ​​also considers past posting history and the reliability of the poster to achieve more accurate evaluations. Next, the information accuracy assurance system uses AI to analyze each frame of video and detect unnatural editing. For example, the AI ​​detects inconsistencies in video metadata and pixels to identify traces of editing. Furthermore, the AI ​​compares the content with past video data to achieve more accurate detection. Furthermore, the information accuracy assurance system uses AI to analyze website tracking codes and detect fraudulent data collection. For example, AI analyzes website scripts and cookies to identify fraudulent data collection. It also notifies users in real time, enabling a prompt response. This allows the information accuracy assurance system to realize a highly reliable information environment. This allows the information accuracy assurance system to realize a highly reliable information environment. For example, it can prevent the spread of false information and deter social unrest. It can also protect fraud and individual reputations. It also protects users' privacy.

[0075] An information accuracy assurance system according to an embodiment includes an evaluation unit, a prevention unit, a detection unit, a protection unit, a detection unit, and a notification unit. The evaluation unit evaluates the reliability of online news and social media posts in real time. The evaluation unit, for example, uses AI to verify the source and citation of the posted content and evaluate the reliability. The evaluation unit can also take into account past posting history and the reliability of the poster when making the evaluation. For example, AI cross-checks the source of the posted content and prioritizes posts with highly reliable sources. AI can also evaluate the reliability of citation sources and rate posts with less reliable sources less highly reliable. AI can also consider the update frequency and past reliability of the source and reflect this in the evaluation. The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The prevention unit, for example, automatically filters unreliable information using AI. The prevention unit can also display a warning message to prevent the spread of false information. The prevention unit can also notify users not to spread unreliable information. The detection unit analyzes videos and images in real time to detect traces of editing. The detection unit, for example, uses AI to analyze each frame of video and detect unnatural edits. The detection unit can also detect inconsistencies in video metadata and pixels. For example, AI can analyze video metadata and detect inconsistencies. AI can also analyze video pixels and detect inconsistencies. AI can also comprehensively analyze video metadata and pixel inconsistencies to identify traces of editing. The protection unit protects fraud and personal reputation based on the traces of editing detected by the detection unit. The protection unit, for example, uses AI to take measures to prevent fraud. The protection unit can also take measures to protect personal reputation. The protection unit can also display warning messages to prevent fraud and protect personal reputation. The detection unit detects unauthorized online data collection and tracking. For example, the detection unit uses AI to analyze website tracking codes to detect unauthorized data collection. The detection unit can also analyze website scripts and cookies to identify unauthorized data collection.For example, AI may analyze website scripts to identify unauthorized data collection. AI may also analyze website cookies to identify unauthorized data collection. AI may also comprehensively analyze website scripts and cookies to identify unauthorized data collection. The notification unit notifies the user of unauthorized data collection or tracking detected by the detection unit in real time. For example, if the notification unit detects unauthorized data collection using AI, it notifies the user in real time. The notification unit may also notify the user in real time if it detects unauthorized tracking. The notification unit may also send a warning message to the user in real time if it detects unauthorized data collection or tracking. As a result, the information accuracy assurance system according to the embodiment can realize a highly reliable information environment.

[0076] The evaluation unit can check the source and citation of the posted content and evaluate its reliability. The evaluation unit can, for example, use AI to cross-check the source of the posted content and prioritize evaluation of posts with highly reliable sources. The evaluation unit can also use AI to evaluate the reliability of citation sources and give a low rating to posts with less reliable sources. The evaluation unit can also use AI to consider the update frequency and past reliability of the source and reflect this in the evaluation. This can increase the reliability of the posted content. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the source and citation of the posted content into AI and have the AI ​​perform an evaluation of reliability.

[0077] The evaluation unit can make an evaluation based on the poster's past posting history or the poster's reliability. The evaluation unit, for example, uses AI to analyze the poster's past posting history and prioritizes evaluation of information from more reliable posters. The evaluation unit can also use AI to consider the poster's past reliability score and reflect it in the evaluation. The evaluation unit can also use AI to check whether the content matches the poster's past postings and reflect it in the evaluation. This makes it possible to make an evaluation that takes the poster's reliability into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the poster's past posting history into AI and have the AI ​​perform a reliability evaluation.

[0078] The evaluation unit can analyze the context of the posted content and reflect it in the evaluation of reliability. The evaluation unit can, for example, use AI to analyze the context of the posted content and prioritize evaluation of posts with highly reliable context. The evaluation unit can also use AI to check the degree of agreement between the context of the posted content and the source and reflect this in the evaluation. The evaluation unit can also use AI to compare the context of the posted content with past reliability scores and reflect this in the evaluation. This enables evaluation that takes the context of the posted content into consideration. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the context of the posted content into AI and have the AI ​​perform the reliability evaluation.

