system

Abstract

The system according to this embodiment aims to assess the veracity of news articles and provide reliable sources of information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a recommendation unit. The reception unit receives news articles as input. The analysis unit analyzes the news articles received by the reception unit and assesses their veracity. The provision unit provides reliable information sources based on the results obtained by the analysis unit. The recommendation unit recommends information that is of interest to the user based on the information provided by the provision unit.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly and accurately assess the authenticity of news articles and provide a reliable information source.

[0005] The system according to the embodiment aims to assess the authenticity of news articles and provide a reliable information source.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a recommendation unit. The reception unit receives news articles as input. The analysis unit analyzes the news articles received by the reception unit and assesses their veracity. The provision unit provides reliable information sources based on the results obtained by the analysis unit. The recommendation unit recommends information that is of interest to the user based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can assess the veracity of news articles and provide reliable sources of information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The TrustNavigator system according to an embodiment of the present invention is an AI agent system that instantly assesses the veracity of news and provides reliable sources of information. The TrustNavigator system accepts news article input, and the AI ​​agent analyzes the news article and assesses its veracity. The AI ​​agent combines language analysis and image recognition to analyze the content of the news from multiple angles. Based on the analysis results, it provides reliable sources of information. This allows users to verify the veracity of news in real time and prevent the spread of misinformation. It also assists journalists and researchers in efficiently verifying information. Furthermore, the TrustNavigator system also has a function to recommend personalized information based on the user's interests. This improves the efficiency of information gathering and reduces the spread of misinformation. The TrustNavigator system has a simple interface and allows news to be assessed with one click, making it easy for anyone to use. The business model will be developed on two axes: a subscription model and an advertising model. The target audience is news consumers aged 18-65, especially young people who are sensitive to information, as well as the media industry, research institutions, and educational institutions. The TrustNavigator system aims to contribute to a healthy information society by providing highly accurate news analysis using AI in today's world where the reliability of information is paramount. Through this, the TrustNavigator system can instantly assess the veracity of news and provide reliable sources, thereby preventing the spread of misinformation.

[0029] The TrustNavigator system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a recommendation unit. The reception unit accepts input of news articles. For example, the reception unit accepts the user entering the URL of a news article or copying and pasting the text. The reception unit receives the news article entered by the user and sends it to the analysis unit. The analysis unit analyzes the news article and assesses its authenticity. The analysis unit analyzes the content of the news from multiple angles, for example, by combining language analysis and image recognition. Language analysis analyzes the text of the news article using, for example, morphological analysis and grammatical analysis. Image recognition analyzes the images contained in the news article using, for example, object detection and face recognition. The analysis unit analyzes the content of the news article from multiple angles and assesses its authenticity. The provision unit provides reliable sources of information based on the results obtained by the analysis unit. The provision unit identifies reliable sources of information and provides them to the user, for example. Reliable sources of information include, for example, official websites and certified news organizations. The information provider unit enables users to verify the veracity of news by providing them with reliable sources. The recommendation unit recommends information tailored to the user's interests based on the information provided by the information provider unit. For example, the recommendation unit recommends personalized information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. By recommending information based on the user's interests, the recommendation unit improves the efficiency of information gathering. As a result, the TrustNavigator system according to this embodiment can instantly assess the veracity of news and prevent the spread of misinformation by providing reliable sources.

[0030] The reception desk accepts news article input. For example, it accepts users entering the URL of a news article or copying and pasting text. Specifically, the user interface is intuitive and easy to use, allowing users to easily utilize text boxes for entering news article URLs and areas for copying and pasting text. Furthermore, the reception desk offers flexibility regarding news article input formats, supporting multiple input methods. For example, users can upload news articles as PDF or image files, and these file formats are also accepted by the reception desk. The reception desk receives the news articles entered by users and sends them to the analysis desk. The reception desk uses an efficient data transfer protocol to quickly transfer the entered data to the analysis desk, ensuring that user-entered information reaches the analysis desk without delay. Additionally, the reception desk has a caching function to temporarily store the entered news article data, allowing for retransmission without data loss even in the event of a temporary network failure. This ensures that the reception desk reliably receives user input and transmits data quickly and accurately to the analysis desk.

