system

Abstract

The system according to this embodiment aims to detect false information on the internet and present correct factual information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a presentation unit. The collection unit collects information from the internet. The analysis unit analyzes the information collected by the collection unit in real time. The detection unit detects false information from the information analyzed by the analysis unit. The presentation unit presents correct factual information based on the false information detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and accurately detect false information on the Internet and provide correct information.

[0005] The system according to the embodiment aims to detect false information on the Internet and present correct factual information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a presentation unit. The collection unit collects information from the internet. The analysis unit analyzes the information collected by the collection unit in real time. The detection unit detects false information from the information analyzed by the analysis unit. The presentation unit presents correct factual information based on the false information detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect false information on the internet and present correct factual information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the 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 applied to the communication I / F include wireless communication standards including 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 information analysis system according to an embodiment of the present invention is a system that utilizes generative AI to analyze information on the internet in real time, detect misinformation, and present correct factual information to the user. In this information analysis system, the generative AI collects information on the internet and analyzes it in real time. Next, the generative AI detects misinformation and presents correct factual information to the user. Furthermore, the generative AI fairly analyzes opinions from various viewpoints and combines different perspectives to create a balanced overall picture. Finally, through interactive questioning with the generative AI, the user can avoid bias and gain a deeper understanding. For example, the information analysis system collects data from various sources such as news articles, blogs, and social media posts. The generative AI collects and analyzes the latest news articles and social media posts. Next, the generative AI analyzes the collected information in real time and detects misinformation. For example, it analyzes the content of news articles and identifies unreliable information. After the generative AI detects misinformation, it presents correct factual information to the user. For example, it presents highly reliable news articles and official announcements to ensure the user obtains accurate information. Furthermore, the generative AI fairly analyzes opinions from various perspectives and combines different viewpoints to create a balanced overall picture. For example, it analyzes news articles and opinions from different viewpoints and presents them in combination. Finally, interactive question-and-answer sessions with the generative AI allow users to avoid bias and gain a deeper understanding. For instance, when a user asks the generative AI a question, the AI ​​provides relevant information, deepening the user's understanding. This enables the information analysis system to analyze information on the internet in real time, detect misinformation, and present accurate factual information.

[0029] The information analysis system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a presentation unit. The collection unit collects information from the internet. The collection unit collects data from various sources, such as news articles, blogs, and social media posts. The collection unit collects information using, for example, web scraping technology. The collection unit can also obtain data using APIs. For example, the collection unit collects the latest news articles using the API of a news site. The collection unit can also evaluate the reliability of the information sources to be collected and prioritize the collection of highly reliable sources. For example, the collection unit evaluates the reliability of news sites and prioritizes the collection of information from highly reliable sites. The analysis unit analyzes the information collected by the collection unit in real time. The analysis unit analyzes the information using, for example, text mining technology. The analysis unit can also analyze the information using natural language processing technology. For example, the analysis unit analyzes the content of collected news articles and identifies unreliable information. The analysis unit can also fairly analyze opinions from various viewpoints and combine different perspectives. For example, the analysis unit analyzes news articles and opinions from different perspectives and combines and presents them. The detection unit detects misinformation from the information analyzed by the analysis unit. The detection unit detects misinformation using, for example, fact-checking techniques. The detection unit can also detect misinformation using reliability scores. For example, the detection unit calculates reliability scores for news articles and detects unreliable information as misinformation. The presentation unit presents correct factual information based on the misinformation detected by the detection unit. The presentation unit presents, for example, reliable news articles and official announcements. The presentation unit can also engage in interactive question-and-answer sessions with the generating AI and provide relevant information to the user. For example, when the user asks the generating AI a question, the generating AI provides relevant information to deepen the user's understanding. As a result, the information analysis system according to this embodiment can analyze information on the internet in real time, detect misinformation, and present correct factual information.

[0030] The analysis unit can analyze the collected information and identify unreliable information. Unreliable information includes, but is not limited to, information with unclear sources or information that is likely to be misinformation. For example, the analysis unit can analyze the content of collected news articles and identify unreliable information. For example, the analysis unit can check the sources of news articles and identify information with unclear sources as unreliable information. The analysis unit can also analyze the content of news articles and identify information that is likely to be misinformation as unreliable information. For example, the analysis unit can compare the content of news articles with other reliable sources and identify inconsistent information as unreliable information. This improves the accuracy of misinformation detection by identifying unreliable information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of collected news articles into a generative AI and have the generative AI perform the identification of unreliable information.

[0031] The presentation unit can present reliable news articles and official announcements. Reliable news articles include, but are not limited to, those from major news media and official announcements. The presentation unit can, for example, present news articles collected from reliable news media. The presentation unit can also present official statements from government agencies and companies. For example, the presentation unit can present official statements collected from the official websites of government agencies. By presenting reliable information, users can obtain accurate information. Some or all of the processing described above in the presentation unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the presentation unit can input reliable news articles and official announcements into a generative AI and have the generative AI select the information to present to the user.

[0032] The analysis unit can fairly analyze opinions from multiple viewpoints and combine different perspectives. Opinions from multiple viewpoints include, but are not limited to, political stances, expert opinions, and the opinions of the general public. For example, the analysis unit can analyze news articles from different political stances and combine and present them. It can also analyze expert opinions and the opinions of the general public and combine and present them. For example, the analysis unit can collect expert opinions and combine different perspectives based on them. This allows for the creation of a balanced overall picture by combining different perspectives. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can input collected opinions into a generative AI and have the generative AI perform combinations of different perspectives.

[0033] The presentation unit can combine and present news articles and opinions from different viewpoints. These may include, but are not limited to, news articles and opinions from different political positions or regions. For example, the presentation unit can combine and present news articles from different political positions. It can also combine and present opinions from different regions. For example, the presentation unit can collect news articles from different regions and combine and present them. This allows users to gain a multifaceted perspective by combining opinions from different viewpoints. Some or all of the processing described above in the presentation unit may be performed using, for example, a generative AI, or not. For example, the presentation unit can input news articles and opinions from different viewpoints into a generative AI and have the generative AI select the information to combine and present.