[0079] The prevention unit can prevent the spread of false information based on the information evaluated by the evaluation unit. The prevention unit, for example, uses AI to automatically filter unreliable information based on the evaluation results of the evaluation unit. The prevention unit can also use AI to display a warning message to prevent the spread of false information. The prevention unit can also use AI to notify users not to spread unreliable information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed, for example, using AI or without AI. For example, the prevention unit can input the evaluation results of the evaluation unit into AI and have the AI ​​prevent the spread of false information.

[0080] The detection unit can analyze each frame of the video and detect unnatural edited parts. The detection unit can, for example, use AI to analyze each frame of the video and detect unnatural edited parts. The detection unit can also use AI to analyze each frame of the video and identify traces of editing. The detection unit can also use AI to analyze each frame of the video and detect editing inconsistencies. This makes it possible to detect traces of editing in the video with high accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input data for each frame of the video into AI and have the AI ​​detect the edited parts.

[0081] The detection unit can detect inconsistencies in video metadata and pixels. For example, the detection unit can use AI to analyze video metadata and detect inconsistencies. The detection unit can also use AI to analyze video pixels and detect inconsistencies. The detection unit can also use AI to comprehensively analyze inconsistencies between video metadata and pixels and identify traces of editing. This makes it possible to detect traces of editing in video with high accuracy. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input video metadata and pixel data into AI and have the AI ​​detect edited areas.

[0082] The detection unit can analyze the tracking code of a website and detect fraudulent data collection. The detection unit can, for example, use AI to analyze the tracking code of a website and detect fraudulent data collection. The detection unit can also use AI to analyze the tracking code of a website and detect inconsistencies in data collection. The detection unit can also use AI to analyze the tracking code of a website and identify traces of fraudulent data collection. This allows for highly accurate detection of fraudulent data collection. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input the tracking code of a website into AI and have the AI ​​detect fraudulent data collection.

[0083] The detection unit can analyze website scripts and cookies to identify unauthorized data collection. The detection unit can, for example, use AI to analyze website scripts and identify unauthorized data collection. The detection unit can also use AI to analyze website cookies and identify unauthorized data collection. The detection unit can also use AI to comprehensively analyze website scripts and cookies to identify unauthorized data collection. This makes it possible to identify unauthorized data collection with high accuracy. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input website script and cookie data into AI and have the AI ​​identify unauthorized data collection.

[0084] The notification unit can notify the user of unauthorized data collection or tracking detected by the detection unit in real time. For example, if the notification unit detects unauthorized data collection using AI, it notifies the user in real time. Furthermore, if the notification unit detects unauthorized tracking using AI, it can also notify the user in real time. Furthermore, if the notification unit detects unauthorized data collection or tracking using AI, it can send a warning message to the user in real time. This makes it possible to notify the user of unauthorized data collection or tracking in real time. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input information about unauthorized data collection or tracking detected by the detection unit into AI and have the AI ​​execute real-time notification.

[0085] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. The notification unit, for example, uses AI to estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can provide detailed notification content and clearly state the basis for reliability. If the user is relaxed, the notification unit can provide concise notification content and display only an overview. If the user is in a hurry, the notification unit can provide notification content that highlights only the important points. This makes it possible to provide appropriate notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input the user's emotion data into AI and have the AI ​​adjust the notification content.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. The evaluation unit, for example, uses AI to estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation criteria can be tightened and low-reliability information can be evaluated more strictly. Also, if the user is relaxed, the evaluation criteria can be relaxed and a wider range of information can be accepted. Also, if the user is in a hurry, the evaluation criteria can be quickly applied and reliability can be immediately determined. This makes it possible to provide appropriate evaluation criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's emotion data into AI and have the AI ​​adjust the evaluation criteria.

[0087] The prevention unit can estimate a user's emotions and adjust measures to prevent the spread of false information based on the estimated user emotions. The prevention unit, for example, uses AI to estimate a user's emotions and adjusts measures to prevent the spread of false information based on the estimated user emotions. For example, if a user feels anxious, the prevention unit can strengthen measures to prevent the spread of false information and perform strict filtering. Alternatively, if a user feels relaxed, the prevention unit can relax measures to prevent the spread of false information and perform flexible filtering. Alternatively, if a user is in a hurry, the prevention unit can quickly apply measures to prevent the spread of false information and respond immediately. This makes it possible to provide appropriate measures to prevent the spread of false information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input user emotion data into AI and have the AI ​​adjust the prevention measures.

