System

Abstract

To provide a system capable of confirming the reliability of information on the Internet and providing the same to a user.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a fact-checking unit, and a providing unit. The collection unit collects information on the Internet. The analysis unit analyzes the information collected by the collection unit. The fact-checking unit fact-checks the information analyzed by the analysis unit. The providing unit provides the information evaluated by the fact-checking unit to a user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had a problem in that they have not been able to efficiently check the reliability of information on the Internet and provide it to users.

[0005] The system according to the embodiment aims to verify the reliability of information on the Internet and provide it to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a fact check unit, and a provision unit. The collection unit collects information on the Internet. The analysis unit analyzes the information collected by the collection unit. The fact check unit fact checks the information analyzed by the analysis unit. The provision unit provides the information evaluated by the fact check unit to a user. Effect of the Invention

[0007] The system according to the embodiment can verify the reliability of information on the Internet and provide it to users. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The information provision system according to the embodiment of the present invention is a system that uses AI to perform fact checking and provide accurate information to users in order to counter deep fakes and misinformation. This information provision system is a mechanism that collects information on the Internet, analyzes it, performs fact checking, and provides accurate information to users. For example, the information provision system collects data from various information sources such as news articles, social media posts, and blogs. Next, the collected information is analyzed and important points are extracted. Furthermore, the collected information is compared with other reliable information sources to evaluate the accuracy of the information. Based on the evaluation result, reliable information is provided to the user. For example, reliable information is preferentially displayed on news sites and SNS. This mechanism can counter misinformation caused by malicious AI and present better options to users. For example, AI can detect deep fake videos and misinformation articles before they are spread and issue a warning to the user. In addition, AI can always collect and analyze the latest information, thereby providing accurate information to users in real time. This allows the information provision system to provide accurate information to users and counter misinformation and deep fakes.

[0029] The information providing system according to the embodiment includes a collection unit, an analysis unit, a fact check unit, and a provision unit. The collection unit collects information on the Internet. The collection unit can collect information such as news articles, social media posts, and blogs. The collection unit can automatically collect information on the Internet using web scraping technology. The collection unit can also use an API to obtain data from a specific information source. For example, the collection unit can collect the latest news articles using an RSS feed of a news site. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the collected information and extract important points. For example, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and identify grammatical elements such as a subject and a predicate. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. The fact check unit fact checks the information analyzed by the analysis unit. The fact check unit compares the collected information with other reliable information sources to evaluate the accuracy of the information. For example, the fact check unit can check whether the claims of the news article are consistent with other reliable news sites or official announcements. The fact check unit can also score the reliability of the information using an algorithm. The providing unit provides the information evaluated by the fact check unit to the user. The providing unit presents reliable information to the user based on the evaluation result. For example, the providing unit can preferentially display reliable information on a news site or SNS. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or a mobile app. As a result, the information providing system according to the embodiment can provide accurate information to the user and counter misinformation and deep fakes.

[0030] The collection unit collects information on the Internet. The collection unit can collect information such as news articles, social media posts, and blogs. The collection unit can automatically collect information on the Internet using web scraping technology. Specifically, by using web scraping technology, the HTML structure of a specific webpage is analyzed and necessary data is extracted. For example, information such as the headline, text, and posting date and time of a news site can be automatically acquired. The collection unit can also acquire data from a specific source using an API. For example, the collection unit can collect the latest news articles using the RSS feed of a news site. An RSS feed is data in XML format provided by a website and is a means of efficiently acquiring the latest content information. Furthermore, the collection unit can collect posts related to specific keywords and hashtags using the API of social media. This allows the collection unit to collect a wide range of data from various sources on the Internet and acquire information that is updated in real time. The collection unit centrally manages the collected data and stores it in a database for access by the analysis unit and fact check unit. The database is designed to enable efficient search and reference of collected information, and also has a data cleansing function to eliminate duplication and redundancy of information, allowing the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the contents of the collected information and extract important points. Specifically, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. Morphological analysis is a technology that identifies words and phrases that make up a sentence and specifies their parts of speech and meanings. This allows the analysis unit to understand the basic structure of a sentence and extract important keywords and phrases. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and specify grammatical elements such as subjects and predicates. Grammar analysis is a technology for clarifying grammatical relationships in a sentence and for understanding the meaning of the sentence more deeply. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. Semantic analysis is a technology that analyzes the context and meaning of a sentence and grasps the meaning of the entire sentence. This allows the analysis unit to efficiently extract information that is important to the user from the collected information. Furthermore, the analysis unit can use machine learning algorithms to classify and cluster the collected information. For example, news articles can be classified into categories, or articles with similar content can be grouped together, allowing the analyzer to efficiently organize the collected information and prepare it for presentation to users.

[0032] The fact-checking unit fact-checks the information analyzed by the analysis unit. The fact-checking unit compares the collected information with other reliable sources to evaluate the accuracy of the information. Specifically, the fact-checking unit can check whether the claims of a news article are consistent with other reliable news sites or official announcements. For example, it verifies whether the facts reported in a particular news article are consistent with official government announcements or information from reliable news organizations. The fact-checking unit can also score the reliability of information using an algorithm. The reliability scoring algorithm quantifies the reliability of information by taking into account the reliability of the source, the consistency of the information, the past reliability history, etc. This allows the fact-checking unit to identify reliable information from the collected information and prepare to provide it to the user. Furthermore, the fact-checking unit can also evaluate the reliability of images and videos using deep learning technology. For example, it can detect fake images and videos generated using deep fake technology and eliminate unreliable content. This allows the fact-checking unit to evaluate the reliability of not only text information but also multimedia content, thereby improving the quality of information provided to users.

[0033] The providing unit provides the user with the information evaluated by the fact-checking unit. The providing unit presents reliable information to the user based on the evaluation result. Specifically, the providing unit can preferentially display reliable information on news sites and SNS. For example, on a news site, articles with a high reliability score are displayed on the top page, making it easier for users to access reliable information. In addition, on an SNS, reliable posts are preferentially displayed, reducing the risk that users will be misled by false information. Furthermore, the providing unit can also provide information to users through a user interface. For example, the providing unit can provide information to users through a website or mobile app. On a website or mobile app, customized information can be provided based on categories or topics in which the user is interested. This allows users to efficiently obtain information that is important to them. Furthermore, the providing unit can collect feedback from users and continuously improve the quality of the information provided. For example, by a user rating or commenting on the provided information, the providing unit can evaluate the reliability and usefulness of the information and identify areas for improvement. This allows the providing unit to always provide high-quality information to users and counter false reports and deep fakes.

[0034] The collection unit can collect information on news articles, social media posts, and blogs. For example, the collection unit can collect news articles from online news sites. The collection unit can automatically collect the latest news articles using RSS feeds. The collection unit can also collect social media posts. The collection unit can use an API to obtain data from social media such as X (formerly Twitter (registered trademark)) and Facebook (registered trademark). For example, the collection unit can collect posts related to specific hashtags and keywords. Furthermore, the collection unit can collect blog information. The collection unit can collect data from personal blogs and corporate blogs using web scraping technology. For example, the collection unit can collect the latest posts from a specific blog site. This allows the collection unit to collect information from a variety of sources. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input news articles and social media posts to a generation AI and cause the generation AI to collect information.

[0035] The analysis unit can analyze the collected information using natural language processing technology and extract important points. The analysis unit can divide the collected information into words and analyze the meaning of each word using, for example, morphological analysis. For example, the analysis unit can perform morphological analysis on the sentences of a news article and identify grammatical elements such as subjects and predicates. The analysis unit can also analyze the structure of the sentence using grammatical analysis. For example, the analysis unit can analyze the grammatical structure of a news article and identify the assertions and evidence of the sentence. Furthermore, the analysis unit can understand the meaning of the sentence and extract important information using semantic analysis. For example, the analysis unit can analyze the meaning of a news article and extract the assertions and evidence of the article. This allows the analysis unit to understand the content of the collected information and extract important points. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information to a generation AI and have the generation AI execute analysis of the information.