[0034] The presentation unit can engage in interactive question-and-answer sessions with the generative AI and provide the user with relevant information. Interactive question-and-answer sessions with the generative AI include, but are not limited to, chatbots or voice assistants. For example, the presentation unit can respond to user questions using a chatbot. Alternatively, the presentation unit can respond to user questions using a voice assistant. For example, when a user asks a question to the chatbot, the generative AI provides relevant information. This allows the user to gain a deeper understanding through interactive question-and-answer sessions. Some or all of the above-described processes in the presentation unit may be performed using, for example, the generative AI, or without the generative AI. For example, the presentation unit can input a user's question into the generative AI and have the generative AI provide the relevant information.

[0035] The data collection unit can evaluate the reliability of the information sources to be collected and prioritize the collection of highly reliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from highly reliable sites. For example, the data collection unit can evaluate based on the past reliability scores of news sites. The data collection unit can also evaluate the reliability of social media posters and prioritize the collection of information from highly reliable posters. For example, the data collection unit can evaluate reliability based on the number of followers and influence of social media posters. The data collection unit can also evaluate the reliability of blogs and prioritize the collection of information from highly reliable blogs. For example, the data collection unit can evaluate based on the past reliability scores and source verification of blogs. By prioritizing the collection of highly reliable sources, the reliability of the collected information is improved. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the reliability evaluation of news sites into a generative AI and have the generative AI select highly reliable information sources.

[0036] The data collection unit can filter the types of information it collects based on the user's areas of interest. For example, the data collection unit can filter information based on news categories that the user is interested in (e.g., sports, politics, entertainment). For example, the data collection unit can identify areas of interest based on the user's past search history and collect relevant information. The data collection unit can also filter information based on the social media accounts that the user follows. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. By filtering information based on the user's areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's areas of interest into a generative AI and have the generative AI perform the filtering of relevant information.

[0037] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during collection. For example, the data collection unit can prioritize collecting local news and event information based on the user's current location. For example, the data collection unit can use GPS data to identify the user's current location and collect relevant information. The data collection unit can also prioritize collecting tourist information and local news for the user's travel destination if the user is traveling. For example, the data collection unit can identify the user's geographical location information based on the user's IP address and collect relevant information. The data collection unit can also prioritize collecting information related to a specific location (e.g., traffic information, weather forecasts) if the user is in that location. For example, the data collection unit can collect traffic information and weather forecasts based on the user's current location. This allows for the collection of more relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of relevant information.

[0038] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect posts from accounts that the user follows on social media. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. The data collection unit can also collect relevant information based on posts that the user "likes" or shares on social media. For example, the data collection unit can analyze the content of posts that the user "likes" or shares and collect relevant information. The data collection unit can also collect information related to groups and events that the user participates in on social media. For example, the data collection unit can analyze the content of posts from groups and events that the user participates in and collect relevant information. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI and have the generative AI collect the relevant information.

[0039] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, in the case of important news or breaking news, the analysis unit can provide a detailed analysis. For example, the analysis unit can analyze the content of collected news articles and evaluate their importance. The analysis unit can also provide a concise analysis in the case of general news or interesting articles. For example, the analysis unit can analyze the content of collected news articles and adjust the level of detail in its analysis based on their importance. The analysis unit can also provide a concise analysis that gets to the point in the case of entertainment information or light topics. For example, the analysis unit can analyze the content of collected news articles and adjust the level of detail in its analysis based on their importance. By adjusting the level of detail in the analysis based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the content of collected news articles into a generative AI and have the generative AI perform the adjustment of the level of detail in the analysis based on importance.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, in the case of news articles, the analysis unit can apply a reliability evaluation algorithm. For example, the analysis unit can analyze the content of collected news articles and evaluate their reliability. The analysis unit can also apply a sentiment analysis algorithm in the case of social media posts. For example, the analysis unit can analyze the content of collected social media posts and evaluate their sentiment. The analysis unit can also apply an algorithm to evaluate the consistency of content in the case of blog posts. For example, the analysis unit can analyze the content of collected blog posts and evaluate their consistency. By applying different analysis algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of the collected information into a generative AI and have the generative AI select the analysis algorithm to apply.

[0041] The analysis department can prioritize analysis based on the timing of information submission. For example, the analysis department might prioritize analyzing the latest news articles. For example, the analysis department might prioritize based on the submission date and time of the collected news articles. The analysis department can also prioritize analyzing urgent information and breaking news. For example, the analysis department might assess the urgency of the collected news articles and prioritize them. The analysis department can also postpone analyzing older information and past news articles. For example, the analysis department might prioritize based on the submission date and time of the collected news articles. By prioritizing analysis based on the timing of information submission, more appropriate information can be provided. Some or all of the above processes in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input the submission date and time of the collected news articles into a generative AI and have the generative AI perform the priority determination.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's areas of interest. For example, the analysis unit can evaluate relevance based on the user's past search history and determine the order of analysis. The analysis unit can also prioritize the analysis of information related to keywords the user has searched for in the past. For example, the analysis unit can evaluate relevance based on the user's search history and determine the order of analysis. The analysis unit can also prioritize the analysis of information related to social media accounts the user follows. For example, the analysis unit can evaluate relevance based on the content of posts from accounts the user follows and determine the order of analysis. By adjusting the order of analysis based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's areas of interest and search history into generative AI and have the generative AI perform the adjustment of the analysis order based on relevance.

