system

Abstract

The system according to this embodiment aims to efficiently identify fake data generated by a generative AI. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a fact-checking unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit and determines whether or not it is fake data. The fact-checking unit performs fact-checking based on the results analyzed by the analysis unit. The provision unit provides the results obtained by the fact-checking unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 was a problem that it was difficult to efficiently identify fake data generated by generative AI.

[0005] The system according to the embodiment aims to efficiently identify fake data generated by generative AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a fact-checking unit, and a data provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit and determines whether or not it is fake data. The fact-checking unit performs fact-checking based on the results of the analysis by the analysis unit. The data provision unit provides the results obtained by the fact-checking unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently identify fake data generated by a generation AI. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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) An AI fake detection service according to an embodiment of the present invention is a tool for identifying and determining fake data generated using generative AI. This AI fake detection service collects data such as images, videos, audio, and text, and the AI ​​analyzes it to determine whether it is fake data. Furthermore, for text information, it performs fact-checking to clarify its authenticity by referring to specific databases and information on the web. The analysis results are provided to the user. This service can address issues such as the loss of reliability of fake data generated by generative AI, defamation, security risks, social disruption, ethical problems, and interference with education. For example, the AI ​​fake detection service collects data such as images, videos, audio, and text from online platforms. The collected data is analyzed by the AI ​​to determine whether it is fake data. For example, the AI ​​uses an image analysis algorithm to analyze image data and detect fake images. The AI ​​also uses an audio analysis algorithm to analyze audio data and detect fake audio. Furthermore, the AI ​​uses a text analysis algorithm to analyze text data and detect fake text. Next, the AI ​​fake detection service performs fact-checking on the collected text information. For example, AI refers to specific databases and web information to determine the truthfulness of text information. AI compares text information with databases to check for matching information. AI also refers to reliable sources on the web to verify the truthfulness of text information. Finally, the AI ​​fake detection service provides the user with the analysis results. For example, if the AI ​​determines that the data is fake, it provides the reasons and evidence. The AI ​​explains the source and inconsistencies of the fake data to the user. This allows the AI ​​fake detection service to identify and determine fake data generated by generative AI.

[0029] The AI ​​fake detection service according to this embodiment comprises a collection unit, an analysis unit, a fact-checking unit, and a provision unit. The collection unit collects data. The collection unit collects data such as images, videos, audio, and text from online platforms, for example. The collection unit collects image data from social media, for example. The collection unit can also collect text data from news sites. Furthermore, the collection unit can also collect audio data from audio platforms. For example, the collection unit collects image data using social media APIs. The collection unit can also collect text data using news site RSS feeds. The collection unit can also collect audio data using audio platform APIs. The analysis unit analyzes the data collected by the collection unit and determines whether or not it is fake data. The analysis unit analyzes image data using, for example, an image analysis algorithm. The analysis unit can also analyze audio data using, for example, an audio analysis algorithm. The analysis unit can also analyze text data using, for example, a text analysis algorithm. For example, the analysis unit performs analysis using an AI model that takes image data as input and outputs fake images. The analysis unit can also perform analysis using an AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using an AI model that takes text data as input and outputs fake text. The fact-checking unit performs fact-checking based on the results analyzed by the analysis unit. The fact-checking unit verifies the truthfulness of text information by referring to specific databases or information on the web, for example. The fact-checking unit can refer to reliable news sites, for example. The fact-checking unit can also refer to academic databases, for example. The fact-checking unit compares text information with databases to check for any matching information. The fact-checking unit can also verify the truthfulness of text information by referring to reliable sources on the web. The provisioning unit provides the results obtained by the fact-checking unit to the user.The service provider, for example, provides the user with the analysis results and, if the data is determined to be fake, presents the reasons and evidence. The service provider, for example, explains to the user the source and inconsistencies of the fake data. The service provider, for example, displays the analysis results to the user through a web application or mobile application. The service provider can also, for example, send the analysis results to the user via email. The service provider can also, for example, print the analysis results using a printer and provide them to the user on paper. In this way, the AI ​​fake detection service according to the embodiment can identify and determine fake data generated by the generating AI.