[0036] The fact check unit can compare the collected information with other reliable sources and evaluate the accuracy of the information. The fact check unit can, for example, check whether the claims of the news article match other reliable news sites or official announcements. For example, the fact check unit can compare the claims of the news article with information from other news sites and evaluate whether they match. The fact check unit can also score the reliability of the information using an algorithm. For example, the fact check unit can score the reliability of the news article and identify reliable information. Furthermore, the fact check unit can compare information from multiple sources using cross-reference technology and evaluate the accuracy of the information. For example, the fact check unit can compare the claims of the news article with multiple sources and evaluate whether they match. This allows the fact check unit to evaluate the accuracy of the collected information and identify reliable information. Some or all of the above-mentioned processing in the fact check unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the fact check unit can input the collected information into a generation AI and have the generation AI evaluate the accuracy of the information.

[0037] The providing unit can present reliable information to the user based on the evaluation result. The providing unit can preferentially display reliable information on, for example, a news site or a social networking site. For example, the providing unit can display reliable news articles evaluated by the fact check unit on the top page of a news site. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or a mobile app. Furthermore, the providing unit can notify the user of important information using a notification function. For example, the providing unit can push-notify the reliable information to the user's smartphone. Thereby, the providing unit can provide reliable information to the user, and the user can obtain accurate information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the information evaluated by the fact check unit to the generation AI, and cause the generation AI to execute the provision of information.

[0038] The collection unit can analyze the user's past browsing history and select an information collection method. The collection unit can, for example, preferentially collect information sources that the user frequently viewed in the past. For example, the collection unit can analyze the user's past browsing history and preferentially collect news sites and blogs that the user frequently viewed. The collection unit can also preferentially collect information related to a specific topic from the user's past browsing history. For example, the collection unit can preferentially collect news articles and social media posts related to topics that the user was interested in in the past. Furthermore, the collection unit can analyze the user's past browsing history and select an optimal information collection time. For example, the collection unit can collect information according to a time period in which the user frequently viewed information in the past. This allows the collection unit to analyze the user's past browsing history and collect information more effectively. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past browsing history data to the generation AI and cause the generation AI to select an information collection method.

[0039] The collection unit can perform filtering based on the user's current field of interest when collecting information. The collection unit can, for example, preferentially collect information related to topics in which the user is currently interested. For example, the collection unit can analyze the user's current field of interest and preferentially collect related news articles and social media posts. The collection unit can also filter unnecessary information based on the user's current field of interest. For example, the collection unit can exclude information that is not related to the user's current field of interest and collect only related information. Furthermore, the collection unit can analyze the user's current field of interest in real time and collect optimal information. For example, the collection unit can analyze the user's current search history and social media activities and collect information related to the field of interest in real time. Thereby, the collection unit can filter information based on the user's current field of interest and collect more relevant information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's current field of interest data to the generation AI and cause the generation AI to perform filtering of information.

[0040] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting information. The collection unit can, for example, preferentially collect news articles related to the user's current location. For example, the collection unit can preferentially collect news articles and event information related to the current location based on the geographical location information of the user. The collection unit can also preferentially collect local event information based on the geographical location information of the user. For example, the collection unit can preferentially collect local event and emergency information based on the geographical location information of the user. Furthermore, the collection unit can also preferentially collect emergency information in consideration of the geographical location information of the user. For example, the collection unit can preferentially collect information related to an emergency based on the geographical location information of the user. Thereby, the collection unit can collect more relevant information in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0041] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can, for example, preferentially collect posts from accounts that the user follows on social media. For example, the collection unit can analyze the user's social media activity and preferentially collect posts from accounts that the user follows. The collection unit can also collect information related to topics of interest from the user's social media activity. For example, the collection unit can analyze the content of the user's social media posts and the number of likes and shares, and collect information related to topics of interest. Furthermore, the collection unit can analyze the user's social media activity and select an optimal information collection method. For example, the collection unit can analyze the user's social media activity and select an optimal information collection time and method. This allows the collection unit to analyze the user's social media activity and collect more relevant information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute collection of related information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The analysis unit can perform a detailed analysis on important information, for example. For example, the analysis unit can evaluate the importance of a news article and perform a detailed data analysis on an important article. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit can evaluate the importance of a news article and provide a simplified summary for an article with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. For example, the analysis unit can evaluate the importance of a news article and prioritize the analysis of important articles. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information, enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply a natural language processing algorithm to, for example, a news article. For example, the analysis unit can apply a natural language processing algorithm such as morphological analysis or grammatical analysis to analyze the content of a news article. The analysis unit can also apply a sentiment analysis algorithm to a social media post. For example, the analysis unit can apply a sentiment analysis algorithm to analyze the content of a social media post and evaluate the emotional nuance of the post. Furthermore, the analysis unit can apply a topic modeling algorithm to a blog post. For example, the analysis unit can apply a topic modeling algorithm to analyze the content of a blog post and extract the main topic of the article. This allows the analysis unit to apply different analysis algorithms depending on the category of information, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the time when the information was collected during the analysis. The analysis unit can, for example, prioritize the analysis of the latest information. For example, the analysis unit can evaluate the time when the information was collected and prioritize the analysis of the latest news articles and social media posts. The analysis unit can also perform a simplified analysis on old information. For example, the analysis unit can evaluate the time when the information was collected and provide a simplified summary for old news articles and blog posts. The analysis unit can also determine the priority of the analysis based on the time when the information was collected. For example, the analysis unit can evaluate the time when the information was collected and prioritize the analysis of the latest information. This allows the analysis unit to determine the priority of the analysis based on the time when the information was collected, making it possible to perform more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during analysis. The analysis unit can, for example, preferentially analyze highly relevant information. For example, the analysis unit can evaluate the relevance of the information and preferentially analyze highly relevant news articles and social media posts. The analysis unit can also perform a simplified analysis for less relevant information. For example, the analysis unit can evaluate the relevance of the information and provide a simplified summary for less relevant news articles and blog posts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and preferentially analyze highly relevant information. This allows the analysis unit to adjust the order of analysis based on the relevance of the information, enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the information to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The fact check unit can improve the accuracy of the check by considering the interrelationship of information during the fact check. The fact check unit can, for example, compare information from multiple information sources and check the interrelationship. For example, the fact check unit can compare the assertion of a news article with the information of multiple news sites and evaluate the interrelationship. The fact check unit can also improve the accuracy of the fact check based on the interrelationship of information. For example, the fact check unit can evaluate the degree of agreement between the assertion of a news article and the assertion of other information sources and improve the accuracy of the fact check. Furthermore, the fact check unit can also improve the accuracy of detection of misinformation by considering the interrelationship of information. For example, the fact check unit can evaluate the interrelationship of the assertion of a news article and the assertion of other information sources and improve the accuracy of detection of misinformation. Thereby, the fact check unit can improve the accuracy of the check by considering the interrelationship of information. Part or all of the above-mentioned processing in the fact check unit may be performed, for example, using AI or may be performed without using AI. For example, the fact check unit can input the interrelationship data of information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the check.

[0047] The fact check unit can perform a fact check while taking into consideration the attribute information of the information provider. For example, the fact check unit can evaluate the reliability of the information provider and adjust the criteria for the fact check. For example, the fact check unit can evaluate the past history of the information provider and preferentially check the information of the highly reliable provider. The fact check unit can also improve the accuracy of the fact check by taking into consideration the past history of the information provider. For example, the fact check unit can evaluate the past history of the information provider and strictly check the information of the less reliable provider. Furthermore, the fact check unit can improve the accuracy of detecting false information based on the attribute information of the information provider. For example, the fact check unit can evaluate the expertise and reliability of the information provider and improve the accuracy of detecting false information. As a result, the fact check unit can perform a check while taking into consideration the attribute information of the information provider and improve the accuracy of the fact check. A part or all of the above-mentioned processing in the fact check unit may be performed using, for example, AI, or may be performed without using AI. For example, the fact check unit can input attribute information data of the information provider to the generation AI and cause the generation AI to execute the check.