[0031] The analysis unit analyzes news articles and assesses their veracity. For example, the analysis unit combines language analysis and image recognition to analyze the content of news articles from multiple perspectives. Specifically, language analysis uses morphological analysis and grammatical analysis to analyze the text of news articles in detail. Morphological analysis is the process of dividing text into word units and identifying the meaning and grammatical role of each word, while grammatical analysis is the process of analyzing the structure of the entire text and evaluating its grammatical accuracy and consistency. This provides basic data for determining whether the content of the news article is accurate. Image recognition analyzes images included in news articles using object detection and face recognition. Object detection is the process of identifying specific objects in an image and confirming whether those objects match the content of the news article, while face recognition is the process of identifying people in an image and confirming whether those people match the people mentioned in the news article. The analysis unit combines these technologies to analyze the content of news articles from multiple perspectives and assess their veracity. Furthermore, the analysis unit uses AI to evaluate the reliability of news articles. The AI ​​has the ability to learn from a large amount of news article data and identify the characteristics of reliable and unreliable articles. This allows the analysis unit to quickly and accurately analyze the content of news articles and assess their veracity.

[0032] The information provider department provides reliable sources of information based on the results obtained by the analysis department. Specifically, it identifies and provides reliable sources of information to users. Reliable sources of information include, for example, official websites and certified news organizations. By providing reliable sources of information to users, the information provider department enables users to verify the veracity of news. Based on the analysis results received from the analysis department, the information provider department uses an algorithm to identify highly reliable sources of information. This algorithm selects the most reliable sources by considering factors such as the source's past reliability rating and the consistency, transparency, and reliability of the information provided by the source. Furthermore, the information provider department provides users with a list of reliable sources of information through the user interface. By referring to this list, users can verify the veracity of news articles and obtain information from reliable sources. The information provider department provides links and reference information to make it easy for users to access reliable sources of information. In addition, the information provider department can always provide the latest information by collecting user feedback and continuously updating the list of reliable sources. In this way, the information provider department can play an important role in enabling users to verify the veracity of news and obtain reliable information.

[0033] The recommendation section recommends information tailored to the user's interests based on the information provided by the information provider. Specifically, it recommends personalized information based on the user's interests. User interests are identified, for example, based on past browsing history and survey results. By recommending information based on user interests, the recommendation section improves the efficiency of information gathering. The recommendation section uses an algorithm to analyze the user's past browsing history and survey results to identify information that the user is likely to be interested in. This algorithm learns the user's behavior patterns and interest trends and recommends the most relevant information. Furthermore, the recommendation section provides personalized information through the user interface. Links and reference information are provided so that users can easily access the recommended information. In addition, the recommendation section can always provide the latest information by collecting user feedback and continuously improving the recommendation algorithm. This allows the recommendation section to efficiently collect information that users are interested in and improve the efficiency of information gathering. Furthermore, by providing information tailored to the user's interests, the recommendation section can improve user satisfaction. This allows the recommendation section to quickly and accurately provide the information that users need and improve the efficiency of information gathering.

[0034] The reception desk accepts users entering the URL of a news article or copying and pasting the text. For example, the reception desk accepts users entering the URL of a news article. By entering the URL of a news article, the user can send the news article to the reception desk. The reception desk also accepts users copying and pasting the text of a news article. By copying and pasting the text of a news article, the user can send the news article to the reception desk. This makes it easy for users to enter news articles. The URL of a news article may include, for example, an http format or a specific domain. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the URL of the news article entered by the user into a generating AI and have the generating AI retrieve the news article.