[0088] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated user emotions. The detection unit, for example, uses AI to estimate the user's emotions and adjust the detection criteria based on the estimated user emotions. For example, if the user is feeling anxious, the detection criteria can be tightened to perform a more detailed analysis. Alternatively, if the user is relaxed, the detection criteria can be relaxed to perform a more flexible analysis. Alternatively, if the user is in a hurry, the detection criteria can be quickly applied to perform an immediate analysis. This makes it possible to provide appropriate detection criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input user emotion data into AI and have the AI ​​adjust the detection criteria.

[0089] The detection unit can estimate the user's emotion and adjust the detection criteria based on the estimated user's emotion. The detection unit can, for example, use AI to estimate the user's emotion and adjust the detection criteria based on the estimated user's emotion. For example, if the user is feeling anxious, the detection criteria can be tightened to perform a more detailed analysis. Alternatively, if the user is relaxed, the detection criteria can be relaxed to perform a more flexible analysis. Alternatively, if the user is in a hurry, the detection criteria can be quickly applied to perform an immediate analysis. This makes it possible to provide appropriate detection criteria according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input user emotion data into AI and have the AI ​​adjust the detection criteria.

[0090] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. The notification unit can estimate the user's emotions using, for example, AI and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification priority can be set high and a quick response can be made. Alternatively, if the user is relaxed, the notification priority can be set low and a flexible response can be made. Alternatively, if the user is in a hurry, the notification priority can be quickly set and a quick response can be made. This makes it possible to provide an appropriate notification priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into AI and have the AI ​​determine the priority of notifications.

[0091] The evaluation unit can customize the evaluation criteria based on the language and region of the posted content. For example, the evaluation unit can use AI to analyze the language of the posted content and apply evaluation criteria appropriate for that language. The evaluation unit can also use AI to analyze regional information of the posted content and apply evaluation criteria appropriate for that region. The evaluation unit can also use AI to set optimal evaluation criteria based on a combination of language and region. This makes it possible to provide appropriate evaluation criteria according to the language and region. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the language and regional information of the posted content into AI and have the AI ​​customize the evaluation criteria.

[0092] The evaluation unit can change the evaluation algorithm depending on the category of the posted content. For example, the evaluation unit can use AI to analyze the category of the posted content and apply a strict evaluation algorithm to the news category. The evaluation unit can also use AI to apply a flexible evaluation algorithm to posts in the entertainment category. The evaluation unit can also use AI to set different evaluation criteria for each category and perform optimal evaluation. This makes it possible to provide an appropriate evaluation algorithm according to the category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input category information of the posted content into AI and have the AI ​​change the evaluation algorithm.

[0093] The evaluation unit can analyze images and videos of the posted content and evaluate them in combination with text information. The evaluation unit can, for example, use AI to analyze images of the posted content and check whether they match the text information. The evaluation unit can also use AI to analyze videos of the posted content and check whether they match the text information. The evaluation unit can also use AI to combine the results of image and video analysis with text information to perform a comprehensive evaluation. This enables a comprehensive evaluation that includes images and videos. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, or can be performed without using AI. For example, the evaluation unit can input image and video data of the posted content into AI and have the AI ​​perform an evaluation in combination with the text information.

[0094] The prevention unit can automatically filter related posts to prevent the spread of false information. The prevention unit can, for example, use AI to automatically detect and filter posts related to false information. The prevention unit can also, for example, use AI to automatically delete related posts to prevent the spread of false information. The prevention unit can also, for example, use AI to automatically hide posts related to false information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit can be performed, for example, using AI or without using AI. For example, the prevention unit can input post data related to false information into AI and have the AI ​​perform the filtering.

[0095] The prevention unit can send a warning message to the user to prevent the spread of false information. For example, if the prevention unit detects the spread of false information using AI, it sends the warning message to the user. The prevention unit can also display a warning message to the user to prevent the spread of false information using AI. The prevention unit can also send a warning message to the user by email to prevent the spread of false information using AI. This makes it possible to provide a warning message to prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, or may be performed without using AI. For example, the prevention unit can input data on the spread of false information into AI and cause the AI ​​to send a warning message.