[0048] The fact check unit can perform a fact check while taking into consideration the geographical distribution of information. The fact check unit can adjust the criteria for the fact check based on the geographical distribution of information, for example. For example, the fact check unit can evaluate the geographical distribution of news articles and prioritize checking information for each region. The fact check unit can also prioritize fact checks on geographically related information. For example, the fact check unit can evaluate the geographical distribution of news articles and prioritize checking events and emergency information for each region. Furthermore, the fact check unit can also improve the accuracy of detecting false information by taking into consideration the geographical distribution of information. For example, the fact check unit can evaluate the geographical distribution of news articles and improve the accuracy of detecting false information for each region. Thereby, the fact check unit can perform a check while taking into consideration the geographical distribution of information and improve the accuracy of the fact check. A part or all of the above-mentioned processing in the fact check unit may be performed using, for example, AI, or may be performed without using AI. For example, the fact check unit can input geographical distribution data of information to the generation AI and cause the generation AI to perform the check.

[0049] The fact check unit can improve the accuracy of the check by referring to related literature of the information during the fact check. The fact check unit can, for example, evaluate the accuracy of the information by referring to related literature. For example, the fact check unit can compare the assertion of the news article with academic papers or reliable news articles and evaluate the accuracy. The fact check unit can also adjust the criteria of the fact check based on the related literature. For example, the fact check unit can compare the assertion of the news article with related literature and adjust the criteria. Furthermore, the fact check unit can also improve the accuracy of detecting false information by referring to related literature. For example, the fact check unit can compare the assertion of the news article with related literature and improve the accuracy of detecting false information. Thereby, the fact check unit can improve the accuracy of the check by referring to related literature of the information. Part or all of the above-mentioned processing in the fact check unit may be performed, for example, using AI, or may be performed without using AI. For example, the fact check unit can input related literature data to the generation AI and have the generation AI perform the check.

[0050] The providing unit can select a providing method by referring to the user's past browsing history when providing information. The providing unit can, for example, provide preferentially information sources that the user frequently viewed in the past. For example, the providing unit can provide preferentially news sites and blogs that the user frequently viewed by referring to the user's past browsing history. The providing unit can also provide preferentially information related to a specific topic from the user's past browsing history. For example, the providing unit can provide preferentially news articles and social media posts related to topics of interest by referring to the user's past browsing history. Furthermore, the providing unit can also select an optimal information providing time by referring to the user's past browsing history. For example, the providing unit can provide information according to a time period in which the user frequently viewed information by referring to the user's past browsing history. This allows the providing unit to provide information more effectively by referring to the user's past browsing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past browsing history data to the generating AI and cause the generating AI to select the information providing method.

[0051] The providing unit can customize the content to be provided based on the user's current field of interest when providing information. The providing unit can, for example, preferentially provide information related to topics in which the user is currently interested. For example, the providing unit can analyze the user's current field of interest and preferentially provide related news articles and social media posts. The providing unit can also filter unnecessary information based on the user's current field of interest. For example, the providing unit can exclude information that is not related to the user's current field of interest and provide only related information. Furthermore, the providing unit can analyze the user's current field of interest in real time and provide optimal information. For example, the providing unit can analyze the user's current search history and social media activities and provide information related to the field of interest in real time. Thereby, the providing unit can customize information based on the user's current field of interest and provide more relevant information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's current field of interest data to the generating AI and cause the generating AI to perform the customization of the information.

[0052] The providing unit can select a providing method in consideration of the geographical location information of the user when providing information. The providing unit can, for example, preferentially provide news articles related to the user's current location. For example, the providing unit can preferentially provide news articles and event information related to the current location based on the geographical location information of the user. The providing unit can also preferentially provide local event information based on the geographical location information of the user. For example, the providing unit can preferentially provide local event and emergency information based on the geographical location information of the user. Furthermore, the providing unit can also preferentially provide emergency information in consideration of the geographical location information of the user. For example, the providing unit can preferentially provide information related to an emergency based on the geographical location information of the user. Thereby, the providing unit can provide more relevant information in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to execute the selection of the providing method.

[0053] The providing unit can adjust the content to be provided by analyzing the user's social media activity when providing information. The providing unit can, for example, preferentially provide posts from accounts that the user follows on social media. For example, the providing unit can analyze the user's social media activity and preferentially provide posts from the accounts that the user follows. The providing unit can also provide information related to topics of interest from the user's social media activity. For example, the providing unit can analyze the content of posts, likes, and shares of the user's social media, and provide information related to topics of interest. Furthermore, the providing unit can analyze the user's social media activity and select an optimal information providing method. For example, the providing unit can analyze the user's social media activity and select an optimal information providing time and method. This allows the providing unit to analyze the user's social media activity and provide more relevant information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to adjust the content to be provided.

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

[0055] The information providing system may further include a history analysis unit that analyzes the user's past search history. The history analysis unit may analyze keywords searched for by the user in the past and websites visited by the user to identify the user's interests. For example, the history analysis unit may identify topics frequently searched for by the user and preferentially collect the latest news articles and blog posts related to the topics. The history analysis unit may also analyze categories of websites visited by the user in the past and preferentially collect information of similar categories. Furthermore, the history analysis unit may find specific patterns from the user's search history and adjust the timing and frequency of information collection based on the patterns. This allows the information providing system to provide more personalized information based on the user's past behavior.

[0056] The collection unit can further monitor the usage of the user's device and optimize the timing of information collection. For example, the collection unit can identify the time period during which the user frequently uses the device and collect information according to that time period. The collection unit can also collect information during the time period during which the user does not use the device and provide the latest information when the user starts using the device again. Furthermore, the collection unit can monitor the remaining battery level of the user's device and reduce the frequency of information collection when the battery is low. This allows the collection unit to adjust the timing of information collection according to the usage of the user's device and provide information more efficiently.

[0057] The collection unit can further monitor the usage of the user's device and optimize the timing of information collection. For example, the collection unit can identify the time period during which the user frequently uses the device and collect information according to that time period. The collection unit can also collect information during the time period during which the user does not use the device and provide the latest information when the user starts using the device again. Furthermore, the collection unit can monitor the remaining battery level of the user's device and reduce the frequency of information collection when the battery is low. This allows the collection unit to adjust the timing of information collection according to the usage of the user's device and provide information more efficiently.

[0058] The analysis unit may further include a reliability evaluation unit that evaluates the reliability of the information. The reliability evaluation unit may evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the reliability evaluation unit may evaluate the reliability of news articles and prioritize analysis of highly reliable articles. The reliability evaluation unit may also evaluate the reliability of social media posts and prioritize analysis of highly reliable posts. The reliability evaluation unit may also evaluate the reliability of blog articles and prioritize analysis of highly reliable articles. This allows the analysis unit to prioritize analysis of highly reliable information and provide more accurate analysis results.

[0059] The fact check unit can further perform a check while taking into consideration the attribute information of the information provider. For example, the fact check unit can evaluate the reliability of the information provider and adjust the criteria for the fact check. For example, the fact check unit can evaluate the past history of the information provider and preferentially check the information of the highly reliable provider. The fact check unit can also improve the accuracy of the fact check while taking into consideration the past history of the information provider. For example, the fact check unit can evaluate the past history of the information provider and strictly check the information of the less reliable provider. Furthermore, the fact check unit can also improve the accuracy of detecting false information based on the attribute information of the information provider. For example, the fact check unit can evaluate the expertise and reliability of the information provider and improve the accuracy of detecting false information. As a result, the fact check unit can perform a check while taking into consideration the attribute information of the information provider and improve the accuracy of the fact check.