[0043] The detection unit can improve the accuracy of its detection based on the interrelationships of information during detection. For example, the detection unit can compare the content of multiple news articles and detect inconsistent information as misinformation. For example, the detection unit can analyze the content of collected news articles and evaluate their interrelationships. The detection unit can also analyze the relationships between social media posters and detect unreliable information. For example, the detection unit can evaluate interrelationships based on the number of followers and influence of social media posters. The detection unit can also check the sources cited in blog posts and detect unreliable information. For example, the detection unit can analyze the sources cited in collected blog posts and evaluate their interrelationships. By improving the accuracy of detection based on the interrelationships of information, the accuracy of misinformation detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the interrelationships of the collected information into a generative AI and have the generative AI perform the detection accuracy improvement.

[0044] The detection unit can perform detection based on the attribute information of the information submitter. For example, the detection unit can evaluate the past reliability of a news reporter and detect information from reporters with low reliability as false information. For example, the detection unit can perform evaluations based on the past reliability scores of the reporters of the collected news articles. The detection unit can also evaluate the number of followers and influence of social media posters and detect posts with low reliability as false information. For example, the detection unit can perform evaluations based on the number of followers and influence of social media posters. The detection unit can also evaluate the expertise and background of blog authors and detect information with low reliability as false information. For example, the detection unit can perform evaluations based on the expertise and background of the authors of the collected blog articles. This improves the accuracy of false information detection by performing detection based on the attribute information of the information submitter. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the attribute information of the information submitter into a generative AI and have the generative AI perform the detection.

[0045] The detection unit can perform detection based on the geographical distribution of information. For example, the detection unit can detect information that is circulating only in a specific region. For example, the detection unit analyzes the geographical distribution of collected information and detects information that is circulating only in a specific region. The detection unit can also evaluate the reliability of information for each region and detect information from unreliable regions as false information. For example, the detection unit analyzes the geographical distribution of collected information and detects information from unreliable regions. The detection unit can also evaluate the consistency of information for each region and detect inconsistent information as false information. For example, the detection unit analyzes the geographical distribution of collected information and detects inconsistent information. By performing detection based on the geographical distribution of information, the accuracy of false information detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the geographical distribution of collected information into a generative AI and have the generative AI perform the detection.

[0046] The detection unit can improve the accuracy of its detection by referring to relevant literature during the detection process. For example, the detection unit can check the source of a news article and detect unreliable information as false information. For example, the detection unit can analyze the source of a collected news article and refer to relevant literature. The detection unit can also compare the content of social media posts with relevant literature and detect inconsistent information as false information. For example, the detection unit can analyze the content of a collected social media post and refer to relevant literature. The detection unit can also compare the content of a blog post with relevant research papers and detect unreliable information as false information. For example, the detection unit can analyze the content of a collected blog post and refer to relevant literature. By referring to relevant literature for information, the accuracy of false information detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the relevant literature for the collected information into a generative AI and have the generative AI perform the task of improving detection accuracy.

[0047] The presentation unit can adjust the level of detail presented based on the importance of the information. For example, in the case of important news or urgent information, the presentation unit will present detailed information. For example, the presentation unit will analyze the content of the collected news articles and evaluate their importance. The presentation unit can also present concise information in the case of general news or interesting articles. For example, the presentation unit will analyze the content of the collected news articles and adjust the level of detail based on their importance. The presentation unit can also present concise information that gets to the point in the case of entertainment information or light topics. For example, the presentation unit will analyze the content of the collected news articles and adjust the level of detail based on their importance. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the content of the collected news articles into a generative AI and have the generative AI perform the adjustment of the level of detail based on importance.

[0048] The presentation unit can apply different presentation algorithms depending on the category of information during presentation. For example, in the case of news articles, the presentation unit can apply a reliability evaluation algorithm. For example, the presentation unit analyzes the content of collected news articles and evaluates their reliability. The presentation unit can also apply a sentiment analysis algorithm to social media posts. For example, the presentation unit analyzes the content of collected social media posts and evaluates their sentiment. The presentation unit can also apply an algorithm to evaluate the consistency of content to blog posts. For example, the presentation unit analyzes the content of collected blog posts and evaluates their consistency. By applying different presentation algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the category of the collected information into a generative AI and have the generative AI select the presentation algorithm to apply.

[0049] The presentation unit can determine the priority of information presentation based on the submission date of the information. For example, the presentation unit can prioritize the presentation of the latest news articles. For example, the presentation unit can determine the priority based on the submission date and time of the collected news articles. The presentation unit can also prioritize the presentation of urgent information and breaking news. For example, the presentation unit can evaluate the urgency of the collected news articles and determine the priority. The presentation unit can also postpone the presentation of older information and past news articles. For example, the presentation unit can determine the priority based on the submission date and time of the collected news articles. By determining the priority of presentation based on the submission date of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the presentation unit can input the submission date and time of the collected news articles into a generating AI and have the generating AI perform the priority determination.

[0050] The presentation unit can adjust the order of presentation based on the relevance of the information. For example, the presentation unit can prioritize information related to the user's areas of interest. For example, the presentation unit can evaluate relevance based on the user's past search history and determine the order of presentation. The presentation unit can also prioritize information related to keywords the user has searched for in the past. For example, the presentation unit can evaluate relevance based on the user's search history and determine the order of presentation. The presentation unit can also prioritize information related to social media accounts the user follows. For example, the presentation unit can evaluate relevance based on the content of posts from accounts the user follows and determine the order of presentation. By adjusting the order of presentation based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the user's areas of interest and search history into a generative AI and have the generative AI perform the adjustment of the order of presentation based on relevance.

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

[0052] The data collection unit can analyze a user's past behavior history and determine the priority of the information to collect. For example, it can prioritize collecting highly relevant information based on links the user has clicked and pages they have viewed in the past. It can also collect relevant information based on newsletters the user has subscribed to and accounts they have followed in the past. Furthermore, it can collect relevant information based on events and seminars the user has attended in the past. By prioritizing information based on the user's past behavior history, it can collect more relevant information.

