system

Abstract

The system according to this embodiment aims to accurately determine the veracity of information disseminated through media and social media, and to issue early warnings. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a warning unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates reliability based on the data analyzed by the analysis unit. The warning unit issues an early warning based on the results obtained by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the authenticity of information transmitted in the media and SNS has not been sufficiently discriminated with high accuracy to prevent the spread of a large amount of false information.

[0005] The system according to the embodiment aims to accurately discriminate the authenticity of information transmitted in the media and SNS and issue an early warning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a warning unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates reliability based on the data analyzed by the analysis unit. The warning unit issues an early warning based on the results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately determine the veracity of information disseminated through media and social media, and issue early warnings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The information reliability evaluation system according to an embodiment of the present invention is a system that comprehensively verifies the truthfulness of information disseminated through media and social media and calculates a reliability score. This information reliability evaluation system can prevent the spread of large-scale misinformation. The information reliability evaluation system analyzes multimodal data such as text, images, videos, and audio, and determines truthfulness through information source reliability checks, comparison with past data, and cross-referencing methods. Furthermore, the information reliability evaluation system detects the spread of fake news in each region and issues early warnings. For example, the information reliability evaluation system collects multimodal data such as text, images, videos, and audio. For example, social media posts, news articles, and digital content are among the target data. Next, the information reliability evaluation system integrates the collected data to form a multimodal dataset. For example, it combines the text of an article, related images, videos, and audio into a single dataset. Next, the information reliability evaluation system analyzes the content of the text using natural language processing (NLP) and extracts keywords, themes, and context. It also extracts relevant data from visual information using image recognition and video analysis. It converts audio data into text using speech recognition technology and analyzes it similarly. This makes it possible to detect consistency and inconsistencies in information. Furthermore, the information reliability evaluation system investigates sources and scores their reliability to assess the trustworthiness of information sources. This includes cross-referencing with reliable sources such as prominent news sites, government agencies, and official statistical data. The information reliability evaluation system has a user feedback function, which allows for the addition of user-submitted assessments of the accuracy of information and continuous improvement of the algorithm. Finally, based on the evaluation results, the information reliability evaluation system issues early warnings for areas and topics suspected of being prone to the spread of fake news. This is notified to relevant parties such as social media platforms, local governments, and media organizations. This prevents the spread of misinformation and avoids negative impacts on society. In this way, the information reliability evaluation system can accurately determine the trustworthiness of information and prevent the spread of misinformation.

[0029] The information reliability evaluation system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a warning unit. The collection unit collects data. The collection unit collects multimodal data such as text, images, videos, and audio. The collection unit targets, for example, social media posts, news articles, and digital content. The collection unit can collect data from the internet using, for example, web scraping technology. The collection unit can also obtain data from specific data sources using APIs. Furthermore, the collection unit can collect data through manual input from users. For example, the collection unit can collect text data and image data provided by users. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the content of the text using, for example, natural language processing (NLP) and extracts keywords, themes, and context. The analysis unit divides the text into words using, for example, morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and clarify the relationships between subjects, predicates, objects, etc. Furthermore, the analysis unit can understand the meaning of text using semantic analysis and extract keywords and themes. The analysis unit extracts relevant data from visual information using image recognition and video analysis. For example, the analysis unit can detect specific objects in an image using object detection technology. It can also identify people in an image using facial recognition technology. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. The analysis unit converts audio data into text and analyzes it using speech recognition technology. For example, the analysis unit can convert audio data into text data using speech recognition algorithms. It can also preprocess audio data, performing noise reduction and speech normalization. The evaluation unit evaluates reliability based on the data analyzed by the analysis unit. For example, the evaluation unit investigates sources and scores reliability. For example, the evaluation unit can evaluate reliability based on sources such as reliable websites, academic papers, and official databases.Furthermore, the evaluation unit can cross-reference multiple data sources to assess the consistency and accuracy of the data. In addition, the evaluation unit accepts user feedback and continuously improves the algorithm. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust the algorithm parameters. The warning unit issues early warnings based on the results obtained by the evaluation unit. For example, the warning unit can issue warnings for areas or topics suspected of being the source of fake news. The warning unit can send notifications to relevant parties such as social media platforms, local governments, and media organizations. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold. Thus, the information reliability evaluation system according to the embodiment enables data collection, analysis, reliability evaluation, and the issuance of early warnings.

[0030] The data collection unit collects data. For example, the data collection unit collects multimodal data such as text, images, videos, and audio. Specifically, the data collection unit targets social media posts, news articles, and digital content, and can collect data from the internet using web scraping technology. Web scraping technology is a technique that automatically extracts data from specific web pages, and the data collection unit uses this to efficiently collect large amounts of data. The data collection unit can also obtain data from specific data sources using APIs. APIs are interfaces for exchanging data between different software, and the data collection unit can use them to directly obtain data from reliable data sources. Furthermore, the data collection unit can also collect data through manual input from users. For example, the data collection unit can collect text data and image data provided by users. Data manually entered by users is automatically saved to a database by the data collection unit and used for analysis by the subsequent analysis unit. This allows the data collection unit to collect a wide range of data from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and evaluation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses natural language processing (NLP) to analyze the content of text and extract keywords, themes, and context. Specifically, it uses morphological analysis to divide the text into words and identify the part of speech of each word. It can also use grammatical analysis to analyze the structure of sentences and clarify relationships such as subject, predicate, and object. Furthermore, it can use semantic analysis to understand the meaning of the text and extract keywords and themes. For example, by analyzing the text data of a news article and extracting important keywords and themes, the content of the article can be summarized. The analysis unit extracts relevant data from visual information using image recognition and video analysis. For example, it can use object detection technology to detect specific objects in an image. It can also use facial recognition technology to identify people in an image. Furthermore, it can use scene analysis technology to analyze scenes in a video and detect specific events or actions. For example, it can analyze surveillance camera footage and detect suspicious movements. The analysis unit converts audio data into text using speech recognition technology and analyzes it. For example, it can use speech recognition algorithms to convert audio data into text data. Furthermore, the system can preprocess audio data, performing noise reduction and normalization. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The evaluation unit assesses reliability based on data analyzed by the analysis unit. For example, the evaluation unit investigates sources and scores their reliability. Specifically, it can evaluate reliability based on sources such as reliable websites, academic papers, and official databases. For instance, if a news article's source is a highly reliable news organization, the article's reliability will be highly rated. Conversely, if the source is unclear or from an unreliable website, the article's reliability will be low. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. For example, if the same news is reported by multiple reliable sources, the news's reliability will be highly rated. Furthermore, the evaluation unit accepts user feedback and continuously improves the algorithm. For example, it can collect user opinions and improvement suggestions and adjust algorithm parameters. This allows the evaluation unit to always provide highly accurate reliability assessments based on the latest information. Additionally, the evaluation unit can verify the accuracy of the evaluation algorithm based on past evaluation results and make improvements as needed. For example, it can compare past evaluation results with actual reliability to verify the accuracy of the evaluation algorithm. Furthermore, the evaluation unit can visualize the evaluation results and provide them to users in an easy-to-understand manner. For example, it can display confidence scores in graphs and charts, allowing users to intuitively understand them. This enables the evaluation unit to provide users with highly reliable information and improve the reliability of the data.