[0030] The data collection unit collects data. For example, it collects data such as images, videos, audio, and text from online platforms. Specifically, the data collection unit uses social media APIs to collect image data. For example, it can obtain images and videos posted by users from social media platforms in real time. This allows the data collection unit to quickly collect data on the latest trends and topics. The data collection unit can also collect text data using RSS feeds from news sites. For example, it can automatically retrieve the latest articles and posts from major news sites and blogs and save them as text data. Furthermore, the data collection unit can collect audio data using audio platform APIs. For example, it can obtain audio data from podcasts and voice messaging services and save it for analysis. This allows the data collection unit to collect a wide range of data from diverse data sources and comprehensively gather the information necessary for detecting fake data. The data collection unit centrally manages this data and makes it accessible to the analysis and fact-checking units. For example, the collected data is stored in a cloud-based database and updated in real time as needed. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to determine whether or not it is fake data. For example, the analysis unit analyzes image data using an image analysis algorithm. Specifically, it utilizes image recognition technology using deep learning to extract image features and detect fake images. For example, images generated by generative AI often contain specific patterns or noise, and by detecting these, fake images can be identified. The analysis unit can also analyze audio data using an audio analysis algorithm. For example, it analyzes the waveform and spectrum of audio to detect features unique to audio generated by generative AI. This allows for highly accurate detection of fake audio. The analysis unit can also analyze text data using a text analysis algorithm. For example, it uses natural language processing technology to analyze the context and grammar of text and detect patterns unique to text generated by generative AI. This allows for highly accurate detection of fake text. Furthermore, the analysis unit can integrate these analysis results to perform a comprehensive fake data determination. For example, it analyzes image, audio, and text data and combines the results to make a final determination. This allows the analysis unit to comprehensively analyze information obtained from multiple data sources and improve the accuracy of fake data detection.

[0032] The fact-checking department performs fact-checking based on the results analyzed by the analysis department. For example, the fact-checking department refers to specific databases and web-based information to determine the veracity of textual information. Specifically, it refers to reliable news sites and academic databases and compares them with the analysis results. For instance, to verify the accuracy of a news article, it refers to multiple reliable news sources to check for matching information. It can also refer to academic databases to verify the accuracy of specific facts and data. The fact-checking department can also refer to reliable web-based sources to verify the veracity of textual information. For example, it refers to the websites of government agencies and public institutions to compare them with official information. This allows the fact-checking department to enhance the reliability of its analysis results and provide users with accurate information. Furthermore, based on the analysis results, the fact-checking department can identify the sources and inconsistencies of fake data, clarifying the basis for the information provided to users. This enhances the reliability of its analysis results and allows the fact-checking department to provide users with accurate and reliable information.

[0033] The service provider will provide users with the results obtained by the fact-checking service provider. Specifically, they will provide users with analysis results and, if data is determined to be fake, will provide the reasons and evidence. For example, they will explain to users the source and inconsistencies of the fake data and provide a detailed explanation of why the data was determined to be fake. The service provider can display analysis results to users through web applications and mobile applications. For example, they can provide a dashboard that users can access and display analysis results and fact-check results in real time. The service provider can also send analysis results to users via email. For example, they can periodically send users reports summarizing the analysis results and reporting the latest fake data detection status. Furthermore, the service provider can print the analysis results and provide them to users in paper form. For example, they can provide printed reports of analysis results to companies and organizations to help manage the risk of fake data. In this way, the service provider can provide users with analysis results in a variety of ways and minimize the risk of fake data. Furthermore, the service provider can collect feedback from users and use it to improve the service. For example, they can provide a feedback function that allows users to submit opinions and questions about the information provided, striving to improve the quality of the service. This allows the service provider to provide users with timely and accurate information and effectively manage the risk of fake data.

[0034] The data collection unit can collect data such as images, videos, audio, and text from online platforms. For example, the data collection unit can collect image data from social media. For example, the data collection unit can also collect text data from news sites. For example, the data collection unit can collect audio data from audio platforms. For example, the data collection unit can collect image data using social media APIs. For example, the data collection unit can also collect text data using RSS feeds from news sites. For example, the data collection unit can also collect audio data using audio platform APIs. This allows for the collection of diverse data from online platforms. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit collects image data using social media APIs, it may use AI to select the images to be collected.

[0035] The analysis unit can analyze the collected data and determine whether or not it is fake data. For example, the analysis unit can analyze image data using an image analysis algorithm. The analysis unit can also analyze audio data using an audio analysis algorithm. The analysis unit can also analyze text data using a text analysis algorithm. For example, the analysis unit can perform analysis using a generative AI model that takes image data as input and outputs a fake image. The analysis unit can also perform analysis using a generative AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using a generative AI model that takes text data as input and outputs fake text. This allows the authenticity of the collected data to be determined. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the fake image determination.

[0036] The fact-checking unit can determine the veracity of text information by referring to specific databases or information on the web. For example, the fact-checking unit may refer to reliable news sites. For example, the fact-checking unit may also refer to academic databases. For example, the fact-checking unit may compare text information with databases to check for any matching information. The fact-checking unit can also refer to reliable sources on the web to verify the veracity of text information. This allows the veracity of text information to be determined. Some or all of the above processes in the fact-checking unit may be performed using AI, for example, or not using AI. For example, the fact-checking unit may input text information into AI and have the AI ​​perform the comparison with databases.

[0037] The service provider can provide the user with the analysis results and, if the data is determined to be fake, can provide the reasons and evidence for this determination. For example, the service provider can explain to the user the source and inconsistencies of the fake data. The service provider can, for example, display the analysis results to the user through a web application or mobile application. The service provider can also, for example, send the analysis results to the user via email. The service provider can also, for example, print the analysis results and provide them to the user on paper. This allows the service provider to provide the user with the reasons and evidence for the fake data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​notify the user.