[0096] The prevention unit can estimate the user's emotions and prioritize the prevention measures based on the estimated user emotions. The prevention unit, for example, uses AI to estimate the user's emotions and prioritize the prevention measures based on the estimated user emotions. For example, if the user is feeling anxious, the prevention measures can be prioritized high and a quick response can be made. Also, if the user is relaxed, the prevention measures can be prioritized low and a flexible response can be made. Also, if the user is in a hurry, the prevention measures can be prioritized quickly and a quick response can be made. This makes it possible to provide appropriate priorities for the prevention measures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prevention unit can be performed using, for example, AI, or without AI. For example, the prevention unit can input the user's emotion data into AI and have the AI ​​determine the priority of the prevention measures.

[0097] The prevention unit can cooperate with social media platforms to prevent the spread of false information. For example, the prevention unit can cooperate with social media platforms using AI to perform filtering to prevent the spread of false information. The prevention unit can also cooperate with social media platforms using AI to display warning messages to prevent the spread of false information. The prevention unit can also cooperate with social media platforms using AI to delete posts to prevent the spread of false information. This makes it possible to prevent the spread of false information in cooperation with social media platforms. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input data from social media platforms into AI and have the AI ​​prevent the spread of false information.

[0098] The prevention unit can automatically display relevant news articles to prevent the spread of false information. For example, the prevention unit can automatically display relevant, reliable news articles to prevent the spread of false information using AI. The prevention unit can also notify users of relevant news articles to prevent the spread of false information using AI. The prevention unit can also display relevant news articles on social media platforms to prevent the spread of false information using AI. In this way, the spread of false information can be prevented by automatically displaying relevant news articles. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input data of relevant news articles into AI and have the AI ​​perform the display.

[0099] The prevention unit can improve the prevention measures by reflecting user feedback in order to prevent the spread of false information. The prevention unit can, for example, use AI to collect user feedback and reflect it in improving the prevention measures. The prevention unit can also use AI to adjust the measures to prevent the spread of false information based on user feedback. The prevention unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the prevention measures. In this way, the prevention measures can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the prevention unit can be performed, for example, using AI or without using AI. For example, the prevention unit can input user feedback data into AI and have the AI ​​improve the prevention measures.

[0100] The detection unit can analyze the content of the video or image and identify traces of editing by comparing it with past data. The detection unit can, for example, use AI to analyze the content of the video or image and identify traces of editing by comparing it with past data. The detection unit can also use AI to analyze the content of the video or image and detect parts that do not match past data. The detection unit can also use AI to analyze the content of the video or image and identify unnatural edited parts by comparing it with past data. This makes it possible to identify traces of editing by comparing it with past data. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input video or image data into AI and have the AI ​​compare it with past data.

[0101] The detection unit can change the detection algorithm depending on the video or image format. For example, the detection unit can use AI to apply the optimal detection algorithm depending on the video format (MP4, AVI, etc.). The detection unit can also use AI to apply the optimal detection algorithm depending on the image format (JPEG, PNG, etc.). The detection unit can also use AI to select the optimal detection algorithm based on the video or image format. This makes it possible to provide an appropriate detection algorithm depending on the format. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video or image format information to AI and have the AI ​​change the detection algorithm.

[0102] The detection unit can improve detection accuracy based on the location and time of video or image capture. The detection unit can, for example, use AI to analyze the location of video or image capture and improve detection accuracy. The detection unit can also use AI to analyze the time of video or image capture and improve detection accuracy. The detection unit can also use AI to comprehensively analyze the location and time of video or image capture and improve detection accuracy. This makes it possible to improve detection accuracy based on the location and time of capture. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input data on the location and time of video or image capture into AI and have the AI ​​improve detection accuracy.

[0103] The detection unit can analyze audio data of video or images and perform detection by combining it with the video content. The detection unit can, for example, use AI to analyze the audio data of the video and check whether it matches the video content. The detection unit can also use AI to analyze the audio data of the image and check whether it matches the video content. The detection unit can also use AI to analyze the audio data of the video or images and perform detection by combining it with the video content. This makes it possible to analyze audio data and combine it with the video content. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the audio data of the video or image into AI and have the AI ​​perform detection by combining it with the video content.

[0104] The protection unit can protect fraudulent acts and personal reputation based on the traces of editing detected by the detection unit. The protection unit can, for example, use AI to take measures to prevent fraudulent acts based on the detection results of the detection unit. The protection unit can also use AI to take measures to protect personal reputation. The protection unit can also use AI to display a warning message to protect fraudulent acts and personal reputation based on the detection results of the detection unit. This makes it possible to protect fraudulent acts and personal reputation based on the detected traces of editing. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without AI. For example, the protection unit can input the detection results of the detection unit into AI and have the AI ​​perform protection of fraudulent acts and personal reputation.