[0033] The warning unit issues early warnings based on the results obtained by the evaluation unit. For example, the warning unit can issue warnings for areas or topics suspected of being the source of fake news. Specifically, it can send notifications to relevant parties such as social media platforms, local governments, and media organizations. For example, it can issue warnings to social media platforms when there is a surge in posts related to specific keywords or themes, prompting them to take measures to prevent the spread of fake news. It can also issue warnings to local governments when fake news is spreading in a particular area, urging them to provide accurate information to residents. Furthermore, it can issue warnings to media organizations regarding topics suspected of being the source of fake news, urging them to provide accurate reporting. The warning unit can also set the content and conditions for issuing warnings. For example, it can issue a warning when information related to specific keywords or themes exceeds a certain threshold. This allows the warning unit to issue warnings quickly and appropriately, preventing the spread of fake news. In addition, the warning unit can record the history of warnings issued for later reference. For example, by recording the content and conditions of past warnings and analyzing them later, the accuracy and effectiveness of the warnings can be verified. Furthermore, the warning unit can receive feedback from users and continuously improve the content and conditions of the warnings. This allows the warning unit to always provide highly accurate warnings based on the latest information, preventing the spread of fake news.

[0034] The data collection unit can collect multimodal data such as text, images, videos, and audio. For example, the data collection unit can use web scraping techniques to collect text data. For instance, the data collection unit can automatically extract text data from a specific website and store it in a database. The data collection unit can also use image recognition techniques to collect image data. For example, the data collection unit can automatically collect images from the internet and store them in an image database. Furthermore, the data collection unit can use video analysis techniques to collect video data. For example, the data collection unit can collect video data from video sharing sites and store it in a video database. The data collection unit can also use speech recognition techniques to collect audio data. For example, the data collection unit can collect audio data from podcasts and audio media and store it in an audio database. This enables the data collection unit to collect a variety of data formats. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data to be collected into a generating AI and have the generating AI perform the data collection.

[0035] The analysis unit can analyze the content of text using natural language processing and extract keywords, themes, and context. For example, the analysis unit can divide the text into words using morphological analysis and identify the part of speech of each word. For example, the analysis unit takes text data as input and uses a morphological analysis algorithm to identify the part of speech of each word. The analysis unit can also analyze the structure of a sentence using grammatical analysis and clarify the relationships between subjects, predicates, objects, etc. For example, the analysis unit uses a grammatical analysis algorithm to analyze the structure of a sentence and identify the relationships between the elements of the sentence. Furthermore, the analysis unit can understand the meaning of the text using semantic analysis and extract keywords and themes. For example, the analysis unit uses a semantic analysis algorithm to analyze the meaning of the text and extract important keywords and themes. This allows the analysis unit to analyze the content of the text data in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI perform the extraction of keywords, themes, and context.

[0036] The analysis unit can extract relevant data from visual information using image recognition and video analysis. For example, the analysis unit can detect specific objects in an image using object detection technology. For instance, it can take image data as input and use an object detection algorithm to identify objects in the image. The analysis unit can also identify people in an image using face recognition technology. For example, it can use a face recognition algorithm to identify people in an image and detect specific individuals. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. For example, it can take video data as input and use a scene analysis algorithm to identify events in the video. This enables the analysis unit to extract relevant data from visual information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input image data or video data into a generating AI and have the generating AI perform the extraction of relevant data.

[0037] The analysis unit can convert audio data into text using speech recognition technology and then analyze it. For example, the analysis unit can convert audio data into text data using a speech recognition algorithm. For instance, the analysis unit takes audio data as input and converts the audio to text using a speech recognition algorithm. The analysis unit can also preprocess the audio data, performing noise reduction and speech normalization. For example, the analysis unit can remove noise from the audio data to improve the audio quality. Furthermore, the analysis unit can analyze the transcribed audio data and extract keywords and themes. For example, the analysis unit takes text data as input and extracts keywords and themes using natural language processing technology. This enables the analysis unit to convert audio data into text and analyze it. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input audio data into a generating AI and have the generating AI perform the transcription and analysis of the audio data.

[0038] The evaluation unit can investigate sources and score their reliability. For example, the evaluation unit can assess reliability based on sources such as highly reliable websites, academic papers, and official databases. For instance, to evaluate the reliability of a source, the evaluation unit can refer to the source's past performance and reliability evaluation results. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. For example, the evaluation unit can compare information from multiple data sources and highly value consistent information. Furthermore, the evaluation unit can accept user feedback and continuously improve its algorithms. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust algorithm parameters. This enables the evaluation unit to assess the reliability of sources. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input source data into a generating AI and have the generating AI perform reliability scoring.

[0039] The evaluation unit can receive feedback from users and continuously improve the algorithm. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust the algorithm parameters. For example, the evaluation unit can make adjustments to improve the accuracy of the algorithm based on user feedback. The evaluation unit can also analyze user feedback and identify areas for improvement in the algorithm. For example, the evaluation unit can analyze user feedback and identify weaknesses and areas for improvement in the algorithm. Furthermore, the evaluation unit can develop new algorithms based on user feedback. For example, the evaluation unit can design and implement a new evaluation algorithm based on user feedback. This makes it possible for the evaluation unit to improve the algorithm based on user feedback. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback data into a generating AI and have the generating AI perform improvements to the algorithm.

[0040] The warning unit can issue early warnings for areas or topics suspected of being affected by the spread of fake news. The warning unit can send notifications to relevant parties, such as social media platforms, local governments, and news organizations. For example, the warning unit can collect information on areas or topics suspected of being affected by fake news and issue warnings to relevant parties. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold. Furthermore, the warning unit can select the method of issuing warnings. For example, the warning unit can issue warnings via email, SMS, or push notifications. This enables the warning unit to provide early warnings about the spread of fake news. Some or all of the above-described processes in the warning unit may be performed using AI, or not. For example, the warning unit can input fake news spread data into a generating AI, which can then execute the early warning process.

[0041] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data types that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past data collection history, identify frequently collected data types, and prioritize collecting that data. The data collection unit can also identify the most efficient collection time from the user's past data collection history and perform collection during that time. For example, the data collection unit can analyze the user's past data collection history, identify the most efficient collection time, and perform data collection during that time. Furthermore, the data collection unit can select a collection method (automatic collection, manual collection, etc.) based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the optimal collection method. This makes it possible for the data collection unit to select the optimal collection method based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal data collection method.

[0042] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can analyze the user's current areas of interest and prioritize collecting relevant data. The data collection unit can also filter out unnecessary data based on the user's current areas of interest to ensure efficient collection. For example, the data collection unit can analyze the user's areas of interest and filter out unnecessary data. Furthermore, if the user's areas of interest change, the data collection unit can update the collection targets in real time to accommodate the latest areas of interest. For example, the data collection unit can detect changes in the user's areas of interest and update the collection targets in real time. This allows the data collection unit to filter data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will analyze the user's geographical location information and prioritize the collection of data related to that region. The data collection unit can also filter and collect region-specific data based on the user's geographical location information. For example, the data collection unit will analyze the user's geographical location information and filter out region-specific data. Furthermore, if the user is on the move, the data collection unit can collect relevant data in real time based on their current location. For example, the data collection unit will analyze the user's geographical location information in real time and collect relevant data based on their current location. This enables the data collection unit to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity and prioritize collecting data related to topics the user has shown interest in. The data collection unit can also analyze the user's social media activity and filter to collect data of interest. For example, the data collection unit can analyze the user's social media activity and filter to collect data of interest. Furthermore, the data collection unit can collect relevant data based on the content of posts from accounts the user follows on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. This makes it possible for the data collection unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data to perform an efficient analysis. For example, the analysis unit can evaluate the importance of the data and determine the priority of the analysis according to its importance. This makes it possible for the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using natural language processing (NLP) for text data. For example, the analysis unit can take text data as input and perform analysis using a natural language processing algorithm. The analysis unit can also perform analysis using image recognition algorithms for image data. For example, the analysis unit can take image data as input and perform analysis using an image recognition algorithm. Furthermore, the analysis unit can also perform analysis using speech recognition algorithms for audio data. For example, the analysis unit can take audio data as input and perform analysis using a speech recognition algorithm. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the most suitable analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide results in real time. For example, the analysis unit can evaluate the data collection timing and prioritize the analysis of the latest data. The analysis unit can also analyze trends and make future predictions based on historical data. For example, the analysis unit can analyze historical data, identify trends, and make future predictions. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing to perform efficient analysis. For example, the analysis unit can evaluate the data collection timing and adjust the priority. This makes it possible for the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal results. For example, the analysis unit can evaluate the relevance of the data and adjust the order of analysis. This makes it possible for the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0049] The evaluation unit can improve the accuracy of its reliability evaluation by considering the interrelationships between data. For example, the evaluation unit can analyze the interrelationships between data to improve the accuracy of the reliability evaluation. For example, the evaluation unit can evaluate the interrelationships between data to improve the accuracy of the reliability evaluation. The evaluation unit can also adjust the reliability evaluation score based on the interrelationships between data. For example, the evaluation unit can evaluate the interrelationships between data and adjust the reliability evaluation score. Furthermore, the evaluation unit can determine the evaluation priority by considering the interrelationships between data. For example, the evaluation unit can evaluate the interrelationships between data and determine the evaluation priority. This makes it possible for the evaluation unit to improve the accuracy of its evaluation by considering the interrelationships between data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0050] The evaluation unit can perform reliability evaluations by considering the attribute information of the data source. For example, the evaluation unit can perform data reliability evaluations based on the reliability of the source. For example, the evaluation unit can evaluate the attribute information of the source and perform data reliability evaluations based on that reliability. The evaluation unit can also perform evaluations by considering the attribute information of the source (e.g., well-known news sites, government agencies). For example, the evaluation unit can evaluate the attribute information of the source and perform reliability evaluations. Furthermore, the evaluation unit can perform current evaluations based on past reliability evaluation results of the source. For example, the evaluation unit can evaluate past reliability evaluation results of the source and perform current evaluations. This makes it possible for the evaluation unit to perform evaluations by considering the attribute information of the data source. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the attribute information of the source into a generating AI and have the generating AI perform the evaluation.