[0038] The data collection unit can select the optimal data collection method according to the type and format of the data to be collected. For example, in the case of image data, the data collection unit can prioritize the collection of high-resolution images. For example, in the case of video data, the data collection unit can also collect the optimal video by considering the frame rate and resolution. For example, in the case of audio data, the data collection unit can also prioritize the collection of clear audio with less noise. This allows the data collection unit to select the optimal data collection method according to the type and format of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in the collection of image data, the data collection unit can use AI to select high-resolution images.

[0039] The data collection unit can evaluate the reliability of the data to be collected and filter out unreliable data during data collection. For example, the data collection unit can verify the source of the data and prioritize collecting data from reliable sources. The data collection unit can also analyze the content of the data and filter out unreliable information. For example, the data collection unit can consider the frequency of data updates and prioritize collecting the latest information. This allows for the filtering of unreliable data. Some or all of the above 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 reliability evaluation into the AI ​​and have the AI ​​perform the filtering of unreliable data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of 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 into AI and have the AI ​​perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about a specific topic, the data collection unit can collect data related to that topic. For example, if a user uses a specific hashtag, the data collection unit can also collect data related to that hashtag. For example, if a user follows a specific account, the data collection unit can also collect data related to that account. This allows for the collection of 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 into AI and have AI perform the collection of relevant data.

[0042] 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 perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit can also apply an audio analysis algorithm to audio data. This allows the appropriate analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows the analysis priority to be determined based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may moderately analyze data with moderate relevance. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0046] The fact-checking unit can predict the truth value of current data by referring to past fact-checking data during fact-checking. For example, the fact-checking unit predicts the truth value of current data based on past fact-checking data. The fact-checking unit can also predict the truth value of similar data by referring to past fact-checking data. For example, the fact-checking unit can analyze past fact-checking data, find patterns, and predict the truth value of current data. This allows it to predict the truth value of current data based on past fact-checking data. Some or all of the above processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input past fact-checking data into AI and have the AI ​​perform a prediction of the truth value of current data.

[0047] The fact-checking unit can apply different fact-checking methods to each data category during fact-checking. For example, the fact-checking unit can perform fact-checking using image analysis for image data. For example, the fact-checking unit can also perform fact-checking using video analysis for video data. For example, the fact-checking unit can also perform fact-checking using audio analysis for audio data. This allows for the application of an appropriate fact-checking method according to the data category. Some or all of the above-described processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate fact-checking method.

[0048] The fact-checking unit can analyze changes in fact-checking based on the data collection period during fact-checking. For example, the fact-checking unit can prioritize fact-checking the most recent data. The fact-checking unit can also postpone fact-checking older data. The fact-checking unit can also prioritize fact-checking data collected during a specific period. This allows for analysis of changes in fact-checking based on the data collection period. Some or all of the above processing in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input the data collection period into the AI ​​and have the AI ​​perform the analysis of changes in fact-checking.

[0049] The fact-checking unit can analyze fact-checks by referring to relevant market data during the fact-checking process. For example, the fact-checking unit can predict the truthfulness of data based on relevant market data. The fact-checking unit can also predict the truthfulness of similar data by referring to relevant market data. For example, the fact-checking unit can analyze relevant market data, find patterns, and predict the truthfulness of data. This allows for fact-check analysis based on relevant market data. Some or all of the above-described processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input relevant market data into AI and have AI perform the fact-check analysis.

[0050] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into AI and have AI select the optimal display method.

[0051] The information provider can customize the information based on the user's current areas of interest at the time of delivery. For example, if the user is interested in a particular topic, the provider will prioritize providing information related to that topic. For example, if the user is interested in a particular field, the provider can also customize and provide information related to that field. For example, the provider can analyze the user's current areas of interest and provide the most suitable information. This allows the information to be customized based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's current areas of interest into the AI ​​and have the AI ​​perform the information customization.

[0052] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. For example, if the user is traveling, the information provider can prioritize providing information related to the travel destination. For example, if the user is at home, the information provider can prioritize providing information around the user's home. This allows the information provider to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into AI and have AI perform the task of providing optimal information.

[0053] The information provider can analyze the user's social media activity and adjust how information is displayed at the time of delivery. For example, if a user posts about a particular topic, the provider can prioritize providing information related to that topic. For example, if a user uses a particular hashtag, the provider can also prioritize providing information related to that hashtag. For example, if a user follows a particular account, the provider can also prioritize providing information related to that account. This allows the information to be displayed in a way that is tailored to the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into AI and have AI perform the adjustment of how information is displayed.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can select the optimal data collection method according to the type and format of the data to be collected. For example, in the case of image data, the data collection unit can prioritize the collection of high-resolution images. For example, in the case of video data, the data collection unit can also collect the optimal video by considering the frame rate and resolution. For example, in the case of audio data, the data collection unit can also prioritize the collection of clear audio with less noise. This allows the data collection unit to select the optimal data collection method according to the type and format of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in the collection of image data, the data collection unit can use AI to select high-resolution images.