[0053] The analysis unit can refer to the historical reliability scores of information sources to evaluate the reliability of the collected information. For example, the analysis unit can evaluate the reliability of collected news articles based on the historical reliability scores of news sites. It can also evaluate the reliability of collected posts based on the historical reliability scores of social media posters. Furthermore, the analysis unit can evaluate the reliability of collected blog posts based on the historical reliability scores of blog authors. This allows for the evaluation of the reliability of collected information by referring to the historical reliability scores of information sources, thereby improving the accuracy of misinformation detection.

[0054] The presentation section can customize and present information based on the user's areas of interest. For example, the presentation section can customize information based on the news categories the user is interested in (e.g., sports, politics, entertainment). It can also customize information based on the social media accounts the user follows. Furthermore, it can customize information based on keywords the user has previously searched for. This allows for the provision of more relevant information by customizing it based on the user's areas of interest.

[0055] The analysis department can cluster the content of collected information and group similar information. For example, the analysis department can cluster the content of news articles and group similar articles. It can also cluster the content of social media posts and group similar posts. Furthermore, it can cluster the content of blog posts and group similar articles. By clustering the content of collected information, the organization and analysis of information becomes easier, and the accuracy of misinformation detection is improved.

[0056] The presentation section can collect user feedback and improve the quality of the information it presents. For example, the presentation section can provide a function for users to leave ratings and comments on the information presented. Furthermore, the presentation section can adjust the selection criteria for the information presented based on user feedback. In addition, the presentation section can analyze user feedback and improve the format and content of the information presented. This allows for improved information quality and more satisfying information provision by collecting user feedback.

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

[0058] Step 1: The data collection unit collects information from the internet. The data collection unit collects data from various sources, such as news articles, blogs, and social media posts. The data collection unit can collect information using techniques such as web scraping. The data collection unit can also obtain data using APIs. For example, the data collection unit can use the API of a news site to collect the latest news articles. The data collection unit can also evaluate the reliability of the information sources it collects and prioritize collecting information from reliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize collecting information from reliable sites. Step 2: The analysis unit analyzes the information collected by the collection unit in real time. The analysis unit analyzes the information using, for example, text mining techniques. The analysis unit can also analyze the information using natural language processing techniques. For example, the analysis unit analyzes the content of collected news articles and identifies unreliable information. The analysis unit can also fairly analyze opinions from various viewpoints and combine different perspectives. For example, the analysis unit analyzes news articles and opinions from different viewpoints and presents them in combination. Step 3: The detection unit detects false information from the information analyzed by the analysis unit. The detection unit can detect false information using, for example, fact-checking techniques. Alternatively, the detection unit can detect false information using reliability scores. For example, the detection unit calculates a reliability score for news articles and detects information with low reliability as false information. Step 4: The presentation unit presents correct factual information based on the false information detected by the detection unit. For example, the presentation unit presents reliable news articles or official announcements. The presentation unit can also engage in interactive question-and-answer sessions with the generating AI to provide relevant information to the user. For example, when the user asks the generating AI a question, the presentation unit can have the generating AI provide relevant information to deepen the user's understanding.

[0059] (Example of form 2) An information analysis system according to an embodiment of the present invention is a system that utilizes generative AI to analyze information on the internet in real time, detect misinformation, and present correct factual information to the user. In this information analysis system, the generative AI collects information on the internet and analyzes it in real time. Next, the generative AI detects misinformation and presents correct factual information to the user. Furthermore, the generative AI fairly analyzes opinions from various viewpoints and combines different perspectives to create a balanced overall picture. Finally, through interactive questioning with the generative AI, the user can avoid bias and gain a deeper understanding. For example, the information analysis system collects data from various sources such as news articles, blogs, and social media posts. The generative AI collects and analyzes the latest news articles and social media posts. Next, the generative AI analyzes the collected information in real time and detects misinformation. For example, it analyzes the content of news articles and identifies unreliable information. After the generative AI detects misinformation, it presents correct factual information to the user. For example, it presents highly reliable news articles and official announcements to ensure the user obtains accurate information. Furthermore, the generative AI fairly analyzes opinions from various perspectives and combines different viewpoints to create a balanced overall picture. For example, it analyzes news articles and opinions from different viewpoints and presents them in combination. Finally, interactive question-and-answer sessions with the generative AI allow users to avoid bias and gain a deeper understanding. For instance, when a user asks the generative AI a question, the AI ​​provides relevant information, deepening the user's understanding. This enables the information analysis system to analyze information on the internet in real time, detect misinformation, and present accurate factual information.

[0060] The information analysis system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a presentation unit. The collection unit collects information from the internet. The collection unit collects data from various sources, such as news articles, blogs, and social media posts. The collection unit collects information using, for example, web scraping technology. The collection unit can also obtain data using APIs. For example, the collection unit collects the latest news articles using the API of a news site. The collection unit can also evaluate the reliability of the information sources to be collected and prioritize the collection of highly reliable sources. For example, the collection unit evaluates the reliability of news sites and prioritizes the collection of information from highly reliable sites. The analysis unit analyzes the information collected by the collection unit in real time. The analysis unit analyzes the information using, for example, text mining technology. The analysis unit can also analyze the information using natural language processing technology. For example, the analysis unit analyzes the content of collected news articles and identifies unreliable information. The analysis unit can also fairly analyze opinions from various viewpoints and combine different perspectives. For example, the analysis unit analyzes news articles and opinions from different perspectives and combines and presents them. The detection unit detects misinformation from the information analyzed by the analysis unit. The detection unit detects misinformation using, for example, fact-checking techniques. The detection unit can also detect misinformation using reliability scores. For example, the detection unit calculates reliability scores for news articles and detects unreliable information as misinformation. The presentation unit presents correct factual information based on the misinformation detected by the detection unit. The presentation unit presents, for example, reliable news articles and official announcements. The presentation unit can also engage in interactive question-and-answer sessions with the generating AI and provide relevant information to the user. For example, when the user asks the generating AI a question, the generating AI provides relevant information to deepen the user's understanding. As a result, the information analysis system according to this embodiment can analyze information on the internet in real time, detect misinformation, and present correct factual information.