[0105] The protection unit can automatically collect relevant evidence to protect fraudulent acts and personal reputation. For example, the protection unit can use AI to automatically collect evidence of fraudulent acts and take protective measures. The protection unit can also use AI to automatically collect evidence to protect personal reputation and take measures. The protection unit can also use AI to automatically collect evidence to protect fraudulent acts and personal reputation and display a warning message. This makes it possible to automatically collect relevant evidence to protect fraudulent acts and personal reputation. Some or all of the above-mentioned processing in the protection unit can be performed, for example, using AI or without AI. For example, the protection unit can input evidential data related to fraudulent acts and personal reputation into AI and have the AI ​​collect evidence.

[0106] The protection unit can send a warning message to the user to protect against fraudulent acts or personal reputation. For example, if the protection unit detects fraudulent acts using AI, it sends a warning message to the user. The protection unit can also display a warning message to the user to protect against personal reputation using AI. The protection unit can also send a warning message to the user by email to protect against fraudulent acts or personal reputation using AI. In this way, a warning message to protect against fraudulent acts or personal reputation can be provided. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input data related to fraudulent acts or personal reputation into AI and have the AI ​​send a warning message.

[0107] The protection unit can estimate the user's emotions and prioritize the protection measures based on the estimated user emotions. The protection unit can estimate the user's emotions using, for example, AI and prioritize the protection measures based on the estimated user emotions. For example, if the user feels anxious, the protection unit can set the priority of the protection measures high and respond quickly. Alternatively, if the user is relaxed, the protection unit can set the priority of the protection measures low and respond flexibly. Alternatively, if the user is in a hurry, the protection measures can be quickly prioritized and responded immediately. This makes it possible to provide appropriate priorities for the protection measures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit can be performed using, for example, AI, or without AI. For example, the protection unit can input the user's emotion data into AI and have the AI ​​determine the priority of the protection measures.

[0108] The protection unit can cooperate with legal authorities to protect against fraudulent acts and personal honor. For example, if the protection unit detects fraudulent acts using AI, it can cooperate with legal authorities to take measures. The protection unit can also cooperate with legal authorities to take measures to protect personal honor using AI. The protection unit can also cooperate with legal authorities to display a warning message to protect against fraudulent acts and personal honor using AI. This makes it possible to protect against fraud and personal honor in cooperation with legal authorities. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or can be performed without using AI. For example, the protection unit can input data related to fraudulent acts and personal honor into AI and have the AI ​​cooperate with legal authorities.

[0109] The protection unit may automatically display relevant news articles to prevent fraud and protect individual reputation. For example, the protection unit may use AI to automatically display relevant, reliable news articles to prevent fraud. The protection unit may also use AI to notify users of relevant news articles to protect individual reputation. The protection unit may also use AI to display relevant news articles on social media platforms to protect fraud and individual reputation. This makes it possible to protect against fraud and individual reputation by automatically displaying relevant news articles. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit may input data of relevant news articles into AI and have the AI ​​perform the display.

[0110] The protection unit can improve protection measures by reflecting user feedback to protect against fraudulent activities and personal reputation. For example, the protection unit can use AI to collect user feedback and reflect it in improving the protection measures. The protection unit can also use AI to adjust measures to protect against fraudulent activities and personal reputation based on user feedback. The protection unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the protection measures. In this way, the protection measures can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without using AI. For example, the protection unit can input user feedback data into AI and have the AI ​​improve the protection measures.

[0111] The detection unit can integrate multiple data sources to detect fraudulent online data collection and tracking. The detection unit can, for example, use AI to integrate the multiple data sources and detect fraudulent data collection. The detection unit can also use AI to integrate the multiple data sources and detect tracking discrepancies. The detection unit can also use AI to integrate the multiple data sources and identify traces of fraudulent data collection and tracking. This makes it possible to integrate the multiple data sources and detect fraudulent data collection and tracking. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input data from multiple data sources into AI and have the AI ​​perform integrated analysis.

[0112] The detection unit can change the detection algorithm depending on the type and category of the website. For example, the detection unit can use AI to apply an optimal detection algorithm depending on the type of website (news site, shopping site, etc.). The detection unit can also use AI to apply an optimal detection algorithm depending on the category of the website (entertainment, education, etc.). The detection unit can also use AI to select an optimal detection algorithm based on the type and category of the website. This makes it possible to provide an appropriate detection algorithm depending on the type and category. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without using AI. For example, the detection unit can input website type and category information into AI and have the AI ​​change the detection algorithm.