[0051] The evaluation unit can perform reliability evaluations while considering the geographical distribution of the data. For example, the evaluation unit can perform reliability evaluations for each region based on the geographical distribution of the data. For example, the evaluation unit can evaluate the geographical distribution of the data and perform reliability evaluations for each region. The evaluation unit can also adjust the reliability evaluation score of the data while considering the geographical distribution. For example, the evaluation unit can evaluate the geographical distribution of the data and adjust the reliability evaluation score. Furthermore, the evaluation unit can improve the accuracy of the reliability evaluation based on region-specific information. For example, the evaluation unit can evaluate region-specific information and improve the accuracy of the reliability evaluation. This makes it possible for the evaluation unit to perform evaluations while considering the geographical distribution of the data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the evaluation.

[0052] The evaluation unit can improve the accuracy of its reliability evaluation by referring to relevant literature for the data. For example, the evaluation unit performs a reliability evaluation of the data based on relevant literature. For example, the evaluation unit evaluates the relevant literature and performs a reliability evaluation. The evaluation unit can also adjust the evaluation score considering the reliability of the relevant literature. For example, the evaluation unit evaluates the relevant literature and adjusts the reliability evaluation score. Furthermore, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. For example, the evaluation unit evaluates the relevant literature and improves the accuracy of its evaluation. This makes it possible for the evaluation unit to improve the accuracy of its evaluation by referring to relevant literature for the data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the relevant literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0053] The warning unit can optimize the current warning by referring to past warning data when issuing a warning. For example, the warning unit can optimize the current warning content based on past warning data. For example, the warning unit can evaluate past warning data and optimize the current warning content. The warning unit can also determine the priority of warnings by referring to past warning data. For example, the warning unit can evaluate past warning data and determine the priority of warnings. Furthermore, the warning unit can adjust how warnings are displayed based on past warning data. For example, the warning unit can evaluate past warning data and adjust how warnings are displayed. This makes it possible for the warning unit to optimize the current warning based on past warning data. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning data into a generating AI and have the generating AI perform the optimization of warnings.

[0054] The warning unit can apply different warning methods depending on the data category when issuing a warning. For example, for text data, the warning unit can issue a warning using a pop-up notification. For example, the warning unit can evaluate the text data and issue a warning using a pop-up notification. The warning unit can also display a visual warning icon for image data. For example, the warning unit can evaluate the image data and display a visual warning icon. Furthermore, the warning unit can insert a warning message during playback of video data. For example, the warning unit can evaluate the video data and insert a warning message during playback. This allows the warning unit to apply the most appropriate warning method according to the data category. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the data category into a generating AI and have the generating AI apply the warning method.

[0055] The warning unit can analyze changes in warnings based on the data collection timing when issuing a warning. For example, the warning unit can issue warnings in real time based on the latest data. For example, the warning unit evaluates the data collection timing and issues warnings in real time based on the latest data. The warning unit can also analyze trends based on past data and predict future warnings. For example, the warning unit evaluates past data, analyzes trends, and predicts future warnings. Furthermore, the warning unit can adjust the priority of warnings according to the data collection timing. For example, the warning unit evaluates the data collection timing and adjusts the priority of warnings. This makes it possible for the warning unit to analyze changes in warnings based on the data collection timing. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in warnings.

[0056] The warning unit can analyze warnings by referring to relevant market data when issuing a warning. For example, the warning unit can optimize the content of the warning based on the relevant market data. For example, the warning unit can evaluate the relevant market data and optimize the content of the warning. The warning unit can also determine the priority of warnings by referring to the relevant market data. For example, the warning unit can evaluate the relevant market data and determine the priority of warnings. Furthermore, the warning unit can adjust how the warning is displayed based on the relevant market data. For example, the warning unit can evaluate the relevant market data and adjust how the warning is displayed. This enables the warning unit to analyze warnings based on relevant market data. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input relevant market data into a generating AI and have the generating AI perform the warning analysis.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The information reliability evaluation system can analyze a user's past information browsing history and select the optimal method of information delivery. For example, it can prioritize providing information types that the user has frequently viewed in the past. For instance, the system analyzes the user's past information browsing history, identifies frequently viewed information types, and prioritizes providing that information. The system can also identify the most efficient time of day for information delivery based on the user's past information browsing history and deliver information during that time. For example, the system analyzes the user's past information browsing history, identifies the most efficient time of day for delivery, and delivers information during that time. Furthermore, the system can select a delivery method (automatic delivery, manual delivery, etc.) based on the user's past information browsing history. For example, the system analyzes the user's past information browsing history and selects the optimal delivery method. This allows the system to select the optimal information delivery method based on the user's past information browsing history. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the user's past information browsing history into a generating AI, which can then perform the selection of the optimal information delivery method.

[0059] The information reliability evaluation system can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if a user is in a specific region, the system can prioritize the collection of data related to that region. For example, the system can analyze the user's geographical location information and prioritize the collection of data related to that region. The system can also filter and collect region-specific data based on the user's geographical location information. For example, the system can analyze the user's geographical location information and filter out region-specific data. Furthermore, if the user is on the move, the system can collect relevant data in real time based on their current location. For example, the system can analyze the user's geographical location information in real time and collect relevant data based on their current location. This enables the system to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input the user's geographical location information data into a generating AI, and have the generating AI perform the collection of relevant data.