[0035] The analysis unit combines language analysis and image recognition to comprehensively analyze the content of news articles and assess their veracity. For example, the analysis unit uses language analysis to analyze the text of news articles. Language analysis can be performed using, for example, morphological analysis or grammatical analysis. The analysis unit also uses image recognition to analyze the images contained in news articles. Image recognition can be performed using, for example, object detection or face recognition. The analysis unit combines language analysis and image recognition to comprehensively analyze the content of news articles and assess their veracity. This comprehensive analysis of news content improves the accuracy of veracity assessment. Language analysis can be implemented using, for example, morphological analysis or grammatical analysis. Image recognition can be implemented using, for example, object detection or face recognition. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the text and images of news articles into a generating AI and have the generating AI perform the analysis of the news content.

[0036] The information provider identifies reliable sources of information based on the results obtained by the analysis unit and provides them to the user. The information provider identifies reliable sources of information based on the results obtained by the analysis unit, for example. Reliable sources of information include, for example, official websites and certified news organizations. The information provider identifies reliable sources of information and provides them to the user. This helps prevent the spread of misinformation by identifying and providing reliable sources of information to the user. Reliable sources of information include, for example, official websites and certified news organizations. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the results obtained by the analysis unit into a generating AI and have the generating AI perform the identification of reliable sources of information.

[0037] The recommendation unit recommends personalized information based on the user's interests. For example, the recommendation unit recommends personalized information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. The recommendation unit recommends information based on the user's interests. This improves the efficiency of information gathering by recommending information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input user interest data into a generating AI and have the generating AI recommend personalized information.

[0038] The reception desk analyzes the user's past news article input history and selects the optimal input method. For example, the reception desk analyzes the user's past news article input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods that the user has frequently used in the past (such as URL input and text pasting). For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. For example, the reception desk analyzes the user's past input history and suggests the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Optimal input methods include, for example, voice input and handwriting input. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0039] The reception unit filters news articles based on the user's current areas of interest when they are entered. For example, the reception unit filters news articles based on the user's current areas of interest when they are entered. For example, the reception unit can prioritize the input of news articles related to the user's areas of interest. For example, the reception unit automatically filters relevant news articles based on the user's areas of interest. For example, the reception unit evaluates the relevance of the entered news articles based on the user's areas of interest and prioritizes them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's areas of interest. Areas of interest are identified based on, for example, past browsing history or survey results. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user interest data into a generating AI and have the generating AI perform the filtering of news articles.

[0040] The reception unit prioritizes inputting news articles based on the user's geographical location information when the user inputs news articles. For example, the reception unit prioritizes inputting news articles based on the user's geographical location information when the user inputs news articles. For example, the reception unit prioritizes inputting news articles related to the user's current location. For example, the reception unit automatically filters highly relevant news articles based on the user's geographical location information. For example, the reception unit evaluates the relevance of the input news articles based on the user's geographical location information and assigns priority to them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's geographical location information. Geographical location information includes, for example, GPS data and IP addresses. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the filtering of news articles.