[0061] The analysis unit can analyze the collected information and identify unreliable information. Unreliable information includes, but is not limited to, information with unclear sources or information that is likely to be misinformation. For example, the analysis unit can analyze the content of collected news articles and identify unreliable information. For example, the analysis unit can check the sources of news articles and identify information with unclear sources as unreliable information. The analysis unit can also analyze the content of news articles and identify information that is likely to be misinformation as unreliable information. For example, the analysis unit can compare the content of news articles with other reliable sources and identify inconsistent information as unreliable information. This improves the accuracy of misinformation detection by identifying unreliable information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of collected news articles into a generative AI and have the generative AI perform the identification of unreliable information.

[0062] The presentation unit can present reliable news articles and official announcements. Reliable news articles include, but are not limited to, those from major news media and official announcements. The presentation unit can, for example, present news articles collected from reliable news media. The presentation unit can also present official statements from government agencies and companies. For example, the presentation unit can present official statements collected from the official websites of government agencies. By presenting reliable information, users can obtain accurate information. Some or all of the processing described above in the presentation unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the presentation unit can input reliable news articles and official announcements into a generative AI and have the generative AI select the information to present to the user.

[0063] The analysis unit can fairly analyze opinions from multiple viewpoints and combine different perspectives. Opinions from multiple viewpoints include, but are not limited to, political stances, expert opinions, and the opinions of the general public. For example, the analysis unit can analyze news articles from different political stances and combine and present them. It can also analyze expert opinions and the opinions of the general public and combine and present them. For example, the analysis unit can collect expert opinions and combine different perspectives based on them. This allows for the creation of a balanced overall picture by combining different perspectives. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can input collected opinions into a generative AI and have the generative AI perform combinations of different perspectives.

[0064] The presentation unit can combine and present news articles and opinions from different viewpoints. These may include, but are not limited to, news articles and opinions from different political positions or regions. For example, the presentation unit can combine and present news articles from different political positions. It can also combine and present opinions from different regions. For example, the presentation unit can collect news articles from different regions and combine and present them. This allows users to gain a multifaceted perspective by combining opinions from different viewpoints. Some or all of the processing described above in the presentation unit may be performed using, for example, a generative AI, or not. For example, the presentation unit can input news articles and opinions from different viewpoints into a generative AI and have the generative AI select the information to combine and present.

[0065] The presentation unit can engage in interactive question-and-answer sessions with the generative AI and provide the user with relevant information. Interactive question-and-answer sessions with the generative AI include, but are not limited to, chatbots or voice assistants. For example, the presentation unit can respond to user questions using a chatbot. Alternatively, the presentation unit can respond to user questions using a voice assistant. For example, when a user asks a question to the chatbot, the generative AI provides relevant information. This allows the user to gain a deeper understanding through interactive question-and-answer sessions. Some or all of the above-described processes in the presentation unit may be performed using, for example, the generative AI, or without the generative AI. For example, the presentation unit can input a user's question into the generative AI and have the generative AI provide the relevant information.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The data collection unit can evaluate the reliability of the information sources to be collected and prioritize the collection of highly reliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from highly reliable sites. For example, the data collection unit can evaluate based on the past reliability scores of news sites. The data collection unit can also evaluate the reliability of social media posters and prioritize the collection of information from highly reliable posters. For example, the data collection unit can evaluate reliability based on the number of followers and influence of social media posters. The data collection unit can also evaluate the reliability of blogs and prioritize the collection of information from highly reliable blogs. For example, the data collection unit can evaluate based on the past reliability scores and source verification of blogs. By prioritizing the collection of highly reliable sources, the reliability of the collected information is improved. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the reliability evaluation of news sites into a generative AI and have the generative AI select highly reliable information sources.

[0068] The data collection unit can filter the types of information it collects based on the user's areas of interest. For example, the data collection unit can filter information based on news categories that the user is interested in (e.g., sports, politics, entertainment). For example, the data collection unit can identify areas of interest based on the user's past search history and collect relevant information. The data collection unit can also filter information based on the social media accounts that the user follows. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. By filtering information based on the user's areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's areas of interest into a generative AI and have the generative AI perform the filtering of relevant information.

[0069] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important news and urgent information. For example, the data collection unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also prioritize collecting interesting articles and entertainment information if the user is relaxed. For example, the data collection unit may record the user's voice and estimate their emotions using voice analysis technology. The data collection unit can also prioritize collecting summary information that can be understood in a short time if the user is in a hurry. For example, the data collection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the collection of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's emotions into a generative AI and have the generative AI determine the priority of the information.

[0070] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during collection. For example, the data collection unit can prioritize collecting local news and event information based on the user's current location. For example, the data collection unit can use GPS data to identify the user's current location and collect relevant information. The data collection unit can also prioritize collecting tourist information and local news for the user's travel destination if the user is traveling. For example, the data collection unit can identify the user's geographical location information based on the user's IP address and collect relevant information. The data collection unit can also prioritize collecting information related to a specific location (e.g., traffic information, weather forecasts) if the user is in that location. For example, the data collection unit can collect traffic information and weather forecasts based on the user's current location. This allows for the collection of more relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of relevant information.

[0071] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect posts from accounts that the user follows on social media. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. The data collection unit can also collect relevant information based on posts that the user "likes" or shares on social media. For example, the data collection unit can analyze the content of posts that the user "likes" or shares and collect relevant information. The data collection unit can also collect information related to groups and events that the user participates in on social media. For example, the data collection unit can analyze the content of posts from groups and events that the user participates in and collect relevant information. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI and have the generative AI collect the relevant information.