[0060] The information reliability evaluation system can analyze a user's social media activity and collect relevant data during data collection. For example, it can prioritize collecting data related to topics the user has shown interest in on social media. The system can also analyze the user's social media activity and filter to collect data of interest. Furthermore, the system can collect relevant data based on the content of posts from accounts the user follows on social media. The system can analyze the content of posts from accounts the user follows and collect relevant data. This enables the system to collect relevant data based on the user's social media activity. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0061] The information reliability evaluation system can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on data with high importance. For example, the system can evaluate the importance of the data and perform a detailed analysis on the data with high importance. The system can also perform a concise analysis on data with low importance. For example, the system can evaluate the importance of the data and perform a concise analysis on the data with low importance. Furthermore, the system can determine the priority of the analysis according to the importance of the data to perform an efficient analysis. For example, the system can evaluate the importance of the data and determine the priority of the analysis according to its importance. This allows the system to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0062] The information reliability evaluation system can prioritize analysis based on the data collection timing during analysis. For example, it can prioritize the analysis of the most recent data and provide results in real time. For instance, the system evaluates the data collection timing and prioritizes the analysis of the most recent data. The system can also analyze trends and make future predictions based on historical data. For example, the system analyzes historical data, identifies trends, and makes future predictions. Furthermore, the system can adjust the analysis priority according to the data collection timing to perform efficient analysis. For example, the system evaluates the data collection timing and adjusts the priority. This allows the system to determine the analysis priority based on the data collection timing. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the data collection timing into a generating AI and have the generating AI perform the determination of analysis priority.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects data. The collection unit collects multimodal data such as text, images, videos, and audio. The collection unit targets, for example, social media posts, news articles, and digital content. The collection unit can collect data from the internet using, for example, web scraping techniques. The collection unit can also obtain data from specific data sources using APIs. Furthermore, the collection unit can collect data through manual input from users. For example, the collection unit can collect text data and image data provided by users. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the content of the text using, for example, natural language processing (NLP) and extracts keywords, themes, and context. The analysis unit divides the text into words using, for example, morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and clarify relationships such as subject, predicate, and object. Furthermore, the analysis unit can understand the meaning of the text using semantic analysis and extract keywords and themes. The analysis unit extracts relevant data from visual information using image recognition and video analysis. The analysis unit can detect specific objects in an image using, for example, object detection technology. The analysis unit can also identify people in an image using facial recognition technology. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. The analysis unit converts audio data into text and analyzes it using speech recognition technology. The analysis unit can convert audio data into text data using, for example, speech recognition algorithms. Furthermore, the analysis unit can preprocess audio data, performing noise reduction and audio normalization. Step 3: The evaluation unit assesses reliability based on the data analyzed by the analysis unit. The evaluation unit, for example, investigates sources and scores their reliability. The evaluation unit can assess reliability based on sources such as reliable websites, academic papers, and official databases. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. Furthermore, the evaluation unit accepts user feedback and continuously improves the algorithm. The evaluation unit can, for example, collect user opinions and improvement suggestions and adjust the algorithm parameters. Step 4: The warning unit issues early warnings based on the results obtained by the evaluation unit. The warning unit can, for example, issue warnings for areas or topics suspected of being involved in the spread of fake news. The warning unit can send notifications to relevant parties such as social media platforms, local governments, and media organizations. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold.

[0065] (Example of form 2) The information reliability evaluation system according to an embodiment of the present invention is a system that comprehensively verifies the truthfulness of information disseminated through media and social media and calculates a reliability score. This information reliability evaluation system can prevent the spread of large-scale misinformation. The information reliability evaluation system analyzes multimodal data such as text, images, videos, and audio, and determines truthfulness through information source reliability checks, comparison with past data, and cross-referencing methods. Furthermore, the information reliability evaluation system detects the spread of fake news in each region and issues early warnings. For example, the information reliability evaluation system collects multimodal data such as text, images, videos, and audio. For example, social media posts, news articles, and digital content are among the target data. Next, the information reliability evaluation system integrates the collected data to form a multimodal dataset. For example, it combines the text of an article, related images, videos, and audio into a single dataset. Next, the information reliability evaluation system analyzes the content of the text using natural language processing (NLP) and extracts keywords, themes, and context. It also extracts relevant data from visual information using image recognition and video analysis. It converts audio data into text using speech recognition technology and analyzes it similarly. This makes it possible to detect consistency and inconsistencies in information. Furthermore, the information reliability evaluation system investigates sources and scores their reliability to assess the trustworthiness of information sources. This includes cross-referencing with reliable sources such as prominent news sites, government agencies, and official statistical data. The information reliability evaluation system has a user feedback function, which allows for the addition of user-submitted assessments of the accuracy of information and continuous improvement of the algorithm. Finally, based on the evaluation results, the information reliability evaluation system issues early warnings for areas and topics suspected of being prone to the spread of fake news. This is notified to relevant parties such as social media platforms, local governments, and media organizations. This prevents the spread of misinformation and avoids negative impacts on society. In this way, the information reliability evaluation system can accurately determine the trustworthiness of information and prevent the spread of misinformation.

[0066] The information reliability evaluation system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a warning unit. The collection unit collects data. The collection unit collects multimodal data such as text, images, videos, and audio. The collection unit targets, for example, social media posts, news articles, and digital content. The collection unit can collect data from the internet using, for example, web scraping technology. The collection unit can also obtain data from specific data sources using APIs. Furthermore, the collection unit can collect data through manual input from users. For example, the collection unit can collect text data and image data provided by users. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the content of the text using, for example, natural language processing (NLP) and extracts keywords, themes, and context. The analysis unit divides the text into words using, for example, morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and clarify the relationships between subjects, predicates, objects, etc. Furthermore, the analysis unit can understand the meaning of text using semantic analysis and extract keywords and themes. The analysis unit extracts relevant data from visual information using image recognition and video analysis. For example, the analysis unit can detect specific objects in an image using object detection technology. It can also identify people in an image using facial recognition technology. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. The analysis unit converts audio data into text and analyzes it using speech recognition technology. For example, the analysis unit can convert audio data into text data using speech recognition algorithms. It can also preprocess audio data, performing noise reduction and speech normalization. The evaluation unit evaluates reliability based on the data analyzed by the analysis unit. For example, the evaluation unit investigates sources and scores reliability. For example, the evaluation unit can evaluate reliability based on sources such as reliable websites, academic papers, and official databases.Furthermore, the evaluation unit can cross-reference multiple data sources to assess the consistency and accuracy of the data. In addition, the evaluation unit accepts user feedback and continuously improves the algorithm. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust the algorithm parameters. The warning unit issues early warnings based on the results obtained by the evaluation unit. For example, the warning unit can issue warnings for areas or topics suspected of being the source of fake news. The warning unit can send notifications to relevant parties such as social media platforms, local governments, and media organizations. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold. Thus, the information reliability evaluation system according to the embodiment enables data collection, analysis, reliability evaluation, and the issuance of early warnings.