[0041] The reception unit analyzes the user's social media activity when inputting news articles and inputs relevant articles. For example, the reception unit analyzes the user's social media activity when inputting news articles and inputs relevant articles. The reception unit, for example, prioritizes inputting relevant news articles based on the user's social media activity. The reception unit, for example, analyzes the user's social media activity and automatically filters relevant news articles. The reception unit, for example, evaluates the relevance of the input news articles based on the user's social media activity and prioritizes them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's social media activity. Social media activity includes, for example, the content of posts and the number of followers. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of news articles.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the news article during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the news article. For example, the analysis unit performs a detailed analysis on news articles of high importance. For example, the analysis unit performs a concise analysis on news articles of low importance. For example, the analysis unit automatically adjusts the level of detail of the analysis based on the importance of the news article. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the news article. The importance of a news article is evaluated based on, for example, the number of views or its impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the category of the news article during analysis. For example, the analysis unit applies a specific analysis algorithm to political news. For example, the analysis unit applies a specific analysis algorithm to economic news. For example, the analysis unit applies a specific analysis algorithm to sports news. By applying different analysis algorithms depending on the category of the news article, more appropriate analysis results can be provided. News article categories are classified into, for example, politics, economics, sports, etc. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit determines the priority of analysis based on the submission date of news articles during analysis. For example, the analysis unit prioritizes the analysis based on the submission date of news articles. For example, the analysis unit prioritizes the analysis of the most recent news articles. For example, the analysis unit postpones the analysis of older news articles. For example, the analysis unit automatically determines the priority of analysis based on the submission date of news articles. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission date of news articles. The submission date of news articles is obtained based on, for example, the posting date and time or the publication date and time. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input news article submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit adjusts the order of analysis based on the relevance of news articles during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of news articles. For example, the analysis unit prioritizes the analysis of highly relevant news articles. For example, the analysis unit postpones the analysis of less relevant news articles. For example, the analysis unit automatically adjusts the order of analysis based on the relevance of news articles. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of news articles. The relevance of news articles is evaluated based on, for example, similarity of content or common keywords. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The service provider adjusts the level of detail of the information sources provided based on the reliability of the news article at the time of provision. For example, the service provider adjusts the level of detail of the information sources provided based on the reliability of the news article. For example, the service provider provides detailed information sources for news articles with high reliability. For example, the service provider provides concise information sources for news articles with low reliability. The service provider automatically adjusts the level of detail of the information sources provided based on the reliability of the news article. This allows for the provision of more appropriate information sources by adjusting the level of detail of the information sources based on the reliability of the news article. The reliability of a news article is evaluated based on, for example, fact-checking results or reliability scores. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input news article reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information sources.

[0047] The service provider applies different information sources depending on the category of the news article when providing it. For example, the service provider applies different information sources depending on the category of the news article. For example, the service provider provides specific information sources for political news. For example, the service provider provides specific information sources for economic news. For example, the service provider provides specific information sources for sports news. This makes it possible to provide appropriate information sources depending on the category of the news article. News article categories are classified into, for example, politics, economics, sports, etc. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input news article category data into a generating AI and have the generating AI perform the application of information sources.

[0048] The information provider determines the priority of information sources based on the submission date of the news article at the time of provision. For example, the information provider determines the priority of information sources based on the submission date of the news article. For example, the information provider provides information sources preferentially for the most recent news article. For example, the information provider postpones providing information sources for older news articles. For example, the information provider automatically determines the priority of information sources based on the submission date of the news article. This allows for the provision of more appropriate information sources by determining the priority of information sources based on the submission date of the news article. The submission date of the news article is obtained based on, for example, the posting date and time or the publication date and time. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input news article submission date data into a generating AI and have the generating AI perform the determination of information source priority.

[0049] The information provider adjusts the order of information sources based on the relevance of the news articles when providing them. For example, the information provider adjusts the order of information sources based on the relevance of the news articles. For example, the information provider prioritizes providing information sources to highly relevant news articles. For example, the information provider postpones providing information sources to less relevant news articles. For example, the information provider automatically adjusts the order of information sources based on the relevance of the news articles. This allows for the provision of more appropriate information sources by adjusting the order of information sources based on the relevance of the news articles. The relevance of news articles is evaluated based on, for example, similarity of content or common keywords. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the order of information sources.

[0050] The recommendation unit analyzes the user's past interest history to select the most relevant information when making recommendations. For example, the recommendation unit analyzes the user's past interest history to select the most relevant information. For example, the recommendation unit recommends the most relevant information based on news articles the user has previously been interested in. For example, the recommendation unit analyzes the user's past interest history and recommends relevant information. For example, the recommendation unit recommends the most relevant information based on the user's past interest history. This allows the recommendation unit to recommend the most relevant information by analyzing the user's past interest history. Past interest history is obtained based on, for example, browsing history and search history. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's past interest history data into a generating AI and have the generating AI select the most relevant information.