[0060] The providing unit can further analyze the user's social media activity to adjust the content to be provided. For example, the providing unit can preferentially provide posts from accounts that the user follows on social media. For example, the providing unit can analyze the user's social media activity and preferentially provide posts from accounts that the user follows. The providing unit can also provide information related to topics of interest from the user's social media activity. For example, the providing unit can analyze the content of posts, likes, and the number of shares on the user's social media, and provide information related to topics of interest. Furthermore, the providing unit can analyze the user's social media activity and select an optimal information provision method. For example, the providing unit can analyze the user's social media activity and select an optimal information provision time and method. This allows the providing unit to analyze the user's social media activity and provide more relevant information.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects information on the Internet. The collection unit may collect information such as news articles, social media posts, and blogs. The collection unit may use web scraping technology to automatically collect information on the Internet. The collection unit may also use an API to obtain data from a specific source. For example, the collection unit may use an RSS feed from a news site to collect the latest news articles. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the collected information and extract important points. For example, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and identify grammatical elements such as subjects and predicates. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. Step 3: The fact-checking department fact-checks the information analyzed by the analysis department. The fact-checking department compares the collected information with other reliable sources and evaluates the accuracy of the information. For example, the fact-checking department can check whether the claims in a news article are consistent with other reliable news sites or official announcements. The fact-checking department can also use an algorithm to score the reliability of the information. Step 4: The providing unit provides the information evaluated by the fact-checking unit to the user. The providing unit presents reliable information to the user based on the evaluation results. For example, the providing unit can preferentially display reliable information on a news site or SNS. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or mobile app.

[0063] (Example 2) The information provision system according to the embodiment of the present invention is a system that uses AI to perform fact checking and provide accurate information to users in order to counter deep fakes and misinformation. This information provision system is a mechanism that collects information on the Internet, analyzes it, performs fact checking, and provides accurate information to users. For example, the information provision system collects data from various information sources such as news articles, social media posts, and blogs. Next, the collected information is analyzed and important points are extracted. Furthermore, the collected information is compared with other reliable information sources to evaluate the accuracy of the information. Based on the evaluation result, reliable information is provided to the user. For example, reliable information is preferentially displayed on news sites and SNS. This mechanism can counter misinformation caused by malicious AI and present better options to users. For example, AI can detect deep fake videos and misinformation articles before they are spread and issue a warning to the user. In addition, AI can always collect and analyze the latest information, thereby providing accurate information to users in real time. This allows the information provision system to provide accurate information to users and counter misinformation and deep fakes.

[0064] The information providing system according to the embodiment includes a collection unit, an analysis unit, a fact check unit, and a provision unit. The collection unit collects information on the Internet. The collection unit can collect information such as news articles, social media posts, and blogs. The collection unit can automatically collect information on the Internet using web scraping technology. The collection unit can also use an API to obtain data from a specific information source. For example, the collection unit can collect the latest news articles using an RSS feed of a news site. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the collected information and extract important points. For example, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and identify grammatical elements such as a subject and a predicate. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. The fact check unit fact checks the information analyzed by the analysis unit. The fact check unit compares the collected information with other reliable information sources to evaluate the accuracy of the information. For example, the fact check unit can check whether the claims of the news article are consistent with other reliable news sites or official announcements. The fact check unit can also score the reliability of the information using an algorithm. The providing unit provides the information evaluated by the fact check unit to the user. The providing unit presents reliable information to the user based on the evaluation result. For example, the providing unit can preferentially display reliable information on a news site or SNS. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or a mobile app. As a result, the information providing system according to the embodiment can provide accurate information to the user and counter misinformation and deep fakes.

[0065] The collection unit collects information on the Internet. The collection unit can collect information such as news articles, social media posts, and blogs. The collection unit can automatically collect information on the Internet using web scraping technology. Specifically, by using web scraping technology, the HTML structure of a specific webpage is analyzed and necessary data is extracted. For example, information such as the headline, main text, and posting date and time of a news site can be automatically acquired. The collection unit can also acquire data from a specific source using an API. For example, the collection unit can collect the latest news articles using the RSS feed of a news site. An RSS feed is data in XML format provided by a website and is a means of efficiently acquiring the latest content information. Furthermore, the collection unit can collect posts related to specific keywords and hashtags using the API of social media. This allows the collection unit to collect a wide range of data from various sources on the Internet and acquire information that is updated in real time. The collection unit centrally manages the collected data and stores it in a database for access by the analysis unit and fact check unit. The database is designed to enable efficient search and reference of collected information, and also has a data cleansing function to eliminate duplication and redundancy of information, allowing the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the contents of the collected information and extract important points. Specifically, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. Morphological analysis is a technology that identifies words and phrases that make up a sentence and specifies their parts of speech and meanings. This allows the analysis unit to understand the basic structure of a sentence and extract important keywords and phrases. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and specify grammatical elements such as subjects and predicates. Grammar analysis is a technology for clarifying grammatical relationships in a sentence and for understanding the meaning of the sentence more deeply. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. Semantic analysis is a technology that analyzes the context and meaning of a sentence and grasps the meaning of the entire sentence. This allows the analysis unit to efficiently extract information that is important to the user from the collected information. Furthermore, the analysis unit can use machine learning algorithms to classify and cluster the collected information. For example, news articles can be classified into categories, or articles with similar content can be grouped together, allowing the analyzer to efficiently organize the collected information and prepare it for presentation to users.

[0067] The fact-checking unit fact-checks the information analyzed by the analysis unit. The fact-checking unit compares the collected information with other reliable sources to evaluate the accuracy of the information. Specifically, the fact-checking unit can check whether the claims of a news article are consistent with other reliable news sites or official announcements. For example, it verifies whether the facts reported in a particular news article are consistent with official government announcements or information from reliable news organizations. The fact-checking unit can also score the reliability of information using an algorithm. The reliability scoring algorithm quantifies the reliability of information by taking into account the reliability of the source, the consistency of the information, the past reliability history, etc. This allows the fact-checking unit to identify reliable information from the collected information and prepare to provide it to the user. Furthermore, the fact-checking unit can also evaluate the reliability of images and videos using deep learning technology. For example, it can detect fake images and videos generated using deep fake technology and eliminate unreliable content. This allows the fact-checking unit to evaluate the reliability of not only text information but also multimedia content, thereby improving the quality of information provided to users.

[0068] The providing unit provides the user with the information evaluated by the fact-checking unit. The providing unit presents reliable information to the user based on the evaluation result. Specifically, the providing unit can preferentially display reliable information on news sites and SNS. For example, on a news site, articles with a high reliability score are displayed on the top page, making it easier for users to access reliable information. In addition, on an SNS, reliable posts are preferentially displayed, reducing the risk that users will be misled by false information. Furthermore, the providing unit can also provide information to users through a user interface. For example, the providing unit can provide information to users through a website or mobile app. On a website or mobile app, customized information can be provided based on categories or topics in which the user is interested. This allows users to efficiently obtain information that is important to them. Furthermore, the providing unit can collect feedback from users and continuously improve the quality of the information provided. For example, by a user rating or commenting on the provided information, the providing unit can evaluate the reliability and usefulness of the information and identify areas for improvement. This allows the providing unit to always provide high-quality information to users and counter false reports and deep fakes.