[0067] The data collection unit collects data. For example, the data collection unit collects multimodal data such as text, images, videos, and audio. Specifically, the data collection unit targets social media posts, news articles, and digital content, and can collect data from the internet using web scraping technology. Web scraping technology is a technique that automatically extracts data from specific web pages, and the data collection unit uses this to efficiently collect large amounts of data. The data collection unit can also obtain data from specific data sources using APIs. APIs are interfaces for exchanging data between different software, and the data collection unit can use them to directly obtain data from reliable data sources. Furthermore, the data collection unit can also collect data through manual input from users. For example, the data collection unit can collect text data and image data provided by users. Data manually entered by users is automatically saved to a database by the data collection unit and used for analysis by the subsequent analysis unit. This allows the data collection unit to collect a wide range of data from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and evaluation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses natural language processing (NLP) to analyze the content of text and extract keywords, themes, and context. Specifically, it uses morphological analysis to divide the text into words and identify the part of speech of each word. It can also use grammatical analysis to analyze the structure of sentences and clarify relationships such as subject, predicate, and object. Furthermore, it can use semantic analysis to understand the meaning of the text and extract keywords and themes. For example, by analyzing the text data of a news article and extracting important keywords and themes, the content of the article can be summarized. The analysis unit extracts relevant data from visual information using image recognition and video analysis. For example, it can use object detection technology to detect specific objects in an image. It can also use facial recognition technology to identify people in an image. Furthermore, it can use scene analysis technology to analyze scenes in a video and detect specific events or actions. For example, it can analyze surveillance camera footage and detect suspicious movements. The analysis unit converts audio data into text using speech recognition technology and analyzes it. For example, it can use speech recognition algorithms to convert audio data into text data. Furthermore, the system can preprocess audio data, performing noise reduction and normalization. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The evaluation unit assesses reliability based on data analyzed by the analysis unit. For example, the evaluation unit investigates sources and scores their reliability. Specifically, it can evaluate reliability based on sources such as reliable websites, academic papers, and official databases. For instance, if a news article's source is a highly reliable news organization, the article's reliability will be highly rated. Conversely, if the source is unclear or from an unreliable website, the article's reliability will be low. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. For example, if the same news is reported by multiple reliable sources, the news's reliability will be highly rated. Furthermore, the evaluation unit accepts user feedback and continuously improves the algorithm. For example, it can collect user opinions and improvement suggestions and adjust algorithm parameters. This allows the evaluation unit to always provide highly accurate reliability assessments based on the latest information. Additionally, the evaluation unit can verify the accuracy of the evaluation algorithm based on past evaluation results and make improvements as needed. For example, it can compare past evaluation results with actual reliability to verify the accuracy of the evaluation algorithm. Furthermore, the evaluation unit can visualize the evaluation results and provide them to users in an easy-to-understand manner. For example, it can display confidence scores in graphs and charts, allowing users to intuitively understand them. This enables the evaluation unit to provide users with highly reliable information and improve the reliability of the data.

[0070] The warning unit issues early warnings based on the results obtained by the evaluation unit. For example, the warning unit can issue warnings for areas or topics suspected of being the source of fake news. Specifically, it can send notifications to relevant parties such as social media platforms, local governments, and media organizations. For example, it can issue warnings to social media platforms when there is a surge in posts related to specific keywords or themes, prompting them to take measures to prevent the spread of fake news. It can also issue warnings to local governments when fake news is spreading in a particular area, urging them to provide accurate information to residents. Furthermore, it can issue warnings to media organizations regarding topics suspected of being the source of fake news, urging them to provide accurate reporting. The warning unit can also set the content and conditions for issuing warnings. For example, it can issue a warning when information related to specific keywords or themes exceeds a certain threshold. This allows the warning unit to issue warnings quickly and appropriately, preventing the spread of fake news. In addition, the warning unit can record the history of warnings issued for later reference. For example, by recording the content and conditions of past warnings and analyzing them later, the accuracy and effectiveness of the warnings can be verified. Furthermore, the warning unit can receive feedback from users and continuously improve the content and conditions of the warnings. This allows the warning unit to always provide highly accurate warnings based on the latest information, preventing the spread of fake news.

[0071] The data collection unit can collect multimodal data such as text, images, videos, and audio. For example, the data collection unit can use web scraping techniques to collect text data. For instance, the data collection unit can automatically extract text data from a specific website and store it in a database. The data collection unit can also use image recognition techniques to collect image data. For example, the data collection unit can automatically collect images from the internet and store them in an image database. Furthermore, the data collection unit can use video analysis techniques to collect video data. For example, the data collection unit can collect video data from video sharing sites and store it in a video database. The data collection unit can also use speech recognition techniques to collect audio data. For example, the data collection unit can collect audio data from podcasts and audio media and store it in an audio database. This enables the data collection unit to collect a variety of data formats. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data to be collected into a generating AI and have the generating AI perform the data collection.

[0072] The analysis unit can analyze the content of text using natural language processing and extract keywords, themes, and context. For example, the analysis unit can divide the text into words using morphological analysis and identify the part of speech of each word. For example, the analysis unit takes text data as input and uses a morphological analysis algorithm to identify the part of speech of each word. The analysis unit can also analyze the structure of a sentence using grammatical analysis and clarify the relationships between subjects, predicates, objects, etc. For example, the analysis unit uses a grammatical analysis algorithm to analyze the structure of a sentence and identify the relationships between the elements of the sentence. Furthermore, the analysis unit can understand the meaning of the text using semantic analysis and extract keywords and themes. For example, the analysis unit uses a semantic analysis algorithm to analyze the meaning of the text and extract important keywords and themes. This allows the analysis unit to analyze the content of the text data in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI perform the extraction of keywords, themes, and context.

[0073] The analysis unit can extract relevant data from visual information using image recognition and video analysis. For example, the analysis unit can detect specific objects in an image using object detection technology. For instance, it can take image data as input and use an object detection algorithm to identify objects in the image. The analysis unit can also identify people in an image using face recognition technology. For example, it can use a face recognition algorithm to identify people in an image and detect specific individuals. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. For example, it can take video data as input and use a scene analysis algorithm to identify events in the video. This enables the analysis unit to extract relevant data from visual information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input image data or video data into a generating AI and have the generating AI perform the extraction of relevant data.

[0074] The analysis unit can convert audio data into text using speech recognition technology and then analyze it. For example, the analysis unit can convert audio data into text data using a speech recognition algorithm. For instance, the analysis unit takes audio data as input and converts the audio to text using a speech recognition algorithm. The analysis unit can also preprocess the audio data, performing noise reduction and speech normalization. For example, the analysis unit can remove noise from the audio data to improve the audio quality. Furthermore, the analysis unit can analyze the transcribed audio data and extract keywords and themes. For example, the analysis unit takes text data as input and extracts keywords and themes using natural language processing technology. This enables the analysis unit to convert audio data into text and analyze it. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input audio data into a generating AI and have the generating AI perform the transcription and analysis of the audio data.

[0075] The evaluation unit can investigate sources and score their reliability. For example, the evaluation unit can assess reliability based on sources such as highly reliable websites, academic papers, and official databases. For instance, to evaluate the reliability of a source, the evaluation unit can refer to the source's past performance and reliability evaluation results. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. For example, the evaluation unit can compare information from multiple data sources and highly value consistent information. Furthermore, the evaluation unit can accept user feedback and continuously improve its algorithms. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust algorithm parameters. This enables the evaluation unit to assess the reliability of sources. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input source data into a generating AI and have the generating AI perform reliability scoring.

[0076] The evaluation unit can receive feedback from users and continuously improve the algorithm. For example, the evaluation unit can collect user opinions and improvement suggestions and adjust the algorithm parameters. For example, the evaluation unit can make adjustments to improve the accuracy of the algorithm based on user feedback. The evaluation unit can also analyze user feedback and identify areas for improvement in the algorithm. For example, the evaluation unit can analyze user feedback and identify weaknesses and areas for improvement in the algorithm. Furthermore, the evaluation unit can develop new algorithms based on user feedback. For example, the evaluation unit can design and implement a new evaluation algorithm based on user feedback. This makes it possible for the evaluation unit to improve the algorithm based on user feedback. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback data into a generating AI and have the generating AI perform improvements to the algorithm.

[0077] The warning unit can issue early warnings for areas or topics suspected of being affected by the spread of fake news. The warning unit can send notifications to relevant parties, such as social media platforms, local governments, and news organizations. For example, the warning unit can collect information on areas or topics suspected of being affected by fake news and issue warnings to relevant parties. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold. Furthermore, the warning unit can select the method of issuing warnings. For example, the warning unit can issue warnings via email, SMS, or push notifications. This enables the warning unit to provide early warnings about the spread of fake news. Some or all of the above-described processes in the warning unit may be performed using AI, or not. For example, the warning unit can input fake news spread data into a generating AI, which can then execute the early warning process.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For instance, the data collection unit analyzes the user's emotional data and, if it determines the user is stressed, reduces the frequency of data collection. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection and gather more detailed information. For example, if the data collection unit analyzes the user's emotional data and determines the user is relaxed, it increases the frequency of data collection. Furthermore, if the user is excited, the data collection unit can collect data in real time and perform immediate analysis. For example, if the data collection unit analyzes the user's emotional data and determines the user is excited, it collects data in real time. This allows the data collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then adjust the timing of data collection.