[0056] The data collection unit can evaluate the reliability of the data to be collected and filter out unreliable data during data collection. For example, the data collection unit can verify the source of the data and prioritize collecting data from reliable sources. The data collection unit can also analyze the content of the data and filter out unreliable information. For example, the data collection unit can consider the frequency of data updates and prioritize collecting the latest information. This allows for the filtering of unreliable data. Some or all of the above 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 reliability evaluation into the AI ​​and have the AI ​​perform the filtering of unreliable data.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of 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 into AI and have the AI ​​perform the collection of highly relevant data.

[0058] 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 perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit can also apply an audio analysis algorithm to audio data. This allows the appropriate analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate analysis algorithm.

[0060] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows the analysis priority to be determined based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects data. The collection unit collects data such as images, videos, audio, and text from online platforms, for example. The collection unit collects image data from social media, for example. The collection unit can also collect text data from news sites. Furthermore, the collection unit can also collect audio data from audio platforms. For example, the collection unit collects image data using social media APIs. The collection unit can also collect text data using news site RSS feeds. The collection unit can also collect audio data using audio platform APIs. Step 2: The analysis unit analyzes the data collected by the collection unit and determines whether or not it is fake data. The analysis unit can, for example, analyze image data using an image analysis algorithm. The analysis unit can also analyze audio data using, for example, an audio analysis algorithm. The analysis unit can also analyze text data using, for example, a text analysis algorithm. For example, the analysis unit can perform analysis using an AI model that takes image data as input and outputs a fake image. The analysis unit can also perform analysis using an AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using an AI model that takes text data as input and outputs fake text. Step 3: The fact-checking unit performs fact-checking based on the results analyzed by the analysis unit. The fact-checking unit verifies the veracity of the text information by referring to specific databases or information on the web, for example. The fact-checking unit may refer to reliable news sites, for example. The fact-checking unit may also refer to academic databases, for example. The fact-checking unit may compare the text information with databases to see if there are any matching pieces of information. The fact-checking unit may also refer to reliable sources on the web to verify the veracity of the text information. Step 4: The provider provides the user with the results obtained by the fact-checking unit. The provider provides the user with the analysis results and, if the data is determined to be fake, presents the reasons and evidence. The provider explains to the user the source and inconsistencies of the fake data. The provider displays the analysis results to the user through a web application or mobile application. The provider can also send the analysis results to the user via email. The provider can also print the analysis results and provide them to the user on paper.

[0063] (Example of form 2) An AI fake detection service according to an embodiment of the present invention is a tool for identifying and determining fake data generated using generative AI. This AI fake detection service collects data such as images, videos, audio, and text, and the AI ​​analyzes it to determine whether it is fake data. Furthermore, for text information, it performs fact-checking to clarify its authenticity by referring to specific databases and information on the web. The analysis results are provided to the user. This service can address issues such as the loss of reliability of fake data generated by generative AI, defamation, security risks, social disruption, ethical problems, and interference with education. For example, the AI ​​fake detection service collects data such as images, videos, audio, and text from online platforms. The collected data is analyzed by the AI ​​to determine whether it is fake data. For example, the AI ​​uses an image analysis algorithm to analyze image data and detect fake images. The AI ​​also uses an audio analysis algorithm to analyze audio data and detect fake audio. Furthermore, the AI ​​uses a text analysis algorithm to analyze text data and detect fake text. Next, the AI ​​fake detection service performs fact-checking on the collected text information. For example, AI refers to specific databases and web information to determine the truthfulness of text information. AI compares text information with databases to check for matching information. AI also refers to reliable sources on the web to verify the truthfulness of text information. Finally, the AI ​​fake detection service provides the user with the analysis results. For example, if the AI ​​determines that the data is fake, it provides the reasons and evidence. The AI ​​explains the source and inconsistencies of the fake data to the user. This allows the AI ​​fake detection service to identify and determine fake data generated by generative AI.