[0113] The detection unit can analyze website access history and compare it with past data to identify unauthorized data collection. The detection unit can, for example, use AI to analyze website access history and compare it with past data to identify unauthorized data collection. The detection unit can also use AI to analyze website access history and detect abnormal patterns. The detection unit can also use AI to analyze website access history and identify parts that do not match past data. This makes it possible to identify unauthorized data collection by comparing it with past data. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input website access history data into AI and have the AI ​​compare it with past data.

[0114] The detection unit can analyze the website user interface and identify fraudulent data collection methods. The detection unit can, for example, use AI to analyze the website user interface and identify fraudulent data collection methods. The detection unit can also use AI to analyze the website user interface and detect inconsistencies in data collection. The detection unit can also use AI to analyze the website user interface and identify traces of fraudulent data collection. This makes it possible to analyze the user interface and identify fraudulent data collection methods. Some or all of the above-described processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input website user interface data into AI and have the AI ​​identify fraudulent data collection methods.

[0115] The notification unit can customize the notification content based on the user's past behavioral history. The notification unit can, for example, use AI to analyze the user's past behavioral history and provide optimal notification content. The notification unit can also use AI to provide relevant notification content based on the user's past behavioral history. The notification unit can also use AI to provide customized notification content by referring to the user's past behavioral history. This makes it possible to provide customized notification content based on the user's past behavioral history. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input the user's past behavioral history data into AI and have the AI ​​customize the notification content.

[0116] The notification unit can optimize the notification content based on the user's device information. The notification unit can, for example, use AI to analyze the user's device information and provide optimal notification content. The notification unit can also use AI to optimize the notification content based on the user's device information. The notification unit can also use AI to provide optimal notification content by referring to the user's device information. This makes it possible to provide optimized notification content based on the user's device information. Some or all of the above-described processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​optimize the notification content.

[0117] The notification unit can make the notification content multilingual according to the user's language setting. The notification unit can, for example, use AI to analyze the user's language setting and provide the notification content in the most appropriate language. The notification unit can also use AI to make the notification content multilingual based on the user's language setting. The notification unit can also use AI to provide the notification content in the most appropriate language, taking the user's language setting into consideration. This makes it possible to provide multilingual notification content according to the user's language setting. Some or all of the above-described processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the user's language setting data into AI and have the AI ​​perform multilingual notification content.

[0118] The notification unit can adjust the notification content based on the user's current activity status. The notification unit can, for example, use AI to analyze the user's current activity status and provide optimal notification content. The notification unit can also use AI to adjust the notification content based on the user's current activity status. The notification unit can also use AI to provide optimal notification content by referring to the user's current activity status. This makes it possible to provide appropriate notification content based on the user's current activity status. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the user's current activity status data into AI and have the AI ​​adjust the notification content.

[0119] The notification unit can improve the notification content by reflecting user feedback. The notification unit can, for example, use AI to collect user feedback and reflect it in improving the notification content. The notification unit can also use AI to adjust the notification content based on user feedback. The notification unit can also use AI to analyze user feedback and evaluate and improve the effectiveness of the notification content. This makes it possible to improve the notification content by reflecting user feedback. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can input user feedback data into AI and have the AI ​​improve the notification content. === Hard Collateral 1-1 === Each of the multiple elements, including the evaluation unit, prevention unit, detection unit, protection unit, detection unit, and notification unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 and evaluates the reliability of online news and social media posts in real time. The prevention unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prevents the spread of false information. The detection unit, for example, analyzes videos and images in real time using the camera 42 of the smart device 14 to detect traces of editing. The protection unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and protects against fraud and personal reputation. The detection unit, for example, detects illegal online data collection and tracking using the communication I / F 44 of the smart device 14. The notification unit, for example, notifies the user in real time using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the evaluation unit, prevention unit, detection unit, protection unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the smart glasses 214 and evaluates the reliability of online news and social media posts in real time. The prevention unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prevents the spread of false information. The detection unit, for example, analyzes videos and images in real time using the camera 42 of the smart glasses 214 to detect traces of editing. The protection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and protects against fraud and personal reputation. The detection unit, for example, detects illegal online data collection and tracking using the communication I / F 44 of the smart glasses 214. The notification unit, for example, notifies the user in real time using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the evaluation unit, prevention unit, detection unit, protection unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the headset type terminal 314 and evaluates the reliability of online news and social media posts in real time. The prevention unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prevents the spread of false information. The detection unit, for example, analyzes videos and images in real time using the camera 42 of the headset type terminal 314 to detect traces of editing. The protection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and protects against fraud and personal reputation. The detection unit, for example, detects illegal online data collection and tracking using the communication I / F 44 of the headset type terminal 314. The notification unit, for example, notifies the user in real time using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the evaluation unit, prevention unit, detection unit, protection unit, detection unit, and notification unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 and evaluates the reliability of online news and social media posts in real time. The prevention unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prevents the spread of false information. The detection unit, for example, analyzes videos and images in real time using the camera 42 of the robot 414 to detect traces of editing. The protection unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and protects against fraud and personal reputation. The detection unit, for example, detects illegal online data collection and tracking using the communication I / F 44 of the robot 414. The notification unit, for example, notifies the user in real time using the speaker 240 of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation criteria can be tightened and low-reliability information can be evaluated more strictly. Alternatively, if the user is relaxed, the evaluation criteria can be relaxed and a wider range of information can be accepted. Alternatively, if the user is in a hurry, the evaluation criteria can be quickly applied and reliability can be immediately determined. This allows for providing appropriate evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's emotion data into an AI and have the AI ​​adjust the evaluation criteria.