[0079] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data types that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past data collection history, identify frequently collected data types, and prioritize collecting that data. The data collection unit can also identify the most efficient collection time from the user's past data collection history and perform collection during that time. For example, the data collection unit can analyze the user's past data collection history, identify the most efficient collection time, and perform data collection during that time. Furthermore, the data collection unit can select a collection method (automatic collection, manual collection, etc.) based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the optimal collection method. This makes it possible for the data collection unit to select the optimal collection method based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can analyze the user's current areas of interest and prioritize collecting relevant data. The data collection unit can also filter out unnecessary data based on the user's current areas of interest to ensure efficient collection. For example, the data collection unit can analyze the user's areas of interest and filter out unnecessary data. Furthermore, if the user's areas of interest change, the data collection unit can update the collection targets in real time to accommodate the latest areas of interest. For example, the data collection unit can detect changes in the user's areas of interest and update the collection targets in real time. This allows the data collection unit to filter data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform data filtering.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the data collection unit analyzes the user's emotional data and determines that the user is stressed, it will prioritize collecting high-priority data. The data collection unit can also prioritize collecting detailed data if the user is relaxed. For example, if the data collection unit analyzes the user's emotional data and determines that the user is relaxed, it will prioritize collecting detailed data. Furthermore, if the user is excited, the data collection unit can prioritize collecting important data in real time. For example, if the data collection unit analyzes the user's emotional data and determines that the user is excited, it will prioritize collecting important data in real time. This allows the data collection unit to prioritize the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform the task of determining the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will analyze the user's geographical location information and prioritize the collection of data related to that region. The data collection unit can also filter and collect region-specific data based on the user's geographical location information. For example, the data collection unit will analyze the user's geographical location information and filter out region-specific data. Furthermore, if the user is on the move, the data collection unit can collect relevant data in real time based on their current location. For example, the data collection unit will analyze the user's geographical location information in real time and collect relevant data based on their current location. This enables the data collection unit to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of relevant data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity and prioritize collecting data related to topics the user has shown interest in. The data collection unit can also analyze the user's social media activity and filter to collect data of interest. For example, the data collection unit can analyze the user's social media activity and filter to collect data of interest. Furthermore, the data collection unit can collect relevant data based on the content of posts from accounts the user follows on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. This makes it possible for the data collection unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the analysis unit analyzes the user's emotional data and determines that the user is nervous, it can provide a simple and easy-to-understand analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit analyzes the user's emotional data and determines that the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. For example, if the analysis unit analyzes the user's emotional data and determines that the user is in a hurry, it can provide a concise analysis result that gets straight to the point. This allows the analysis unit to adjust the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the way the analysis is expressed.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data to perform an efficient analysis. For example, the analysis unit can evaluate the importance of the data and determine the priority of the analysis according to its importance. This makes it possible for the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using natural language processing (NLP) for text data. For example, the analysis unit can take text data as input and perform analysis using a natural language processing algorithm. The analysis unit can also perform analysis using image recognition algorithms for image data. For example, the analysis unit can take image data as input and perform analysis using an image recognition algorithm. Furthermore, the analysis unit can also perform analysis using speech recognition algorithms for audio data. For example, the analysis unit can take audio data as input and perform analysis using a speech recognition algorithm. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the most suitable analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the analysis unit analyzes the user's emotional data and determines that the user is in a hurry, it can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit analyzes the user's emotional data and determines that the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the analysis unit analyzes the user's emotional data and determines that the user is excited, it can provide a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide results in real time. For example, the analysis unit can evaluate the data collection timing and prioritize the analysis of the latest data. The analysis unit can also analyze trends and make future predictions based on historical data. For example, the analysis unit can analyze historical data, identify trends, and make future predictions. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing to perform efficient analysis. For example, the analysis unit can evaluate the data collection timing and adjust the priority. This makes it possible for the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide efficient results. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal results. For example, the analysis unit can evaluate the relevance of the data and adjust the order of analysis. This makes it possible for the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0090] The evaluation unit can estimate the user's emotions and adjust the reliability evaluation criteria based on the estimated user emotions. For example, if the user is tense, the evaluation unit can perform a reliability evaluation using strict criteria. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is tense, it can perform a reliability evaluation using strict criteria. The evaluation unit can also perform a reliability evaluation using flexible criteria if the user is relaxed. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is relaxed, it can perform a reliability evaluation using flexible criteria. Furthermore, if the user is in a hurry, the evaluation unit can perform a reliability evaluation quickly. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is in a hurry, it can perform a reliability evaluation quickly. This makes it possible for the evaluation unit to adjust the reliability evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI, which can then adjust the reliability evaluation criteria.

[0091] The evaluation unit can improve the accuracy of its reliability evaluation by considering the interrelationships between data. For example, the evaluation unit can analyze the interrelationships between data to improve the accuracy of the reliability evaluation. For example, the evaluation unit can evaluate the interrelationships between data to improve the accuracy of the reliability evaluation. The evaluation unit can also adjust the reliability evaluation score based on the interrelationships between data. For example, the evaluation unit can evaluate the interrelationships between data and adjust the reliability evaluation score. Furthermore, the evaluation unit can determine the evaluation priority by considering the interrelationships between data. For example, the evaluation unit can evaluate the interrelationships between data and determine the evaluation priority. This makes it possible for the evaluation unit to improve the accuracy of its evaluation by considering the interrelationships between data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0092] The evaluation unit can perform reliability evaluations by considering the attribute information of the data source. For example, the evaluation unit can perform data reliability evaluations based on the reliability of the source. For example, the evaluation unit can evaluate the attribute information of the source and perform data reliability evaluations based on that reliability. The evaluation unit can also perform evaluations by considering the attribute information of the source (e.g., well-known news sites, government agencies). For example, the evaluation unit can evaluate the attribute information of the source and perform reliability evaluations. Furthermore, the evaluation unit can perform current evaluations based on past reliability evaluation results of the source. For example, the evaluation unit can evaluate past reliability evaluation results of the source and perform current evaluations. This makes it possible for the evaluation unit to perform evaluations by considering the attribute information of the data source. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the attribute information of the source into a generating AI and have the generating AI perform the evaluation.

[0093] The evaluation unit can estimate the user's emotions and adjust the order in which it displays the reliability evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can prioritize displaying important evaluation results. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is nervous, it will prioritize displaying important evaluation results. The evaluation unit can also display detailed evaluation results if the user is relaxed. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is relaxed, it will display detailed evaluation results. Furthermore, if the evaluation unit is in a hurry, it can prioritize displaying concise evaluation results. For example, if the evaluation unit analyzes the user's emotional data and determines that the user is in a hurry, it will prioritize displaying concise evaluation results. This allows the evaluation unit to adjust the order in which it displays the reliability evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user sentiment data into a generating AI, which can then adjust the display order of the reliability evaluation results.

[0094] The evaluation unit can perform reliability evaluations while considering the geographical distribution of the data. For example, the evaluation unit can perform reliability evaluations for each region based on the geographical distribution of the data. For example, the evaluation unit can evaluate the geographical distribution of the data and perform reliability evaluations for each region. The evaluation unit can also adjust the reliability evaluation score of the data while considering the geographical distribution. For example, the evaluation unit can evaluate the geographical distribution of the data and adjust the reliability evaluation score. Furthermore, the evaluation unit can improve the accuracy of the reliability evaluation based on region-specific information. For example, the evaluation unit can evaluate region-specific information and improve the accuracy of the reliability evaluation. This makes it possible for the evaluation unit to perform evaluations while considering the geographical distribution of the data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the evaluation.