[0064] The AI ​​fake detection service according to this embodiment comprises a collection unit, an analysis unit, a fact-checking unit, and a provision unit. The collection unit collects data. The collection unit collects data such as images, videos, audio, and text from online platforms, for example. The collection unit collects image data from social media, for example. The collection unit can also collect text data from news sites. Furthermore, the collection unit can also collect audio data from audio platforms. For example, the collection unit collects image data using social media APIs. The collection unit can also collect text data using news site RSS feeds. The collection unit can also collect audio data using audio platform APIs. The analysis unit analyzes the data collected by the collection unit and determines whether or not it is fake data. The analysis unit analyzes image data using, for example, an image analysis algorithm. The analysis unit can also analyze audio data using, for example, an audio analysis algorithm. The analysis unit can also analyze text data using, for example, a text analysis algorithm. For example, the analysis unit performs analysis using an AI model that takes image data as input and outputs fake images. The analysis unit can also perform analysis using an AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using an AI model that takes text data as input and outputs fake text. The fact-checking unit performs fact-checking based on the results analyzed by the analysis unit. The fact-checking unit verifies the truthfulness of text information by referring to specific databases or information on the web, for example. The fact-checking unit can refer to reliable news sites, for example. The fact-checking unit can also refer to academic databases, for example. The fact-checking unit compares text information with databases to check for any matching information. The fact-checking unit can also verify the truthfulness of text information by referring to reliable sources on the web. The provisioning unit provides the results obtained by the fact-checking unit to the user.The service provider, for example, provides the user with the analysis results and, if the data is determined to be fake, presents the reasons and evidence. The service provider, for example, explains to the user the source and inconsistencies of the fake data. The service provider, for example, displays the analysis results to the user through a web application or mobile application. The service provider can also, for example, send the analysis results to the user via email. The service provider can also, for example, print the analysis results using a printer and provide them to the user on paper. In this way, the AI ​​fake detection service according to the embodiment can identify and determine fake data generated by the generating AI.

[0065] The data collection unit collects data. For example, it collects data such as images, videos, audio, and text from online platforms. Specifically, the data collection unit uses social media APIs to collect image data. For example, it can obtain images and videos posted by users from social media platforms in real time. This allows the data collection unit to quickly collect data on the latest trends and topics. The data collection unit can also collect text data using RSS feeds from news sites. For example, it can automatically retrieve the latest articles and posts from major news sites and blogs and save them as text data. Furthermore, the data collection unit can collect audio data using audio platform APIs. For example, it can obtain audio data from podcasts and voice messaging services and save it for analysis. This allows the data collection unit to collect a wide range of data from diverse data sources and comprehensively gather the information necessary for detecting fake data. The data collection unit centrally manages this data and makes it accessible to the analysis and fact-checking units. For example, the collected data is stored in a cloud-based database and updated in real time as needed. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0066] The analysis unit analyzes the data collected by the collection unit to determine whether or not it is fake data. For example, the analysis unit analyzes image data using an image analysis algorithm. Specifically, it utilizes image recognition technology using deep learning to extract image features and detect fake images. For example, images generated by generative AI often contain specific patterns or noise, and by detecting these, fake images can be identified. The analysis unit can also analyze audio data using an audio analysis algorithm. For example, it analyzes the waveform and spectrum of audio to detect features unique to audio generated by generative AI. This allows for highly accurate detection of fake audio. The analysis unit can also analyze text data using a text analysis algorithm. For example, it uses natural language processing technology to analyze the context and grammar of text and detect patterns unique to text generated by generative AI. This allows for highly accurate detection of fake text. Furthermore, the analysis unit can integrate these analysis results to perform a comprehensive fake data determination. For example, it analyzes image, audio, and text data and combines the results to make a final determination. This allows the analysis unit to comprehensively analyze information obtained from multiple data sources and improve the accuracy of fake data detection.

[0067] The fact-checking department performs fact-checking based on the results analyzed by the analysis department. For example, the fact-checking department refers to specific databases and web-based information to determine the veracity of textual information. Specifically, it refers to reliable news sites and academic databases and compares them with the analysis results. For instance, to verify the accuracy of a news article, it refers to multiple reliable news sources to check for matching information. It can also refer to academic databases to verify the accuracy of specific facts and data. The fact-checking department can also refer to reliable web-based sources to verify the veracity of textual information. For example, it refers to the websites of government agencies and public institutions to compare them with official information. This allows the fact-checking department to enhance the reliability of its analysis results and provide users with accurate information. Furthermore, based on the analysis results, the fact-checking department can identify the sources and inconsistencies of fake data, clarifying the basis for the information provided to users. This enhances the reliability of its analysis results and allows the fact-checking department to provide users with accurate and reliable information.

[0068] The service provider will provide users with the results obtained by the fact-checking service provider. Specifically, they will provide users with analysis results and, if data is determined to be fake, will provide the reasons and evidence. For example, they will explain to users the source and inconsistencies of the fake data and provide a detailed explanation of why the data was determined to be fake. The service provider can display analysis results to users through web applications and mobile applications. For example, they can provide a dashboard that users can access and display analysis results and fact-check results in real time. The service provider can also send analysis results to users via email. For example, they can periodically send users reports summarizing the analysis results and reporting the latest fake data detection status. Furthermore, the service provider can print the analysis results and provide them to users in paper form. For example, they can provide printed reports of analysis results to companies and organizations to help manage the risk of fake data. In this way, the service provider can provide users with analysis results in a variety of ways and minimize the risk of fake data. Furthermore, the service provider can collect feedback from users and use it to improve the service. For example, they can provide a feedback function that allows users to submit opinions and questions about the information provided, striving to improve the quality of the service. This allows the service provider to provide users with timely and accurate information and effectively manage the risk of fake data.