[0069] The collection unit can collect information on news articles, social media posts, and blogs. For example, the collection unit can collect news articles from online news sites. The collection unit can automatically collect the latest news articles using an RSS feed. The collection unit can also collect social media posts. The collection unit can use an API to obtain data from social media such as X (formerly Twitter) and Facebook. For example, the collection unit can collect posts related to a specific hashtag or keyword. Furthermore, the collection unit can collect blog information. The collection unit can collect data from personal blogs and corporate blogs using web scraping technology. For example, the collection unit can collect the latest posts from a specific blog site. This allows the collection unit to collect information from a variety of sources. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input news articles and social media posts to a generation AI and cause the generation AI to collect information.

[0070] The analysis unit can analyze the collected information using natural language processing technology and extract important points. The analysis unit can, for example, use morphological analysis to divide the collected information into words and analyze the meaning of each word. For example, the analysis unit can perform morphological analysis on the sentences of a news article and identify grammatical elements such as subjects and predicates. The analysis unit can also analyze the structure of the sentence using grammatical analysis. For example, the analysis unit can analyze the grammatical structure of a news article and identify the assertions and evidence of the sentence. Furthermore, the analysis unit can also understand the meaning of the sentence and extract important information using semantic analysis. For example, the analysis unit can analyze the meaning of a news article and extract the assertions and evidence of the article. This allows the analysis unit to understand the content of the collected information and extract important points. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information to a generation AI and have the generation AI execute analysis of the information.

[0071] The fact check unit can compare the collected information with other reliable sources and evaluate the accuracy of the information. The fact check unit can, for example, check whether the claims of the news article match other reliable news sites or official announcements. For example, the fact check unit can compare the claims of the news article with information from other news sites and evaluate whether they match. The fact check unit can also score the reliability of the information using an algorithm. For example, the fact check unit can score the reliability of the news article and identify reliable information. Furthermore, the fact check unit can compare information from multiple sources using cross-reference technology and evaluate the accuracy of the information. For example, the fact check unit can compare the claims of the news article with multiple sources and evaluate whether they match. This allows the fact check unit to evaluate the accuracy of the collected information and identify reliable information. Some or all of the above-mentioned processing in the fact check unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the fact check unit can input the collected information into a generation AI and have the generation AI evaluate the accuracy of the information.

[0072] The providing unit can present reliable information to the user based on the evaluation result. The providing unit can preferentially display reliable information on, for example, a news site or a social networking site. For example, the providing unit can display reliable news articles evaluated by the fact check unit on the top page of a news site. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or a mobile app. Furthermore, the providing unit can notify the user of important information using a notification function. For example, the providing unit can push-notify the reliable information to the user's smartphone. Thereby, the providing unit can provide reliable information to the user, and the user can obtain accurate information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the information evaluated by the fact check unit to the generation AI, and cause the generation AI to execute the provision of information.

[0073] The collection unit can estimate the user's emotions and determine the timing of information collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of information collection and collect only important information. For example, the collection unit can estimate the user's emotions and, when the user is feeling stressed, reduce the frequency of information collection and collect only important news articles. In addition, the collection unit can increase the frequency of information collection and collect detailed information when the user is relaxed. For example, the collection unit can estimate the user's emotions and, when the user is relaxed, increase the frequency of information collection and collect detailed news articles and blog posts. Furthermore, the collection unit can collect information in real time and provide it quickly when the user is in a hurry. For example, the collection unit can estimate the user's emotions and, when the user is in a hurry, collect the latest news articles in real time and provide them quickly. This allows the collection unit to adjust the timing of information collection according to the user's emotions, making it possible to collect more appropriate information. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to execute the timing of information collection.

[0074] The collection unit can analyze the user's past browsing history and select an information collection method. The collection unit can, for example, preferentially collect information sources that the user frequently viewed in the past. For example, the collection unit can analyze the user's past browsing history and preferentially collect news sites and blogs that the user frequently viewed. The collection unit can also preferentially collect information related to a specific topic from the user's past browsing history. For example, the collection unit can preferentially collect news articles and social media posts related to topics that the user was interested in in the past. Furthermore, the collection unit can analyze the user's past browsing history and select an optimal information collection time. For example, the collection unit can collect information according to a time period in which the user frequently viewed information in the past. This allows the collection unit to analyze the user's past browsing history and collect information more effectively. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past browsing history data to the generation AI and cause the generation AI to select an information collection method.

[0075] The collection unit can perform filtering based on the user's current field of interest when collecting information. The collection unit can, for example, preferentially collect information related to topics in which the user is currently interested. For example, the collection unit can analyze the user's current field of interest and preferentially collect related news articles and social media posts. The collection unit can also filter unnecessary information based on the user's current field of interest. For example, the collection unit can exclude information that is not related to the user's current field of interest and collect only related information. Furthermore, the collection unit can analyze the user's current field of interest in real time and collect optimal information. For example, the collection unit can analyze the user's current search history and social media activities and collect information related to the field of interest in real time. Thereby, the collection unit can filter information based on the user's current field of interest and collect more relevant information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's current field of interest data to the generation AI and cause the generation AI to perform filtering of information.

[0076] The collection unit can estimate the user's emotion and determine the order of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can preferentially collect only important information. For example, the collection unit can estimate the user's emotion and preferentially collect important news articles and official announcements when the user is feeling stressed. The collection unit can also preferentially collect detailed information when the user is relaxed. For example, the collection unit can estimate the user's emotion and preferentially collect detailed news articles and blog posts when the user is relaxed. Furthermore, the collection unit can also preferentially collect important information in real time when the user is in a hurry. For example, the collection unit can estimate the user's emotion and preferentially collect the latest news articles in real time when the user is in a hurry. Thereby, the collection unit can determine the priority of information according to the user's emotion and preferentially collect more important information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data to the generation AI and have the generation AI determine the priority order of information.

[0077] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting information. The collection unit can, for example, preferentially collect news articles related to the user's current location. For example, the collection unit can preferentially collect news articles and event information related to the current location based on the geographical location information of the user. The collection unit can also preferentially collect local event information based on the geographical location information of the user. For example, the collection unit can preferentially collect local event and emergency information based on the geographical location information of the user. Furthermore, the collection unit can also preferentially collect emergency information in consideration of the geographical location information of the user. For example, the collection unit can preferentially collect information related to an emergency based on the geographical location information of the user. Thereby, the collection unit can collect more relevant information in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0078] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can, for example, preferentially collect posts from accounts that the user follows on social media. For example, the collection unit can analyze the user's social media activity and preferentially collect posts from accounts that the user follows. The collection unit can also collect information related to topics of interest from the user's social media activity. For example, the collection unit can analyze the content of the user's social media posts and the number of likes and shares, and collect information related to topics of interest. Furthermore, the collection unit can analyze the user's social media activity and select an optimal information collection method. For example, the collection unit can analyze the user's social media activity and select an optimal information collection time and method. This allows the collection unit to analyze the user's social media activity and collect more relevant information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute collection of related information.