[0095] The evaluation unit can improve the accuracy of its reliability evaluation by referring to relevant literature for the data. For example, the evaluation unit performs a reliability evaluation of the data based on relevant literature. For example, the evaluation unit evaluates the relevant literature and performs a reliability evaluation. The evaluation unit can also adjust the evaluation score considering the reliability of the relevant literature. For example, the evaluation unit evaluates the relevant literature and adjusts the reliability evaluation score. Furthermore, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. For example, the evaluation unit evaluates the relevant literature and improves the accuracy of its evaluation. This makes it possible for the evaluation unit to improve the accuracy of its evaluation by referring to relevant literature for the data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the relevant literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0096] The warning unit can estimate the user's emotions and adjust how warnings are displayed based on the estimated emotions. For example, if the user is tense, the warning unit can display a simple and highly visible warning. For example, if the warning unit analyzes the user's emotional data and determines that the user is tense, it can display a simple and highly visible warning. The warning unit can also display detailed warning information if the user is relaxed. For example, if the warning unit analyzes the user's emotional data and determines that the user is relaxed, it can display detailed warning information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning. For example, if the warning unit analyzes the user's emotional data and determines that the user is in a hurry, it can display a concise warning. This allows the warning unit to adjust how warnings are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input user emotion data into a generating AI, which can then adjust how the warning is displayed.

[0097] The warning unit can optimize the current warning by referring to past warning data when issuing a warning. For example, the warning unit can optimize the current warning content based on past warning data. For example, the warning unit can evaluate past warning data and optimize the current warning content. The warning unit can also determine the priority of warnings by referring to past warning data. For example, the warning unit can evaluate past warning data and determine the priority of warnings. Furthermore, the warning unit can adjust how warnings are displayed based on past warning data. For example, the warning unit can evaluate past warning data and adjust how warnings are displayed. This makes it possible for the warning unit to optimize the current warning based on past warning data. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning data into a generating AI and have the generating AI perform the optimization of warnings.

[0098] The warning unit can apply different warning methods depending on the data category when issuing a warning. For example, for text data, the warning unit can issue a warning using a pop-up notification. For example, the warning unit can evaluate the text data and issue a warning using a pop-up notification. The warning unit can also display a visual warning icon for image data. For example, the warning unit can evaluate the image data and display a visual warning icon. Furthermore, the warning unit can insert a warning message during playback of video data. For example, the warning unit can evaluate the video data and insert a warning message during playback. This allows the warning unit to apply the most appropriate warning method according to the data category. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the data category into a generating AI and have the generating AI apply the warning method.

[0099] The warning system can estimate the user's emotions and adjust the importance of warnings based on those emotions. For example, if the user is stressed, the warning system will prioritize displaying high-priority warnings. For instance, it can analyze the user's emotional data and, if it determines the user is stressed, prioritize displaying high-priority warnings. The warning system can also display detailed warning information if the user is relaxed. For example, it can analyze the user's emotional data and, if it determines the user is relaxed, display detailed warning information. Furthermore, if the user is in a hurry, the warning system can display concise warnings. For example, it can analyze the user's emotional data and, if it determines the user is in a hurry, display concise warnings. This allows the warning system to adjust the importance of warnings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input user emotion data into a generating AI, which can then adjust the importance of the warning.

[0100] The warning unit can analyze changes in warnings based on the data collection timing when issuing a warning. For example, the warning unit can issue warnings in real time based on the latest data. For example, the warning unit evaluates the data collection timing and issues warnings in real time based on the latest data. The warning unit can also analyze trends based on past data and predict future warnings. For example, the warning unit evaluates past data, analyzes trends, and predicts future warnings. Furthermore, the warning unit can adjust the priority of warnings according to the data collection timing. For example, the warning unit evaluates the data collection timing and adjusts the priority of warnings. This makes it possible for the warning unit to analyze changes in warnings based on the data collection timing. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in warnings.

[0101] The warning unit can analyze warnings by referring to relevant market data when issuing a warning. For example, the warning unit can optimize the content of the warning based on the relevant market data. For example, the warning unit can evaluate the relevant market data and optimize the content of the warning. The warning unit can also determine the priority of warnings by referring to the relevant market data. For example, the warning unit can evaluate the relevant market data and determine the priority of warnings. Furthermore, the warning unit can adjust how the warning is displayed based on the relevant market data. For example, the warning unit can evaluate the relevant market data and adjust how the warning is displayed. This enables the warning unit to analyze warnings based on relevant market data. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input relevant market data into a generating AI and have the generating AI perform the warning analysis.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The information reliability evaluation system can estimate the user's emotions and adjust the reliability evaluation of the information based on those emotions. For example, if the user is feeling anxious, the system can evaluate the reliability of the information using stricter criteria to provide the user with a sense of security. If the user is relaxed, the system can evaluate using more flexible criteria and provide the user with a variety of information. Furthermore, if the user is in a hurry, the system can perform a rapid evaluation and prioritize the provision of important information. This allows the information reliability evaluation system to adjust the reliability evaluation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reliability evaluation.

[0104] The information reliability evaluation system can analyze a user's past information browsing history and select the optimal method of information delivery. For example, it can prioritize providing information types that the user has frequently viewed in the past. For instance, the system analyzes the user's past information browsing history, identifies frequently viewed information types, and prioritizes providing that information. The system can also identify the most efficient time of day for information delivery based on the user's past information browsing history and deliver information during that time. For example, the system analyzes the user's past information browsing history, identifies the most efficient time of day for delivery, and delivers information during that time. Furthermore, the system can select a delivery method (automatic delivery, manual delivery, etc.) based on the user's past information browsing history. For example, the system analyzes the user's past information browsing history and selects the optimal delivery method. This allows the system to select the optimal information delivery method based on the user's past information browsing history. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the user's past information browsing history into a generating AI, which can then perform the selection of the optimal information delivery method.

[0105] The information reliability evaluation system can estimate the user's emotions and adjust how information is displayed based on the estimated emotions. For example, if the user is nervous, the system can display simple and highly visible information. For example, the system can analyze the user's emotional data and, if it determines that the user is nervous, display simple and highly visible information. Also, if the user is relaxed, the system can display detailed information. For example, the system can analyze the user's emotional data and, if it determines that the user is relaxed, display detailed information. Furthermore, if the user is in a hurry, the system can display concise information that gets straight to the point. For example, the system can analyze the user's emotional data and, if it determines that the user is in a hurry, display concise information that gets straight to the point. This allows the system to adjust how information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input user emotion data into a generating AI, which can then adjust how the information is displayed.

[0106] The information reliability evaluation system can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if a user is in a specific region, the system can prioritize the collection of data related to that region. For example, the system can analyze the user's geographical location information and prioritize the collection of data related to that region. The system can also filter and collect region-specific data based on the user's geographical location information. For example, the system can analyze the user's geographical location information and filter out region-specific data. Furthermore, if the user is on the move, the system can collect relevant data in real time based on their current location. For example, the system can analyze the user's geographical location information in real time and collect relevant data based on their current location. This enables the system to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input the user's geographical location information data into a generating AI, and have the generating AI perform the collection of relevant data.

[0107] The information reliability evaluation system can estimate the user's emotions and adjust the way warnings are displayed based on the estimated emotions. For example, if the user is nervous, the system can display a simple and highly visible warning. For example, the system can analyze the user's emotional data and, if it determines that the user is nervous, display a simple and highly visible warning. Also, if the user is relaxed, the system can display detailed warning information. For example, the system can analyze the user's emotional data and, if it determines that the user is relaxed, display detailed warning information. Furthermore, if the user is in a hurry, the system can display a concise warning. For example, the system can analyze the user's emotional data and, if it determines that the user is in a hurry, display a concise warning. This allows the system to adjust how warnings are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input user emotion data into a generating AI, which can then adjust how warnings are displayed.