[0069] The data collection unit can collect data such as images, videos, audio, and text from online platforms. For example, the data collection unit can collect image data from social media. For example, the data collection unit can also collect text data from news sites. For example, the data collection unit can collect audio data from audio platforms. For example, the data collection unit can collect image data using social media APIs. For example, the data collection unit can also collect text data using RSS feeds from news sites. For example, the data collection unit can also collect audio data using audio platform APIs. This allows for the collection of diverse data from online platforms. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit collects image data using social media APIs, it may use AI to select the images to be collected.

[0070] The analysis unit can analyze the collected data and determine whether or not it is fake data. For example, the analysis unit can analyze image data using an image analysis algorithm. The analysis unit can also analyze audio data using an audio analysis algorithm. The analysis unit can also analyze text data using a text analysis algorithm. For example, the analysis unit can perform analysis using a generative AI model that takes image data as input and outputs a fake image. The analysis unit can also perform analysis using a generative AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using a generative AI model that takes text data as input and outputs fake text. This allows the authenticity of the collected data to be determined. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input image data into a generative AI and have the generative AI perform the fake image determination.

[0071] The fact-checking unit can determine the veracity of text information by referring to specific databases or information on the web. For example, the fact-checking unit may refer to reliable news sites. For example, the fact-checking unit may also refer to academic databases. For example, the fact-checking unit may compare text information with databases to check for any matching information. The fact-checking unit can also refer to reliable sources on the web to verify the veracity of text information. This allows the veracity of text information to be determined. Some or all of the above processes in the fact-checking unit may be performed using AI, for example, or not using AI. For example, the fact-checking unit may input text information into AI and have the AI ​​perform the comparison with databases.

[0072] The service provider can provide the user with the analysis results and, if the data is determined to be fake, can provide the reasons and evidence for this determination. For example, the service provider can explain to the user the source and inconsistencies of the fake data. The service provider can, for example, display the analysis results to the user through a web application or mobile application. The service provider can also, for example, send the analysis results to the user via email. The service provider can also, for example, print the analysis results and provide them to the user on paper. This allows the service provider to provide the user with the reasons and evidence for the fake data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​notify the user.

[0073] 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 example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. This allows the timing of data collection to be adjusted 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0074] The data collection unit can select the optimal data collection method according to the type and format of the data to be collected. For example, in the case of image data, the data collection unit can prioritize the collection of high-resolution images. For example, in the case of video data, the data collection unit can also collect the optimal video by considering the frame rate and resolution. For example, in the case of audio data, the data collection unit can also prioritize the collection of clear audio with less noise. This allows the data collection unit to select the optimal data collection method according to the type and format of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in the collection of image data, the data collection unit can use AI to select high-resolution images.

[0075] The data collection unit can evaluate the reliability of the data to be collected and filter out unreliable data during data collection. For example, the data collection unit can verify the source of the data and prioritize collecting data from reliable sources. The data collection unit can also analyze the content of the data and filter out unreliable information. For example, the data collection unit can consider the frequency of data updates and prioritize collecting the latest information. This allows for the filtering of unreliable data. Some or all of the above 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 reliability evaluation into the AI ​​and have the AI ​​perform the filtering of unreliable data.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting reliable data. For example, if the user is excited, the data collection unit may also prioritize collecting the most recent data. For example, if the user is relaxed, the data collection unit may also prioritize collecting detailed data. This allows the data collection unit to determine the priority of 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of 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 into AI and have the AI ​​perform the collection of highly relevant data.

[0078] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about a specific topic, the data collection unit can collect data related to that topic. For example, if a user uses a specific hashtag, the data collection unit can also collect data related to that hashtag. For example, if a user follows a specific account, the data collection unit can also collect data related to that account. This allows for the collection of 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 into AI and have AI perform the collection of relevant data.

[0079] 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 tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0080] 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 perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit can also apply an audio analysis algorithm to audio data. This allows the appropriate analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate analysis algorithm.

[0082] 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 perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually stimulating analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-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 generative AI and have the generative AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows the analysis priority to be determined based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may moderately analyze data with moderate relevance. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0085] The fact-checking unit can estimate the user's emotions and adjust the fact-checking method based on the estimated emotions. For example, if the user is nervous, the fact-checking unit can provide simple and easy-to-understand fact-checking results. For example, if the user is relaxed, the fact-checking unit can also provide detailed fact-checking results. For example, if the user is in a hurry, the fact-checking unit can also provide concise fact-checking results. This allows the fact-checking method to be adjusted 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 fact-checking unit may be performed using AI, for example, or not using AI. For example, the fact-checking unit can input user emotion data into the generative AI and have the generative AI adjust the fact-checking method.