[0079] The analysis unit can estimate the user's emotion and determine the method of expressing the analysis based on the estimated user's emotion. The analysis unit can provide a simple and highly visible analysis result when the user is nervous, for example. For example, the analysis unit can estimate the user's emotion and provide the analysis result using a simple and highly visible graph or chart when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotion and provide the analysis result using a detailed data analysis or text summary when the user is relaxed. Furthermore, the analysis unit can also provide an analysis result that covers the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotion and provide a concise analysis result that covers the main points when the user is in a hurry. This allows the analysis unit to adjust the method of expressing the analysis according to the user's emotion and provide a more appropriate analysis result. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and have the generation AI determine the method of expressing the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The analysis unit can perform a detailed analysis on important information, for example. For example, the analysis unit can evaluate the importance of a news article and perform a detailed data analysis on an important article. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit can evaluate the importance of a news article and provide a simplified summary for an article with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. For example, the analysis unit can evaluate the importance of a news article and prioritize the analysis of important articles. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information, enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply a natural language processing algorithm to, for example, a news article. For example, the analysis unit can apply a natural language processing algorithm such as morphological analysis or grammatical analysis to analyze the content of a news article. The analysis unit can also apply a sentiment analysis algorithm to a social media post. For example, the analysis unit can apply a sentiment analysis algorithm to analyze the content of a social media post and evaluate the emotional nuance of the post. Furthermore, the analysis unit can apply a topic modeling algorithm to a blog post. For example, the analysis unit can apply a topic modeling algorithm to analyze the content of a blog post and extract the main topic of the article. This allows the analysis unit to apply different analysis algorithms depending on the category of information, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0082] The analysis unit can estimate the user's emotion and determine the length of the analysis based on the estimated user's emotion. The analysis unit can provide a short and to the point analysis result when the user is in a hurry, for example. For example, the analysis unit can estimate the user's emotion and provide a short and to the point concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotion and provide an analysis result using detailed data analysis or text summary when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit can estimate the user's emotion and provide an analysis result using visually stimulating graphs or charts when the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotion and provide a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and have the generation AI determine the length of the analysis.

[0083] The analysis unit can determine the priority of the analysis based on the time when the information was collected during the analysis. The analysis unit can, for example, prioritize the analysis of the latest information. For example, the analysis unit can evaluate the time when the information was collected and prioritize the analysis of the latest news articles and social media posts. The analysis unit can also perform a simplified analysis on old information. For example, the analysis unit can evaluate the time when the information was collected and provide a simplified summary for old news articles and blog posts. The analysis unit can also determine the priority of the analysis based on the time when the information was collected. For example, the analysis unit can evaluate the time when the information was collected and prioritize the analysis of the latest information. This allows the analysis unit to determine the priority of the analysis based on the time when the information was collected, making it possible to perform more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the information during analysis. The analysis unit can, for example, preferentially analyze highly relevant information. For example, the analysis unit can evaluate the relevance of the information and preferentially analyze highly relevant news articles and social media posts. The analysis unit can also perform a simplified analysis for less relevant information. For example, the analysis unit can evaluate the relevance of the information and provide a simplified summary for less relevant news articles and blog posts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and preferentially analyze highly relevant information. This allows the analysis unit to adjust the order of analysis based on the relevance of the information, enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the information to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] The fact check unit can estimate the user's emotions and determine the criteria for fact check based on the estimated user's emotions. For example, the fact check unit can apply strict fact check criteria when the user is nervous. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information with strict criteria when the user is nervous. The fact check unit can also apply flexible fact check criteria when the user is relaxed. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information with flexible criteria when the user is relaxed. Furthermore, the fact check unit can also perform quick fact check when the user is in a hurry. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information quickly when the user is in a hurry. This allows the fact check unit to adjust the criteria for fact check according to the user's emotions, enabling more appropriate fact check. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the fact-checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the fact-checking unit may input the user's emotion data to the generation AI and have the generation AI determine the criteria for the fact-checking.

[0086] The fact check unit can improve the accuracy of the check by considering the interrelationship of information during the fact check. The fact check unit can, for example, compare information from multiple information sources and check the interrelationship. For example, the fact check unit can compare the assertion of a news article with the information of multiple news sites and evaluate the interrelationship. The fact check unit can also improve the accuracy of the fact check based on the interrelationship of information. For example, the fact check unit can evaluate the degree of agreement between the assertion of a news article and the assertion of other information sources and improve the accuracy of the fact check. Furthermore, the fact check unit can also improve the accuracy of detection of misinformation by considering the interrelationship of information. For example, the fact check unit can evaluate the interrelationship of the assertion of a news article and the assertion of other information sources and improve the accuracy of detection of misinformation. Thereby, the fact check unit can improve the accuracy of the check by considering the interrelationship of information. Part or all of the above-mentioned processing in the fact check unit may be performed, for example, using AI or may be performed without using AI. For example, the fact check unit can input the interrelationship data of information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the check.

[0087] The fact check unit can perform a fact check while taking into consideration the attribute information of the information provider. For example, the fact check unit can evaluate the reliability of the information provider and adjust the criteria for the fact check. For example, the fact check unit can evaluate the past history of the information provider and preferentially check the information of the highly reliable provider. The fact check unit can also improve the accuracy of the fact check by taking into consideration the past history of the information provider. For example, the fact check unit can evaluate the past history of the information provider and strictly check the information of the less reliable provider. Furthermore, the fact check unit can improve the accuracy of detecting false information based on the attribute information of the information provider. For example, the fact check unit can evaluate the expertise and reliability of the information provider and improve the accuracy of detecting false information. As a result, the fact check unit can perform a check while taking into consideration the attribute information of the information provider and improve the accuracy of the fact check. A part or all of the above-mentioned processing in the fact check unit may be performed using, for example, AI, or may be performed without using AI. For example, the fact check unit can input attribute information data of the information provider to the generation AI and cause the generation AI to execute the check.

[0088] The fact check unit can estimate the user's emotions and determine the order of displaying the fact check results based on the estimated user's emotions. For example, when the user is nervous, the fact check unit can preferentially display important fact check results. For example, the fact check unit can estimate the user's emotions and preferentially display fact check results of important news articles when the user is nervous. In addition, the fact check unit can also display detailed fact check results when the user is relaxed. For example, the fact check unit can estimate the user's emotions and display fact check results using detailed data analysis or text summaries when the user is relaxed. Furthermore, the fact check unit can also preferentially display fact check results that capture the main points when the user is in a hurry. For example, the fact check unit can estimate the user's emotions and display concise fact check results that capture the main points when the user is in a hurry. This allows the fact check unit to adjust the order of displaying the fact check results according to the user's emotions, making it possible to provide more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the fact check unit may be performed, for example, using AI, or may be performed without using AI. For example, the fact check unit may input user emotion data to the generation AI and cause the generation AI to determine the display order of the fact check results.

[0089] The fact check unit can perform a fact check while taking into consideration the geographical distribution of information. The fact check unit can adjust the criteria for the fact check based on the geographical distribution of information, for example. For example, the fact check unit can evaluate the geographical distribution of news articles and prioritize checking information for each region. The fact check unit can also prioritize fact checks on geographically related information. For example, the fact check unit can evaluate the geographical distribution of news articles and prioritize checking events and emergency information for each region. Furthermore, the fact check unit can also improve the accuracy of detecting false information by taking into consideration the geographical distribution of information. For example, the fact check unit can evaluate the geographical distribution of news articles and improve the accuracy of detecting false information for each region. Thereby, the fact check unit can perform a check while taking into consideration the geographical distribution of information and improve the accuracy of the fact check. A part or all of the above-mentioned processing in the fact check unit may be performed using, for example, AI, or may be performed without using AI. For example, the fact check unit can input geographical distribution data of information to the generation AI and cause the generation AI to perform the check.

[0090] The fact check unit can improve the accuracy of the check by referring to related literature of the information during the fact check. The fact check unit can, for example, evaluate the accuracy of the information by referring to related literature. For example, the fact check unit can compare the assertion of the news article with academic papers or reliable news articles and evaluate the accuracy. The fact check unit can also adjust the criteria of the fact check based on the related literature. For example, the fact check unit can compare the assertion of the news article with related literature and adjust the criteria. Furthermore, the fact check unit can also improve the accuracy of detecting false information by referring to related literature. For example, the fact check unit can compare the assertion of the news article with related literature and improve the accuracy of detecting false information. Thereby, the fact check unit can improve the accuracy of the check by referring to related literature of the information. Part or all of the above-mentioned processing in the fact check unit may be performed, for example, using AI, or may be performed without using AI. For example, the fact check unit can input related literature data to the generation AI and have the generation AI perform the check.