[0051] The recommendation unit customizes the recommendation method based on the user's current areas of interest when making recommendations. For example, the recommendation unit customizes the recommendation method based on the user's current areas of interest. For example, the recommendation unit provides the optimal recommendation method based on the areas the user is currently interested in. For example, the recommendation unit recommends relevant information based on the user's current areas of interest. For example, the recommendation unit recommends the most relevant information based on the user's current areas of interest. This allows for the recommendation of more appropriate information by customizing the recommendation method based on the user's current areas of interest. Current areas of interest are identified based on, for example, past browsing history or survey results. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the recommendation method.

[0052] The recommendation unit selects the most relevant information by considering the user's geographical location when making recommendations. For example, the recommendation unit selects the most relevant information by considering the user's geographical location. For example, the recommendation unit prioritizes recommending information related to the user's current location. For example, the recommendation unit recommends relevant information based on the user's geographical location. For example, the recommendation unit recommends the most relevant information based on the user's geographical location. This allows for the provision of more appropriate information by recommending the most relevant information based on the user's geographical location. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant information.

[0053] The recommendation unit analyzes the user's social media activity and proposes a recommendation method when making a recommendation. For example, the recommendation unit analyzes the user's social media activity and proposes a recommendation method. For example, the recommendation unit provides the optimal recommendation method based on the user's social media activity. For example, the recommendation unit analyzes the user's social media activity and recommends relevant information. For example, the recommendation unit recommends the most relevant information based on the user's social media activity. This allows for the recommendation of more appropriate information by providing the optimal recommendation method based on the user's social media activity. Social media activity is analyzed based on, for example, the content of posts and the number of followers. Recommendation methods include, for example, email notifications and app notifications. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI propose recommendation methods.

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

[0055] The reception unit can also accept voice input when users enter news articles. For example, if a user reads the content of a news article aloud, the reception unit can convert the audio into text and send it to the analysis unit. This makes it easy to enter news articles even when hands are busy. The reception unit can also accept news articles scanned by users as images. For example, if a user takes a picture of a newspaper or magazine article with their smartphone camera and sends the image to the reception unit, the reception unit can use image recognition technology to convert it into text and send it to the analysis unit. Furthermore, the reception unit can also accept news articles entered by hand. For example, if a user writes a news article by hand on a tablet device, the reception unit can convert the handwritten text into text and send it to the analysis unit. This allows users to enter news articles in a variety of ways, improving convenience.

[0056] The analysis unit can also evaluate the reliability of the article's author when analyzing the content of a news article. For example, it can calculate an author reliability score by considering the author's past writing history, expertise, and the reliability of the media outlet they belong to. This allows for a more accurate assessment of the veracity of the news article. The analysis unit can also verify whether the content of a news article is consistent with other reliable sources. For example, it can assess the veracity by collecting information from multiple reliable sources on the same news story and comparing that information with the content of the input news article. Furthermore, the analysis unit can verify whether the content of a news article is consistent with past data. For example, it can evaluate the reliability of a news article by comparing it with past news articles and information stored in the database to check for inconsistencies. This allows for a multifaceted evaluation of the veracity of a news article.

[0057] The service provider can visually indicate the reliability of news articles to users based on the results obtained by the analysis unit. For example, reliable news articles can be shown with a green icon, and unreliable news articles with a red icon, allowing users to judge reliability at a glance. The service provider can also generate and provide users with detailed reports on the reliability of news articles. For example, by generating and providing users with reports that include the reliability score of news articles, the criteria used to evaluate reliability, and detailed analysis results, users can gain a deeper understanding of the reliability of news articles. Furthermore, the service provider can provide a function that allows users to save reliable news articles for later reference. For example, saving reliable news articles as bookmarks for easy access later can improve the efficiency of users' information gathering.