[0086] The fact-checking unit can predict the truth value of current data by referring to past fact-checking data during fact-checking. For example, the fact-checking unit predicts the truth value of current data based on past fact-checking data. The fact-checking unit can also predict the truth value of similar data by referring to past fact-checking data. For example, the fact-checking unit can analyze past fact-checking data, find patterns, and predict the truth value of current data. This allows it to predict the truth value of current data based on past fact-checking data. Some or all of the above processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input past fact-checking data into AI and have the AI ​​perform a prediction of the truth value of current data.

[0087] The fact-checking unit can apply different fact-checking methods to each data category during fact-checking. For example, the fact-checking unit can perform fact-checking using image analysis for image data. For example, the fact-checking unit can also perform fact-checking using video analysis for video data. For example, the fact-checking unit can also perform fact-checking using audio analysis for audio data. This allows for the application of an appropriate fact-checking method according to the data category. Some or all of the above-described processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate fact-checking method.

[0088] The fact-checking unit can estimate the user's emotions and adjust the importance of fact-checking based on the estimated emotions. For example, if the user is feeling anxious, the fact-checking unit will prioritize high-importance fact-checking. For example, if the user is relaxed, the fact-checking unit can perform detailed fact-checking. For example, if the user is in a hurry, the fact-checking unit can perform concise fact-checking. This allows the importance of fact-checking to be adjusted 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 is, 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 fact-checking unit may be performed using AI or not. For example, the fact-checking unit can input user emotion data into the generative AI and have the generative AI adjust the importance of fact-checking.

[0089] The fact-checking unit can analyze changes in fact-checking based on the data collection period during fact-checking. For example, the fact-checking unit can prioritize fact-checking the most recent data. The fact-checking unit can also postpone fact-checking older data. The fact-checking unit can also prioritize fact-checking data collected during a specific period. This allows for analysis of changes in fact-checking based on the data collection period. Some or all of the above processing in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input the data collection period into the AI ​​and have the AI ​​perform the analysis of changes in fact-checking.

[0090] The fact-checking unit can analyze fact-checks by referring to relevant market data during the fact-checking process. For example, the fact-checking unit can predict the truthfulness of data based on relevant market data. The fact-checking unit can also predict the truthfulness of similar data by referring to relevant market data. For example, the fact-checking unit can analyze relevant market data, find patterns, and predict the truthfulness of data. This allows for fact-check analysis based on relevant market data. Some or all of the above-described processes in the fact-checking unit may be performed using AI, for example, or without AI. For example, the fact-checking unit can input relevant market data into AI and have AI perform the fact-check analysis.

[0091] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can also provide a display method that gets straight to the point. This allows the service provider to adjust how information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the information is displayed.

[0092] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into AI and have AI select the optimal display method.

[0093] The information provider can customize the information based on the user's current areas of interest at the time of delivery. For example, if the user is interested in a particular topic, the provider will prioritize providing information related to that topic. For example, if the user is interested in a particular field, the provider can also customize and provide information related to that field. For example, the provider can analyze the user's current areas of interest and provide the most suitable information. This allows the information to be customized based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's current areas of interest into the AI ​​and have the AI ​​perform the information customization.

[0094] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider may prioritize providing reliable information. For example, if the user is excited, the information provider may prioritize providing the latest information. For example, if the user is relaxed, the information provider may prioritize providing detailed information. This allows the information to be prioritized 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0095] The information provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. For example, if the user is traveling, the information provider can prioritize providing information related to the travel destination. For example, if the user is at home, the information provider can prioritize providing information around the user's home. This allows the information provider to provide optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into AI and have AI perform the task of providing optimal information.

[0096] The information provider can analyze the user's social media activity and adjust how information is displayed at the time of delivery. For example, if a user posts about a particular topic, the provider can prioritize providing information related to that topic. For example, if a user uses a particular hashtag, the provider can also prioritize providing information related to that hashtag. For example, if a user follows a particular account, the provider can also prioritize providing information related to that account. This allows the information to be displayed in a way that is tailored to the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into AI and have AI perform the adjustment of how information is displayed.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] 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 example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. This allows the timing of data collection to be adjusted 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0099] The data collection unit can select the optimal data collection method according to the type and format of the data to be collected. For example, in the case of image data, the data collection unit can prioritize the collection of high-resolution images. For example, in the case of video data, the data collection unit can also collect the optimal video by considering the frame rate and resolution. For example, in the case of audio data, the data collection unit can also prioritize the collection of clear audio with less noise. This allows the data collection unit to select the optimal data collection method according to the type and format of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in the collection of image data, the data collection unit can use AI to select high-resolution images.

[0100] The data collection unit can evaluate the reliability of the data to be collected and filter out unreliable data during data collection. For example, the data collection unit can verify the source of the data and prioritize collecting data from reliable sources. The data collection unit can also analyze the content of the data and filter out unreliable information. For example, the data collection unit can consider the frequency of data updates and prioritize collecting the latest information. This allows for the filtering of unreliable data. Some or all of the above 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 reliability evaluation into the AI ​​and have the AI ​​perform the filtering of unreliable data.