[0091] The providing unit can estimate the user's emotion and determine a method of providing information based on the estimated user's emotion. For example, when the user is nervous, the providing unit can select a simple and highly visible information providing method. For example, the providing unit can estimate the user's emotion and provide information using a simple and highly visible graph or chart when the user is nervous. The providing unit can also provide detailed information when the user is relaxed. For example, the providing unit can estimate the user's emotion and provide information using detailed data analysis or text summary when the user is relaxed. Furthermore, the providing unit can also select an information providing method that focuses on the main points when the user is in a hurry. For example, the providing unit can estimate the user's emotion and select a concise information providing method that focuses on the main points when the user is in a hurry. This allows the providing unit to adjust the method of providing information according to the user's emotion and provide more appropriate information. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to determine the information providing method.

[0092] The providing unit can select a providing method by referring to the user's past browsing history when providing information. The providing unit can, for example, provide preferentially information sources that the user frequently viewed in the past. For example, the providing unit can provide preferentially news sites and blogs that the user frequently viewed by referring to the user's past browsing history. The providing unit can also provide preferentially information related to a specific topic from the user's past browsing history. For example, the providing unit can provide preferentially news articles and social media posts related to topics of interest by referring to the user's past browsing history. Furthermore, the providing unit can also select an optimal information providing time by referring to the user's past browsing history. For example, the providing unit can provide information according to a time period in which the user frequently viewed information by referring to the user's past browsing history. This allows the providing unit to provide information more effectively by referring to the user's past browsing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past browsing history data to the generating AI and cause the generating AI to select the information providing method.

[0093] The providing unit can customize the content to be provided based on the user's current field of interest when providing information. The providing unit can, for example, preferentially provide information related to topics in which the user is currently interested. For example, the providing unit can analyze the user's current field of interest and preferentially provide related news articles and social media posts. The providing unit can also filter unnecessary information based on the user's current field of interest. For example, the providing unit can exclude information that is not related to the user's current field of interest and provide only related information. Furthermore, the providing unit can analyze the user's current field of interest in real time and provide optimal information. For example, the providing unit can analyze the user's current search history and social media activities and provide information related to the field of interest in real time. Thereby, the providing unit can customize information based on the user's current field of interest and provide more relevant information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's current field of interest data to the generating AI and cause the generating AI to perform the customization of the information.

[0094] The providing unit can estimate the user's emotion and determine the order of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide only important information preferentially. For example, the providing unit can estimate the user's emotion and provide important news articles and official announcements preferentially when the user is feeling stressed. The providing unit can also provide detailed information preferentially when the user is relaxed. For example, the providing unit can estimate the user's emotion and provide detailed news articles and blog posts preferentially when the user is relaxed. Furthermore, the providing unit can also provide important information preferentially in real time when the user is in a hurry. For example, the providing unit can estimate the user's emotion and provide the latest news articles preferentially in real time when the user is in a hurry. Thereby, the providing unit can determine the priority of information provision according to the user's emotion and provide more important information preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to determine the order of information provision.

[0095] The providing unit can select a providing method in consideration of the geographical location information of the user when providing information. The providing unit can, for example, preferentially provide news articles related to the user's current location. For example, the providing unit can preferentially provide news articles and event information related to the current location based on the geographical location information of the user. The providing unit can also preferentially provide local event information based on the geographical location information of the user. For example, the providing unit can preferentially provide local event and emergency information based on the geographical location information of the user. Furthermore, the providing unit can also preferentially provide emergency information in consideration of the geographical location information of the user. For example, the providing unit can preferentially provide information related to an emergency based on the geographical location information of the user. Thereby, the providing unit can provide more relevant information in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to execute the selection of the providing method.

[0096] The providing unit can adjust the content to be provided by analyzing the user's social media activity when providing information. The providing unit can, for example, preferentially provide posts from accounts that the user follows on social media. For example, the providing unit can analyze the user's social media activity and preferentially provide posts from the accounts that the user follows. The providing unit can also provide information related to topics of interest from the user's social media activity. For example, the providing unit can analyze the content of posts, likes, and shares of the user's social media, and provide information related to topics of interest. Furthermore, the providing unit can analyze the user's social media activity and select an optimal information providing method. For example, the providing unit can analyze the user's social media activity and select an optimal information providing time and method. This allows the providing unit to analyze the user's social media activity and provide more relevant information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to adjust the content to be provided.

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

[0098] The information providing system may further include a history analysis unit that analyzes the user's past search history. The history analysis unit may analyze keywords searched for by the user in the past and websites visited by the user to identify the user's interests. For example, the history analysis unit may identify topics frequently searched for by the user and preferentially collect the latest news articles and blog posts related to the topics. The history analysis unit may also analyze categories of websites visited by the user in the past and preferentially collect information of similar categories. Furthermore, the history analysis unit may find specific patterns from the user's search history and adjust the timing and frequency of information collection based on the patterns. This allows the information providing system to provide more personalized information based on the user's past behavior.

[0099] The collection unit can further monitor the usage of the user's device and optimize the timing of information collection. For example, the collection unit can identify the time period during which the user frequently uses the device and collect information according to that time period. The collection unit can also collect information during the time period during which the user does not use the device and provide the latest information when the user starts using the device again. Furthermore, the collection unit can monitor the remaining battery level of the user's device and reduce the frequency of information collection when the battery is low. This allows the collection unit to adjust the timing of information collection according to the usage of the user's device and provide information more efficiently.

[0100] The analysis unit can further estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit can provide a concise and to the point analysis result when the user is stressed. For example, the analysis unit can estimate the user's emotions and provide the analysis result using a concise summary or a visually easy-to-understand graph when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can estimate the user's emotions and provide the analysis result using a detailed data analysis or a text summary when the user is relaxed. Furthermore, the analysis unit can also provide the analysis result with the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and provide the concise analysis result with the main points when the user is in a hurry. This allows the analysis unit to adjust the level of detail of the analysis according to the user's emotions and provide a more appropriate analysis result.

[0101] The fact check unit can further estimate the user's emotions and adjust the fact check criteria based on the estimated user's emotions. For example, the fact check unit can apply strict fact check criteria when the user is nervous. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information with strict criteria when the user is nervous. The fact check unit can also apply flexible fact check criteria when the user is relaxed. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information with flexible criteria when the user is relaxed. Furthermore, the fact check unit can perform quick fact check when the user is in a hurry. For example, the fact check unit can estimate the user's emotions and evaluate the accuracy of information quickly when the user is in a hurry. This allows the fact check unit to adjust the fact check criteria according to the user's emotions, enabling more appropriate fact check.

[0102] The providing unit can further estimate the user's emotions and determine a method of providing information based on the estimated user's emotions. For example, when the user is nervous, the providing unit can select a simple and highly visible information providing method. For example, the providing unit can estimate the user's emotions and provide information using a simple and highly visible graph or chart when the user is nervous. The providing unit can also provide detailed information when the user is relaxed. For example, the providing unit can estimate the user's emotions and provide information using detailed data analysis or text summary when the user is relaxed. Furthermore, the providing unit can also select an information providing method that focuses on the main points when the user is in a hurry. For example, the providing unit can estimate the user's emotions and select a concise information providing method that focuses on the main points when the user is in a hurry. This allows the providing unit to adjust the method of providing information according to the user's emotions, making it possible to provide more appropriate information.

[0103] The collection unit can further monitor the usage of the user's device and optimize the timing of information collection. For example, the collection unit can identify the time period during which the user frequently uses the device and collect information according to that time period. The collection unit can also collect information during the time period during which the user does not use the device and provide the latest information when the user starts using the device again. Furthermore, the collection unit can monitor the remaining battery level of the user's device and reduce the frequency of information collection when the battery is low. This allows the collection unit to adjust the timing of information collection according to the usage of the user's device and provide information more efficiently.