[0072] 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 highly visual presentation. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a presentation that includes detailed information. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Also, if the user is in a hurry, the analysis unit can provide a concise presentation that gets straight to the point. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's emotions into a generative AI and have the generative AI adjust the way the analysis is expressed.

[0073] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, in the case of important news or breaking news, the analysis unit can provide a detailed analysis. For example, the analysis unit can analyze the content of collected news articles and evaluate their importance. The analysis unit can also provide a concise analysis in the case of general news or interesting articles. For example, the analysis unit can analyze the content of collected news articles and adjust the level of detail in its analysis based on their importance. The analysis unit can also provide a concise analysis that gets to the point in the case of entertainment information or light topics. For example, the analysis unit can analyze the content of collected news articles and adjust the level of detail in its analysis based on their importance. By adjusting the level of detail in the analysis based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the content of collected news articles into a generative AI and have the generative AI perform the adjustment of the level of detail in the analysis based on importance.

[0074] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, in the case of news articles, the analysis unit can apply a reliability evaluation algorithm. For example, the analysis unit can analyze the content of collected news articles and evaluate their reliability. The analysis unit can also apply a sentiment analysis algorithm in the case of social media posts. For example, the analysis unit can analyze the content of collected social media posts and evaluate their sentiment. The analysis unit can also apply an algorithm to evaluate the consistency of content in the case of blog posts. For example, the analysis unit can analyze the content of collected blog posts and evaluate their consistency. By applying different analysis algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of the collected information into a generative AI and have the generative AI select the analysis algorithm to apply.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also provide a longer analysis with more detailed explanations if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also provide an analysis with visually stimulating effects if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more relevant information by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the user's emotions into the generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis department can prioritize analysis based on the timing of information submission. For example, the analysis department might prioritize analyzing the latest news articles. For example, the analysis department might prioritize based on the submission date and time of the collected news articles. The analysis department can also prioritize analyzing urgent information and breaking news. For example, the analysis department might assess the urgency of the collected news articles and prioritize them. The analysis department can also postpone analyzing older information and past news articles. For example, the analysis department might prioritize based on the submission date and time of the collected news articles. By prioritizing analysis based on the timing of information submission, more appropriate information can be provided. Some or all of the above processes in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input the submission date and time of the collected news articles into a generative AI and have the generative AI perform the priority determination.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's areas of interest. For example, the analysis unit can evaluate relevance based on the user's past search history and determine the order of analysis. The analysis unit can also prioritize the analysis of information related to keywords the user has searched for in the past. For example, the analysis unit can evaluate relevance based on the user's search history and determine the order of analysis. The analysis unit can also prioritize the analysis of information related to social media accounts the user follows. For example, the analysis unit can evaluate relevance based on the content of posts from accounts the user follows and determine the order of analysis. By adjusting the order of analysis based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's areas of interest and search history into generative AI and have the generative AI perform the adjustment of the analysis order based on relevance.

[0078] The detection unit can estimate the user's emotions and adjust the false information detection criteria based on the estimated emotions. For example, if the user is tense, the detection unit can detect false information using strict criteria. For example, the detection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The detection unit can also detect false information using flexible criteria if the user is relaxed. For example, the detection unit can record the user's voice and estimate their emotions using voice analysis technology. The detection unit can also quickly detect false information if the user is in a hurry. For example, the detection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate false information detection by adjusting the false information detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the user's emotions into the generating AI and have the generating AI adjust the criteria for detecting false information.

[0079] The detection unit can improve the accuracy of its detection based on the interrelationships of information during detection. For example, the detection unit can compare the content of multiple news articles and detect inconsistent information as misinformation. For example, the detection unit can analyze the content of collected news articles and evaluate their interrelationships. The detection unit can also analyze the relationships between social media posters and detect unreliable information. For example, the detection unit can evaluate interrelationships based on the number of followers and influence of social media posters. The detection unit can also check the sources cited in blog posts and detect unreliable information. For example, the detection unit can analyze the sources cited in collected blog posts and evaluate their interrelationships. By improving the accuracy of detection based on the interrelationships of information, the accuracy of misinformation detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the interrelationships of the collected information into a generative AI and have the generative AI perform the detection accuracy improvement.

[0080] The detection unit can perform detection based on the attribute information of the information submitter. For example, the detection unit can evaluate the past reliability of a news reporter and detect information from reporters with low reliability as false information. For example, the detection unit can perform evaluations based on the past reliability scores of the reporters of the collected news articles. The detection unit can also evaluate the number of followers and influence of social media posters and detect posts with low reliability as false information. For example, the detection unit can perform evaluations based on the number of followers and influence of social media posters. The detection unit can also evaluate the expertise and background of blog authors and detect information with low reliability as false information. For example, the detection unit can perform evaluations based on the expertise and background of the authors of the collected blog articles. This improves the accuracy of false information detection by performing detection based on the attribute information of the information submitter. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the attribute information of the information submitter into a generative AI and have the generative AI perform the detection.

[0081] The detection unit can estimate the user's emotions and adjust the display order of the detection results based on the estimated emotions. For example, if the user is tense, the detection unit will prioritize displaying important detection results. For example, the detection unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The detection unit can also display detailed detection results if the user is relaxed. For example, the detection unit may record the user's voice and estimate the emotion using voice analysis technology. The detection unit can also prioritize displaying concise detection results if the user is in a hurry. For example, the detection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the display order of detection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the user's emotions into the generating AI and have the generating AI adjust the display order of the detection results.