[0122] The prevention unit can estimate the user's emotions and adjust measures to prevent the spread of false information based on the estimated user emotions. For example, if the user is feeling anxious, the prevention measures for the spread of false information can be strengthened and strict filtering can be performed. Alternatively, if the user is relaxed, the prevention measures for the spread of false information can be relaxed and flexible filtering can be performed. Alternatively, if the user is in a hurry, the prevention measures for the spread of false information can be quickly applied and an immediate response can be provided. This makes it possible to provide appropriate measures to prevent the spread of false information according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prevention unit can be performed using an AI, for example, or without an AI. For example, the prevention unit can input the user's emotion data into an AI and have the AI ​​adjust the prevention measures.

[0123] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated user emotions. For example, if the user is feeling anxious, the detection criteria can be tightened to perform a more detailed analysis. Alternatively, if the user is relaxed, the detection criteria can be relaxed to perform a more flexible analysis. Alternatively, if the user is in a hurry, the detection criteria can be quickly applied to perform an immediate analysis. This allows for providing appropriate detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the user's emotion data into an AI and have the AI ​​adjust the detection criteria.

[0124] The detection unit can estimate the user's emotion and adjust the detection criteria based on the estimated user emotion. For example, if the user is feeling anxious, the detection criteria can be tightened to perform a more detailed analysis. Alternatively, if the user is relaxed, the detection criteria can be relaxed to perform a more flexible analysis. Alternatively, if the user is in a hurry, the detection criteria can be quickly applied to perform an immediate analysis. This allows for providing appropriate detection criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the user's emotion data into an AI and have the AI ​​adjust the detection criteria.

[0125] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification priority can be set high and a prompt response can be made. Alternatively, if the user is relaxed, the notification priority can be set low and a flexible response can be made. Alternatively, if the user is in a hurry, the notification priority can be set quickly and a prompt response can be made. This makes it possible to provide an appropriate notification priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into an AI and have the AI ​​determine the notification priority.

[0126] The evaluation unit can customize the evaluation criteria based on the language and region of the posted content. For example, the evaluation unit can use AI to analyze the language of the posted content and apply evaluation criteria appropriate for that language. The evaluation unit can also use AI to analyze regional information of the posted content and apply evaluation criteria appropriate for that region. The evaluation unit can also use AI to set optimal evaluation criteria based on a combination of language and region. This makes it possible to provide appropriate evaluation criteria according to the language and region. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the language and regional information of the posted content into AI and have the AI ​​customize the evaluation criteria.

[0127] The evaluation unit can change the evaluation algorithm depending on the category of the posted content. For example, the evaluation unit can use AI to analyze the category of the posted content and apply a strict evaluation algorithm to the news category. The evaluation unit can also use AI to apply a flexible evaluation algorithm to posts in the entertainment category. The evaluation unit can also use AI to set different evaluation criteria for each category and perform optimal evaluation. This makes it possible to provide an appropriate evaluation algorithm according to the category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input category information of the posted content into AI and have the AI ​​change the evaluation algorithm.