[0058] The recommendation section can also consider the user's current activity when recommending personalized information based on their interests. For example, if the user is at work, news articles related to work can be prioritized. If the user is on a break, entertainment news that helps them relax can be recommended. Furthermore, if the user is traveling, news articles related to their travel destination can be recommended. This allows for the provision of information that is optimal according to the user's current activity, further improving the efficiency of information gathering for the user. The recommendation section can also customize the format of the information recommended based on the user's interests. For example, if the user prefers visual information, news articles containing images and videos can be prioritized. On the other hand, if the user prefers text information, detailed text articles can be recommended. This allows for the provision of information tailored to the user's preferences, improving user satisfaction.

[0059] The input section can provide an auto-completion function when users enter news articles. For example, when a user enters part of a news article, the input section can analyze the content and suggest relevant suggestions, making it easier for the user to complete the input. The input section can also analyze the content of the news article entered by the user in real time and provide a function to automatically correct typos and grammatical errors. This allows users to enter accurate news articles and improves the accuracy of the analysis by the analysis section. Furthermore, the input section can also provide a function to automatically search for and suggest related news articles when a user enters a news article. For example, when a user enters a news article on a specific topic, the input section can search for other news articles related to that topic and suggest them to the user, allowing the user to obtain more information. This improves the efficiency of the user's information gathering.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception department accepts news article input. For example, it accepts users entering the URL of a news article or copying and pasting the text. The reception department receives the news article entered by the user and sends it to the analysis department. Step 2: The analysis unit analyzes the news article and assesses its veracity. For example, it combines language analysis and image recognition to analyze the news content from multiple angles. Language analysis uses morphological and grammatical analysis to analyze the text of the news article, while image recognition uses object detection and face recognition to analyze the images contained in the news article. Step 3: The provider provides reliable sources of information based on the results obtained by the analysis department. For example, they identify and provide reliable sources to users. Reliable sources include official websites and certified news organizations. Step 4: The recommendation section recommends information tailored to the user's interests based on the information provided by the service provider. For example, it recommends personalized information based on the user's interests. The user's interests are identified based on past browsing history, survey results, etc.

[0062] (Example of form 2) The TrustNavigator system according to an embodiment of the present invention is an AI agent system that instantly assesses the veracity of news and provides reliable sources of information. The TrustNavigator system accepts news article input, and the AI ​​agent analyzes the news article and assesses its veracity. The AI ​​agent combines language analysis and image recognition to analyze the content of the news from multiple angles. Based on the analysis results, it provides reliable sources of information. This allows users to verify the veracity of news in real time and prevent the spread of misinformation. It also assists journalists and researchers in efficiently verifying information. Furthermore, the TrustNavigator system also has a function to recommend personalized information based on the user's interests. This improves the efficiency of information gathering and reduces the spread of misinformation. The TrustNavigator system has a simple interface and allows news to be assessed with one click, making it easy for anyone to use. The business model will be developed on two axes: a subscription model and an advertising model. The target audience is news consumers aged 18-65, especially young people who are sensitive to information, as well as the media industry, research institutions, and educational institutions. The TrustNavigator system aims to contribute to a healthy information society by providing highly accurate news analysis using AI in today's world where the reliability of information is paramount. Through this, the TrustNavigator system can instantly assess the veracity of news and provide reliable sources, thereby preventing the spread of misinformation.