[0108] The information reliability evaluation system can analyze a user's social media activity and collect relevant data during data collection. For example, it can prioritize collecting data related to topics the user has shown interest in on social media. The system can also analyze the user's social media activity and filter to collect data of interest. Furthermore, the system can collect relevant data based on the content of posts from accounts the user follows on social media. The system can analyze the content of posts from accounts the user follows and collect relevant data. This enables the system to collect relevant data based on the user's social media activity. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0109] The information reliability evaluation system can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the system can provide a simple and easy-to-understand analysis result. For example, if the system analyzes the user's emotional data and determines that the user is nervous, it can provide a simple and easy-to-understand analysis result. Also, if the user is relaxed, the system can provide a detailed analysis result. For example, if the system analyzes the user's emotional data and determines that the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, the system can provide a concise analysis result that gets straight to the point. For example, if the system analyzes the user's emotional data and determines that the user is in a hurry, it can provide a concise analysis result that gets straight to the point. This allows the system to adjust the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI, for example, or not using AI. For example, the system can input user emotion data into a generating AI, which can then adjust the way the analysis is presented.

[0110] The information reliability evaluation system can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on data with high importance. For example, the system can evaluate the importance of the data and perform a detailed analysis on the data with high importance. The system can also perform a concise analysis on data with low importance. For example, the system can evaluate the importance of the data and perform a concise analysis on the data with low importance. Furthermore, the system can determine the priority of the analysis according to the importance of the data to perform an efficient analysis. For example, the system can evaluate the importance of the data and determine the priority of the analysis according to its importance. This allows the system to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0111] The information reliability evaluation system can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. For example, the system analyzes the user's emotional data and, if it determines that the user is in a hurry, provides a short, concise analysis. It can also provide a detailed analysis if the user is relaxed. For example, the system analyzes the user's emotional data and, if it determines that the user is relaxed, provides a detailed analysis. Furthermore, if the user is excited, it can provide a visually stimulating analysis. For example, the system analyzes the user's emotional data and, if it determines that the user is excited, provides a visually stimulating analysis. This allows the system to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI, for example, or without AI. For example, the system can input user emotion data into a generating AI, which can then adjust the length of the analysis.

[0112] The information reliability evaluation system can prioritize analysis based on the data collection timing during analysis. For example, it can prioritize the analysis of the most recent data and provide results in real time. For instance, the system evaluates the data collection timing and prioritizes the analysis of the most recent data. The system can also analyze trends and make future predictions based on historical data. For example, the system analyzes historical data, identifies trends, and makes future predictions. Furthermore, the system can adjust the analysis priority according to the data collection timing to perform efficient analysis. For example, the system evaluates the data collection timing and adjusts the priority. This allows the system to determine the analysis priority based on the data collection timing. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the data collection timing into a generating AI and have the generating AI perform the determination of analysis priority.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects data. The collection unit collects multimodal data such as text, images, videos, and audio. The collection unit targets, for example, social media posts, news articles, and digital content. The collection unit can collect data from the internet using, for example, web scraping techniques. The collection unit can also obtain data from specific data sources using APIs. Furthermore, the collection unit can collect data through manual input from users. For example, the collection unit can collect text data and image data provided by users. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the content of the text using, for example, natural language processing (NLP) and extracts keywords, themes, and context. The analysis unit divides the text into words using, for example, morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and clarify relationships such as subject, predicate, and object. Furthermore, the analysis unit can understand the meaning of the text using semantic analysis and extract keywords and themes. The analysis unit extracts relevant data from visual information using image recognition and video analysis. The analysis unit can detect specific objects in an image using, for example, object detection technology. The analysis unit can also identify people in an image using facial recognition technology. Furthermore, the analysis unit can analyze scenes in a video using scene analysis technology and detect specific events or actions. The analysis unit converts audio data into text and analyzes it using speech recognition technology. The analysis unit can convert audio data into text data using, for example, speech recognition algorithms. Furthermore, the analysis unit can preprocess audio data, performing noise reduction and audio normalization. Step 3: The evaluation unit assesses reliability based on the data analyzed by the analysis unit. The evaluation unit, for example, investigates sources and scores their reliability. The evaluation unit can assess reliability based on sources such as reliable websites, academic papers, and official databases. The evaluation unit can also cross-reference multiple data sources to assess data consistency and accuracy. Furthermore, the evaluation unit accepts user feedback and continuously improves the algorithm. The evaluation unit can, for example, collect user opinions and improvement suggestions and adjust the algorithm parameters. Step 4: The warning unit issues early warnings based on the results obtained by the evaluation unit. The warning unit can, for example, issue warnings for areas or topics suspected of being involved in the spread of fake news. The warning unit can send notifications to relevant parties such as social media platforms, local governments, and media organizations. The warning unit can also set the content and conditions for issuing warnings. For example, the warning unit can issue a warning when information related to specific keywords or themes exceeds a certain threshold.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data using web scraping technology or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using natural language processing or image recognition technology. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs reliability scoring. The warning unit is implemented by the control unit 46A of the smart device 14 and issues a warning when the spread of fake news is suspected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data using web scraping technology or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using natural language processing or image recognition technology. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs reliability scoring. The warning unit is implemented by the control unit 46A of the smart glasses 214 and issues a warning when the spread of fake news is suspected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data using web scraping technology or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using natural language processing or image recognition technology. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs reliability scoring. The warning unit is implemented by the control unit 46A of the headset terminal 314 and issues a warning when the spread of fake news is suspected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and warning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data using web scraping technology or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using natural language processing or image recognition technology. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs reliability scoring. The warning unit is implemented by the control unit 46A of the robot 414 and issues a warning when the spread of fake news is suspected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates reliability based on the data analyzed by the analysis unit, The system includes a warning unit that issues an early warning based on the results obtained by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect multimodal data such as text, images, videos, and audio. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the content of text using natural language processing and extract keywords, themes, and context. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Extract relevant data from visual information using image recognition and video analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Speech recognition technology is used to convert speech data into text and then analyze it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit described above, Investigate the source and score its reliability. The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit described above, We accept user feedback and continuously improve our algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned warning unit is We issue early warnings about areas and topics where the spread of fake news is suspected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit described above, We estimate user sentiment and adjust the reliability evaluation criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit described above, When evaluating reliability, consider the interrelationships between data to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit described above, When evaluating reliability, the attribute information of the data source should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit described above, It estimates the user's sentiment and adjusts the order in which the reliability evaluation results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit described above, When evaluating reliability, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, When evaluating reliability, referencing relevant literature on the data improves the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warning unit is It estimates the user's emotions and adjusts how warnings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned warning unit is When issuing a warning, the system optimizes the current warning by referencing past warning data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned warning unit is When issuing a warning, different warning methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warning unit is It estimates the user's emotions and adjusts the importance of the warning based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned warning unit is When an alert is issued, the system analyzes changes in the alert based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned warning unit is When an alert is issued, the relevant market data is referenced to analyze the alert. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates reliability based on the data analyzed by the analysis unit, The system includes a warning unit that issues an early warning based on the results obtained by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect multimodal data such as text, images, videos, and audio. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the content of text using natural language processing and extract keywords, themes, and context. The system according to feature 1.

4. The aforementioned analysis unit, Extract relevant data from visual information using image recognition and video analysis. The system according to feature 1.

5. The aforementioned analysis unit, Speech recognition technology is used to convert speech data into text and then analyze it. The system according to feature 1.

6. The evaluation unit, Investigate the source and score its reliability. The system according to feature 1.

7. The evaluation unit, We accept user feedback and continuously improve our algorithms. The system according to feature 1.

8. The aforementioned warning unit is We issue early warnings about areas and topics where the spread of fake news is suspected. The system according to feature 1.

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