[0082] The detection unit can perform detection based on the geographical distribution of information. For example, the detection unit can detect information that is circulating only in a specific region. For example, the detection unit analyzes the geographical distribution of collected information and detects information that is circulating only in a specific region. The detection unit can also evaluate the reliability of information for each region and detect information from unreliable regions as false information. For example, the detection unit analyzes the geographical distribution of collected information and detects information from unreliable regions. The detection unit can also evaluate the consistency of information for each region and detect inconsistent information as false information. For example, the detection unit analyzes the geographical distribution of collected information and detects inconsistent information. By performing detection based on the geographical distribution of information, the accuracy of false information detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the geographical distribution of collected information into a generative AI and have the generative AI perform the detection.

[0083] The detection unit can improve the accuracy of its detection by referring to relevant literature during the detection process. For example, the detection unit can check the source of a news article and detect unreliable information as false information. For example, the detection unit can analyze the source of a collected news article and refer to relevant literature. The detection unit can also compare the content of social media posts with relevant literature and detect inconsistent information as false information. For example, the detection unit can analyze the content of a collected social media post and refer to relevant literature. The detection unit can also compare the content of a blog post with relevant research papers and detect unreliable information as false information. For example, the detection unit can analyze the content of a collected blog post and refer to relevant literature. By referring to relevant literature for information, the accuracy of false information detection is improved. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the relevant literature for the collected information into a generative AI and have the generative AI perform the task of improving detection accuracy.

[0084] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, the presentation unit can present information in a simple and highly visible way. For example, the presentation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Also, if the user is relaxed, the presentation unit can present information in a way that includes detailed information. For example, the presentation unit can record the user's voice and estimate their emotions using voice analysis technology. Also, if the user is in a hurry, the presentation unit can present information in a concise way that gets straight to the point. For example, the presentation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the presentation method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the presentation unit can input the user's emotions into the generative AI and have the generative AI adjust the presentation method.

[0085] The presentation unit can adjust the level of detail presented based on the importance of the information. For example, in the case of important news or urgent information, the presentation unit will present detailed information. For example, the presentation unit will analyze the content of the collected news articles and evaluate their importance. The presentation unit can also present concise information in the case of general news or interesting articles. For example, the presentation unit will analyze the content of the collected news articles and adjust the level of detail based on their importance. The presentation unit can also present concise information that gets to the point in the case of entertainment information or light topics. For example, the presentation unit will analyze the content of the collected news articles and adjust the level of detail based on their importance. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the content of the collected news articles into a generative AI and have the generative AI perform the adjustment of the level of detail based on importance.

[0086] The presentation unit can apply different presentation algorithms depending on the category of information during presentation. For example, in the case of news articles, the presentation unit can apply a reliability evaluation algorithm. For example, the presentation unit analyzes the content of collected news articles and evaluates their reliability. The presentation unit can also apply a sentiment analysis algorithm to social media posts. For example, the presentation unit analyzes the content of collected social media posts and evaluates their sentiment. The presentation unit can also apply an algorithm to evaluate the consistency of content to blog posts. For example, the presentation unit analyzes the content of collected blog posts and evaluates their consistency. By applying different presentation algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the category of the collected information into a generative AI and have the generative AI select the presentation algorithm to apply.

[0087] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the presentation unit can present short, concise information. For example, the presentation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The presentation unit can also present longer information, including detailed explanations, if the user is relaxed. For example, the presentation unit can record the user's voice and estimate their emotions using voice analysis technology. The presentation unit can also present information with visually stimulating effects if the user is excited. For example, the presentation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the length of the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the presentation unit can input the user's emotions into the generative AI and have the generative AI adjust the length of the presentation.

[0088] The presentation unit can determine the priority of information presentation based on the submission date of the information. For example, the presentation unit can prioritize the presentation of the latest news articles. For example, the presentation unit can determine the priority based on the submission date and time of the collected news articles. The presentation unit can also prioritize the presentation of urgent information and breaking news. For example, the presentation unit can evaluate the urgency of the collected news articles and determine the priority. The presentation unit can also postpone the presentation of older information and past news articles. For example, the presentation unit can determine the priority based on the submission date and time of the collected news articles. By determining the priority of presentation based on the submission date of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the presentation unit can input the submission date and time of the collected news articles into a generating AI and have the generating AI perform the priority determination.

[0089] The presentation unit can adjust the order of presentation based on the relevance of the information. For example, the presentation unit can prioritize information related to the user's areas of interest. For example, the presentation unit can evaluate relevance based on the user's past search history and determine the order of presentation. The presentation unit can also prioritize information related to keywords the user has searched for in the past. For example, the presentation unit can evaluate relevance based on the user's search history and determine the order of presentation. The presentation unit can also prioritize information related to social media accounts the user follows. For example, the presentation unit can evaluate relevance based on the content of posts from accounts the user follows and determine the order of presentation. By adjusting the order of presentation based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the user's areas of interest and search history into a generative AI and have the generative AI perform the adjustment of the order of presentation based on relevance. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as news articles and social media posts. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected information in real time. The detection unit is implemented by the identification processing unit 290 of the data processing device 12 and detects false information. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents correct factual information to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as news articles and social media posts. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information in real time. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects false information. The presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and presents correct factual information to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as news articles and social media posts. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information in real time. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects false information. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents correct factual information to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and presentation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information such as news articles and social media posts. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information in real time. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects false information. The presentation unit is implemented, for example, by the control unit 46A of the robot 414 and presents correct factual information to the user.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can analyze a user's past behavior history and determine the priority of the information to collect. For example, it can prioritize collecting highly relevant information based on links the user has clicked and pages they have viewed in the past. It can also collect relevant information based on newsletters the user has subscribed to and accounts they have followed in the past. Furthermore, it can collect relevant information based on events and seminars the user has attended in the past. By prioritizing information based on the user's past behavior history, it can collect more relevant information.

[0092] The analysis unit can refer to the historical reliability scores of information sources to evaluate the reliability of the collected information. For example, the analysis unit can evaluate the reliability of collected news articles based on the historical reliability scores of news sites. It can also evaluate the reliability of collected posts based on the historical reliability scores of social media posters. Furthermore, the analysis unit can evaluate the reliability of collected blog posts based on the historical reliability scores of blog authors. This allows for the evaluation of the reliability of collected information by referring to the historical reliability scores of information sources, thereby improving the accuracy of misinformation detection.