[0104] The analysis unit may further include a reliability evaluation unit that evaluates the reliability of the information. The reliability evaluation unit may evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the reliability evaluation unit may evaluate the reliability of news articles and prioritize analysis of highly reliable articles. The reliability evaluation unit may also evaluate the reliability of social media posts and prioritize analysis of highly reliable posts. The reliability evaluation unit may also evaluate the reliability of blog articles and prioritize analysis of highly reliable articles. This allows the analysis unit to prioritize analysis of highly reliable information and provide more accurate analysis results.

[0105] The fact check unit can further perform a check while taking into consideration the attribute information of the information provider. For example, the fact check unit can evaluate the reliability of the information provider and adjust the criteria for the fact check. For example, the fact check unit can evaluate the past history of the information provider and preferentially check the information of the highly reliable provider. The fact check unit can also improve the accuracy of the fact check while taking into consideration the past history of the information provider. For example, the fact check unit can evaluate the past history of the information provider and strictly check the information of the less reliable provider. Furthermore, the fact check unit can also improve the accuracy of detecting false information based on the attribute information of the information provider. For example, the fact check unit can evaluate the expertise and reliability of the information provider and improve the accuracy of detecting false information. As a result, the fact check unit can perform a check while taking into consideration the attribute information of the information provider and improve the accuracy of the fact check.

[0106] The providing unit can further analyze the user's social media activity to adjust the content to be provided. For example, the providing unit can preferentially provide posts from accounts that the user follows on social media. For example, the providing unit can analyze the user's social media activity and preferentially provide posts from accounts that the user follows. The providing unit can also provide information related to topics of interest from the user's social media activity. For example, the providing unit can analyze the content of posts, likes, and the number of shares on the user's social media, and provide information related to topics of interest. Furthermore, the providing unit can analyze the user's social media activity and select an optimal information provision method. For example, the providing unit can analyze the user's social media activity and select an optimal information provision time and method. This allows the providing unit to analyze the user's social media activity and provide more relevant information.

[0107] The providing unit can further estimate the user's emotion and determine the order of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide only important information preferentially. For example, the providing unit can estimate the user's emotion and provide important news articles and official announcements preferentially when the user is feeling stressed. The providing unit can also provide detailed information preferentially when the user is relaxed. For example, the providing unit can estimate the user's emotion and provide detailed news articles and blog posts preferentially when the user is relaxed. Furthermore, the providing unit can also provide important information preferentially in real time when the user is in a hurry. For example, the providing unit can estimate the user's emotion and provide the latest news articles preferentially in real time when the user is in a hurry. Thereby, the providing unit can determine the priority of information provision according to the user's emotion and provide more important information preferentially.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects information on the Internet. The collection unit may collect information such as news articles, social media posts, and blogs. The collection unit may use web scraping technology to automatically collect information on the Internet. The collection unit may also use an API to obtain data from a specific source. For example, the collection unit may use an RSS feed from a news site to collect the latest news articles. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the collected information and extract important points. For example, the analysis unit can use morphological analysis to divide a sentence into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the structure of a sentence and identify grammatical elements such as subjects and predicates. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a sentence and extract important information. Step 3: The fact-checking department fact-checks the information analyzed by the analysis department. The fact-checking department compares the collected information with other reliable sources and evaluates the accuracy of the information. For example, the fact-checking department can check whether the claims in a news article are consistent with other reliable news sites or official announcements. The fact-checking department can also use an algorithm to score the reliability of the information. Step 4: The providing unit provides the information evaluated by the fact-checking unit to the user. The providing unit presents reliable information to the user based on the evaluation results. For example, the providing unit can preferentially display reliable information on a news site or SNS. The providing unit can also provide information to the user through a user interface. For example, the providing unit can provide information to the user through a website or mobile app.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned collection unit, analysis unit, fact check unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on the Internet by the control unit 46A of the smart device 14, and is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the contents of the collected information and extracts important points. The fact check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the collected information with other reliable information sources to evaluate the accuracy of the information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and presents reliable information to the user based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned collection unit, analysis unit, fact check unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on the Internet by the control unit 46A of the smart glasses 214, and is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the content of the collected information and extracts important points. The fact check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the collected information with other reliable information sources to evaluate the accuracy of the information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and presents reliable information to the user based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, fact check unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information on the Internet by the control unit 46A of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the contents of the collected information and extracts important points. The fact check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the collected information with other reliable information sources to evaluate the accuracy of the information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and presents reliable information to the user based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned collection unit, analysis unit, fact check unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the Internet by the control unit 46A of the robot 414, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the contents of the collected information and extracts important points. The fact check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the collected information with other reliable information sources to evaluate the accuracy of the information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and presents reliable information to the user based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection unit that collects information on the Internet; an analysis unit that analyzes the information collected by the collection unit; A fact checking unit that fact-checks the information analyzed by the analysis unit; A providing unit that provides the information evaluated by the fact check unit to a user. A system characterized by: (Appendix 2) The collecting unit includes: Collect news articles, social media posts, and blogs 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected information using natural language processing technology and extract key points 2. The system of claim 1, (Appendix 4) The fact-checking department: Cross-reference the collected information with other reliable sources to assess its accuracy 2. The system of claim 1, (Appendix 5) The providing unit is Present reliable information to users based on evaluation results 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and determine the timing of information collection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze users' past browsing history and select the method of collecting information 2. The system of claim 1, (Appendix 8) The collecting unit includes: As information is collected, it is filtered based on the user's current interests 2. The system of claim 1, (Appendix 9) The collecting unit includes: Inferring user emotions and determining the order of information to be collected based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting information, prioritize collection of information that is highly relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate the user's emotions and determine how to express the analysis based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the information 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 15) The analysis unit is Inferring a user's emotion and determining the length of the analysis based on the estimated user's emotion 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the information was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on the relevance of information 2. The system of claim 1, (Appendix 18) The fact-checking department: Infer user sentiment and determine fact-check criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The fact-checking department: When fact-checking, take into account the correlation between information to improve the accuracy of the check. 2. The system of claim 1, (Appendix 20) The fact-checking department: When fact-checking, the attributes of the information provider are taken into consideration 2. The system of claim 1, (Appendix 21) The fact-checking department: Infer user sentiment and determine the order in which fact-check results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The fact-checking department: When fact-checking, take into account the geographical distribution of information 2. The system of claim 1, (Appendix 23) The fact-checking department: When fact-checking, refer to related literature to improve the accuracy of the check. 2. The system of claim 1, (Appendix 24) The providing unit is A method of providing information to a user based on the estimated user's emotion is determined. 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the method of providing it is selected by referring to the user's past browsing history. 2. The system of claim 1, (Appendix 26) The providing unit is When you provide information, tailor the content to you based on your current interests 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the user's emotions and determine the order of information provision based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, the method of providing it is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, analyze users' social media activity to tailor the content provided 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects information on the Internet; an analysis unit that analyzes the information collected by the collection unit; A fact checking unit that fact-checks the information analyzed by the analysis unit; A providing unit that provides the information evaluated by the fact check unit to a user. A system characterized by:

2. The collecting unit includes: Collect news articles, social media posts, and blogs 2. The system of claim 1 .

3. The analysis unit is Analyze collected information using natural language processing technology and extract key points 2. The system of claim 1 .

4. The fact-checking department: Cross-reference the collected information with other reliable sources to assess its accuracy 2. The system of claim 1 .

5. The providing unit is Present reliable information to users based on evaluation results 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and determine the timing of information collection based on the estimated user's emotion.

2. The system of claim 1 .

7. The collecting unit includes: Analyze users' past browsing history and select the method of collecting information 2. The system of claim 1 .

8. The collecting unit includes: As information is collected, it is filtered based on the user's current interests 2. The system of claim 1 .

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