[0101] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting reliable data. For example, if the user is excited, the data collection unit may also prioritize collecting the most recent data. For example, if the user is relaxed, the data collection unit may also prioritize collecting detailed data. This allows the data collection unit to determine the priority of 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of 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 into AI and have the AI ​​perform the collection of highly relevant data.

[0103] 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 tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0104] 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 perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the level of detail of the analysis can be adjusted based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0105] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. For example, the analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit can also apply an audio analysis algorithm to audio data. This allows the appropriate analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of an appropriate analysis algorithm.

[0106] 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 perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually stimulating analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-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 generative AI and have the generative AI adjust the length of the analysis.

[0107] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows the analysis priority to be determined based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​determine the analysis priority.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects data. The collection unit collects data such as images, videos, audio, and text from online platforms, for example. The collection unit collects image data from social media, for example. The collection unit can also collect text data from news sites. Furthermore, the collection unit can also collect audio data from audio platforms. For example, the collection unit collects image data using social media APIs. The collection unit can also collect text data using news site RSS feeds. The collection unit can also collect audio data using audio platform APIs. Step 2: The analysis unit analyzes the data collected by the collection unit and determines whether or not it is fake data. The analysis unit can, for example, analyze image data using an image analysis algorithm. The analysis unit can also analyze audio data using, for example, an audio analysis algorithm. The analysis unit can also analyze text data using, for example, a text analysis algorithm. For example, the analysis unit can perform analysis using an AI model that takes image data as input and outputs a fake image. The analysis unit can also perform analysis using an AI model that takes audio data as input and outputs fake audio. The analysis unit can also perform analysis using an AI model that takes text data as input and outputs fake text. Step 3: The fact-checking unit performs fact-checking based on the results analyzed by the analysis unit. The fact-checking unit verifies the veracity of the text information by referring to specific databases or information on the web, for example. The fact-checking unit may refer to reliable news sites, for example. The fact-checking unit may also refer to academic databases, for example. The fact-checking unit may compare the text information with databases to see if there are any matching pieces of information. The fact-checking unit may also refer to reliable sources on the web to verify the veracity of the text information. Step 4: The provider provides the user with the results obtained by the fact-checking unit. The provider provides the user with the analysis results and, if the data is determined to be fake, presents the reasons and evidence. The provider explains to the user the source and inconsistencies of the fake data. The provider displays the analysis results to the user through a web application or mobile application. The provider can also send the analysis results to the user via email. The provider can also print the analysis results and provide them to the user on paper.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the data collection unit, analysis unit, fact-checking unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data from an online platform using the communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The fact-checking unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs fact-checking based on the analysis results. The provision unit provides the analysis results to the user using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, fact-checking unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from an online platform using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The fact-checking unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and performs fact-checking based on the analysis results. The provision unit provides the analysis results to the user, for example, using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, fact-checking unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data from an online platform using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The fact-checking unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs fact-checking based on the analysis results. The provision unit provides the analysis results to the user using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, fact-checking unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from an online platform using the communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The fact-checking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs fact-checking based on the analysis results. The provision unit provides the analysis results to the user, for example, by using the speaker 240 of the robot 414. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit and determines whether or not it is fake data, A fact-checking unit performs fact-checking based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides the results obtained by the fact-checking unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as images, videos, audio, and text from online platforms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine whether or not it is fake data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The fact-checking unit, Referencing specific databases and web information to determine the veracity of textual information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The analysis results will be provided to the user, and if the data is determined to be fake, the reasons and evidence will be presented. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Select the optimal data collection method depending on the type and format of the data to be collected. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the reliability of the data to be collected is evaluated, and unreliable data is filtered out. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) The fact-checking unit, We estimate user sentiment and adjust our fact-checking methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The fact-checking unit, During fact-checking, we refer to past fact-checking data to predict the truthfulness of the current data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fact-checking unit, When fact-checking, apply different fact-checking methods to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fact-checking unit, We estimate user sentiment and adjust the importance of fact-checking based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The fact-checking unit, During fact-checking, analyze how fact-checking changes based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The fact-checking unit, When fact-checking, analyze the facts by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, it will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust how the information is displayed. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 and determines whether or not it is fake data, A fact-checking unit performs fact-checking based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides the results obtained by the fact-checking unit to the user. A system characterized by the following features.

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

3. The aforementioned analysis unit, The collected data is analyzed to determine whether or not it is fake data. The system according to feature 1.

4. The fact-checking unit, Referencing specific databases and web information to determine the veracity of textual information. The system according to feature 1.

5. The aforementioned supply unit is, The analysis results will be provided to the user, and if the data is determined to be fake, the reasons and evidence will be presented. The system according to feature 1.

6. 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 according to feature 1.

7. The aforementioned collection unit is Select the optimal data collection method depending on the type and format of the data to be collected. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the reliability of the data to be collected is evaluated, and unreliable data is filtered out. The system according to feature 1.

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

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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