[0128] The evaluation unit can analyze images and videos of the posted content and perform an evaluation by combining them with text information. For example, the evaluation unit can use AI to analyze images of the posted content and check whether they match the text information. The evaluation unit can also use AI to analyze videos of the posted content and check whether they match the text information. The evaluation unit can also perform a comprehensive evaluation by combining the results of image and video analysis using AI with the text information. This enables a comprehensive evaluation that includes images and videos. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input image and video data of the posted content into AI and have the AI ​​perform an evaluation in combination with the text information.

[0129] The prevention unit can automatically filter related posts to prevent the spread of false information. For example, the prevention unit can use AI to automatically detect and filter posts related to false information. The prevention unit can also use AI to automatically delete related posts to prevent the spread of false information. The prevention unit can also use AI to automatically hide posts related to false information. This can effectively prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input post data related to false information into AI and have the AI ​​perform the filtering.

[0130] The prevention unit can send a warning message to the user to prevent the spread of false information. For example, if the spread of false information is detected using AI, the prevention unit sends a warning message to the user. The prevention unit can also display a warning message to the user to prevent the spread of false information using AI. The prevention unit can also send a warning message to the user by email to prevent the spread of false information using AI. This makes it possible to provide a warning message to prevent the spread of false information. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, for example, or may be performed without using AI. For example, the prevention unit can input data on the spread of false information into AI and cause the AI ​​to send a warning message.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The evaluation department evaluates the reliability of online news and social media posts in real time. The evaluation department uses AI to check the source and citation of the posted content and evaluate its reliability. It can also take into account past posting history and the reliability of the poster when making its evaluation. For example, AI can cross-check the source of the posted content and prioritize posts with highly reliable sources. AI can also evaluate the reliability of citations and give a lower rating to posts with less reliable sources. Furthermore, AI can take into account the update frequency and past reliability of the source and reflect this in its evaluation. Step 2: The prevention unit prevents the spread of false information based on the information evaluated by the evaluation unit. The prevention unit uses AI to automatically filter out unreliable information. It can also display warning messages to prevent the spread of false information. It can also notify users not to spread unreliable information. Step 3: The detection unit analyzes the video and images in real time to detect signs of editing. The detection unit uses AI to analyze each frame of the video to detect unnatural edits. It can also detect inconsistencies in video metadata and pixels. For example, AI can analyze video metadata to detect inconsistencies. AI can also analyze video pixels to detect inconsistencies. Furthermore, AI can comprehensively analyze video metadata and pixel inconsistencies to identify signs of editing. Step 4: The protection unit protects against fraud and personal reputation based on the edit traces detected by the detection unit. The protection unit uses AI to take measures to prevent fraud and can also take measures to protect personal reputation. In addition, it can display warning messages to prevent fraud and protect personal reputation. Step 5: The detection unit detects unauthorized online data collection and tracking. The detection unit uses AI to analyze website tracking codes to detect unauthorized data collection. It can also analyze website scripts and cookies to identify unauthorized data collection. For example, AI can analyze website scripts to identify unauthorized data collection. AI can also analyze website cookies to identify unauthorized data collection. AI can also comprehensively analyze website scripts and cookies to identify unauthorized data collection. Step 6: The notification unit notifies the user in real time of any unauthorized data collection or tracking detected by the detection unit. If the notification unit detects unauthorized data collection using AI, it notifies the user in real time. It can also notify the user in real time if it detects unauthorized tracking. Furthermore, if it detects unauthorized data collection or tracking, it can send a warning message to the user in real time.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an evaluation department that evaluates the reliability of online news or SNS posts in real time; a prevention unit that prevents the spread of false information based on the information evaluated by the evaluation unit; A detection unit that analyzes videos and images in real time to detect traces of editing, a protection unit that protects against fraud and personal reputation based on the edit traces detected by the detection unit; a detection unit that detects unauthorized online data collection and tracking; a notification unit that notifies in real time of unauthorized data collection or tracking detected by the detection unit. A system characterized by:

2. The evaluation unit Check the source and citation of the post to assess its credibility 2. The system of claim 1.

3. The evaluation unit Rating based on past posting history or poster credibility 2. The system of claim 1.

4. The evaluation unit Analyze the context of posts and reflect it in assessing their credibility 2. The system of claim 1.

5. The prevention unit is Preventing the spread of false information based on the information evaluated by the evaluation unit 2. The system of claim 1.

6. The detection unit Analyzes each frame of the video to detect unnatural edits 2. The system of claim 1.

7. The detection unit Detect video metadata and pixel inconsistencies 2. The system of claim 1.

8. The detection unit Analyzes website tracking code and detects unauthorized data collection 2. The system of claim 1.

Images