[0093] The presentation section can customize and present information based on the user's areas of interest. For example, the presentation section can customize information based on the news categories the user is interested in (e.g., sports, politics, entertainment). It can also customize information based on the social media accounts the user follows. Furthermore, it can customize information based on keywords the user has previously searched for. This allows for the provision of more relevant information by customizing it based on the user's areas of interest.

[0094] The analysis department can cluster the content of collected information and group similar information. For example, the analysis department can cluster the content of news articles and group similar articles. It can also cluster the content of social media posts and group similar posts. Furthermore, it can cluster the content of blog posts and group similar articles. By clustering the content of collected information, the organization and analysis of information becomes easier, and the accuracy of misinformation detection is improved.

[0095] The presentation section can collect user feedback and improve the quality of the information it presents. For example, the presentation section can provide a function for users to leave ratings and comments on the information presented. Furthermore, the presentation section can adjust the selection criteria for the information presented based on user feedback. In addition, the presentation section can analyze user feedback and improve the format and content of the information presented. This allows for improved information quality and more satisfying information provision by collecting user feedback.

[0096] The data collection unit can estimate the user's emotions and adjust the frequency of information collection based on those emotions. For example, if the user is stressed, the data collection unit will reduce the frequency of information collection. Conversely, if the user is relaxed, the data collection unit can increase the frequency of information collection. Furthermore, if the user is excited, the data collection unit can increase the frequency of information collection on a specific topic. By adjusting the frequency of information collection according to the user's emotions, it becomes possible to provide more appropriate information.

[0097] The analytics unit can estimate the user's emotions and adjust the visualization method of the analysis based on those estimated emotions. For example, if the user is stressed, the analytics unit can provide simple, easy-to-read graphs and charts. If the user is relaxed, the analytics unit can also provide complex visualizations with detailed data. Furthermore, if the user is excited, the analytics unit can provide visualizations with visually stimulating effects. By adjusting the visualization method of the analysis according to the user's emotions, it becomes possible to provide more relevant information.

[0098] The detection unit can estimate the user's emotions and adjust the misinformation detection algorithm based on those emotions. For example, if the user is stressed, the detection unit can detect misinformation using strict criteria. Conversely, if the user is relaxed, the detection unit can detect misinformation using more flexible criteria. Furthermore, if the user is in a hurry, the detection unit can quickly detect misinformation. This allows for more effective misinformation detection by adjusting the misinformation detection algorithm according to the user's emotions.

[0099] The presentation unit can estimate the user's emotions and adjust the order in which information is presented based on those emotions. For example, if the user is nervous, the presentation unit will prioritize presenting important information. If the user is relaxed, the presentation unit can also present information in an order that includes more details. Furthermore, if the user is in a hurry, the presentation unit can prioritize presenting information that gets straight to the point. By adjusting the order in which information is presented according to the user's emotions, it becomes possible to provide more appropriate information.

[0100] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on those emotions. For example, if the user is feeling tense, the analysis unit can provide a concise and to-the-point analysis. If the user is relaxed, it can provide an analysis with more detailed data. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the level of detail in the analysis according to the user's emotions, it becomes possible to provide more relevant information.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects information from the internet. The data collection unit collects data from various sources, such as news articles, blogs, and social media posts. The data collection unit can collect information using techniques such as web scraping. The data collection unit can also obtain data using APIs. For example, the data collection unit can use the API of a news site to collect the latest news articles. The data collection unit can also evaluate the reliability of the information sources it collects and prioritize collecting information from reliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize collecting information from reliable sites. Step 2: The analysis unit analyzes the information collected by the collection unit in real time. The analysis unit analyzes the information using, for example, text mining techniques. The analysis unit can also analyze the information using natural language processing techniques. For example, the analysis unit analyzes the content of collected news articles and identifies unreliable information. The analysis unit can also fairly analyze opinions from various viewpoints and combine different perspectives. For example, the analysis unit analyzes news articles and opinions from different viewpoints and presents them in combination. Step 3: The detection unit detects false information from the information analyzed by the analysis unit. The detection unit can detect false information using, for example, fact-checking techniques. Alternatively, the detection unit can detect false information using reliability scores. For example, the detection unit calculates a reliability score for news articles and detects information with low reliability as false information. Step 4: The presentation unit presents correct factual information based on the false information detected by the detection unit. For example, the presentation unit presents reliable news articles or official announcements. The presentation unit can also engage in interactive question-and-answer sessions with the generating AI to provide relevant information to the user. For example, when the user asks the generating AI a question, the presentation unit can have the generating AI provide relevant information to deepen the user's understanding.

[0103] 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.

[0104] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] 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. 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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. 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.

[0154] 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.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of Symbols]

[0175] 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 collection unit that collects information from the internet, An analysis unit analyzes the information collected by the aforementioned collection unit in real time, A detection unit that detects false information from the information analyzed by the aforementioned analysis unit, The system includes a presentation unit that presents correct factual information based on false information detected by the detection unit. A system characterized by the following features.

2. The aforementioned analysis unit is The collected information is analyzed to identify unreliable information. The system according to feature 1.

3. The aforementioned display unit is, Present reliable news articles and official announcements. The system according to feature 1.

4. The aforementioned analysis unit is Analyze opinions from multiple perspectives fairly and combine different viewpoints. The system according to feature 1.

5. The aforementioned display unit is, Present a combination of news articles and opinions from different perspectives. The system according to feature 1.

6. The aforementioned display unit is, Interactive Q&A with the generating AI provides users with relevant information. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Evaluate the reliability of the information sources to be collected and prioritize collecting from reliable sources. The system according to feature 1.

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