[0063] The TrustNavigator system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a recommendation unit. The reception unit accepts input of news articles. For example, the reception unit accepts the user entering the URL of a news article or copying and pasting the text. The reception unit receives the news article entered by the user and sends it to the analysis unit. The analysis unit analyzes the news article and assesses its authenticity. The analysis unit analyzes the content of the news from multiple angles, for example, by combining language analysis and image recognition. Language analysis analyzes the text of the news article using, for example, morphological analysis and grammatical analysis. Image recognition analyzes the images contained in the news article using, for example, object detection and face recognition. The analysis unit analyzes the content of the news article from multiple angles and assesses its authenticity. The provision unit provides reliable sources of information based on the results obtained by the analysis unit. The provision unit identifies reliable sources of information and provides them to the user, for example. Reliable sources of information include, for example, official websites and certified news organizations. The information provider unit enables users to verify the veracity of news by providing them with reliable sources. The recommendation unit recommends information tailored to the user's interests based on the information provided by the information provider unit. For example, the recommendation unit recommends personalized information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. By recommending information based on the user's interests, the recommendation unit improves the efficiency of information gathering. As a result, the TrustNavigator system according to this embodiment can instantly assess the veracity of news and prevent the spread of misinformation by providing reliable sources.

[0064] The reception desk accepts news article input. For example, it accepts users entering the URL of a news article or copying and pasting text. Specifically, the user interface is intuitive and easy to use, allowing users to easily utilize text boxes for entering news article URLs and areas for copying and pasting text. Furthermore, the reception desk offers flexibility regarding news article input formats, supporting multiple input methods. For example, users can upload news articles as PDF or image files, and these file formats are also accepted by the reception desk. The reception desk receives the news articles entered by users and sends them to the analysis desk. The reception desk uses an efficient data transfer protocol to quickly transfer the entered data to the analysis desk, ensuring that user-entered information reaches the analysis desk without delay. Additionally, the reception desk has a caching function to temporarily store the entered news article data, allowing for retransmission without data loss even in the event of a temporary network failure. This ensures that the reception desk reliably receives user input and transmits data quickly and accurately to the analysis desk.

[0065] The analysis unit analyzes news articles and assesses their veracity. For example, the analysis unit combines language analysis and image recognition to analyze the content of news articles from multiple perspectives. Specifically, language analysis uses morphological analysis and grammatical analysis to analyze the text of news articles in detail. Morphological analysis is the process of dividing text into word units and identifying the meaning and grammatical role of each word, while grammatical analysis is the process of analyzing the structure of the entire text and evaluating its grammatical accuracy and consistency. This provides basic data for determining whether the content of the news article is accurate. Image recognition analyzes images included in news articles using object detection and face recognition. Object detection is the process of identifying specific objects in an image and confirming whether those objects match the content of the news article, while face recognition is the process of identifying people in an image and confirming whether those people match the people mentioned in the news article. The analysis unit combines these technologies to analyze the content of news articles from multiple perspectives and assess their veracity. Furthermore, the analysis unit uses AI to evaluate the reliability of news articles. The AI ​​has the ability to learn from a large amount of news article data and identify the characteristics of reliable and unreliable articles. This allows the analysis unit to quickly and accurately analyze the content of news articles and assess their veracity.

[0066] The information provider department provides reliable sources of information based on the results obtained by the analysis department. Specifically, it identifies and provides reliable sources of information to users. Reliable sources of information include, for example, official websites and certified news organizations. By providing reliable sources of information to users, the information provider department enables users to verify the veracity of news. Based on the analysis results received from the analysis department, the information provider department uses an algorithm to identify highly reliable sources of information. This algorithm selects the most reliable sources by considering factors such as the source's past reliability rating and the consistency, transparency, and reliability of the information provided by the source. Furthermore, the information provider department provides users with a list of reliable sources of information through the user interface. By referring to this list, users can verify the veracity of news articles and obtain information from reliable sources. The information provider department provides links and reference information to make it easy for users to access reliable sources of information. In addition, the information provider department can always provide the latest information by collecting user feedback and continuously updating the list of reliable sources. In this way, the information provider department can play an important role in enabling users to verify the veracity of news and obtain reliable information.

[0067] The recommendation section recommends information tailored to the user's interests based on the information provided by the information provider. Specifically, it recommends personalized information based on the user's interests. User interests are identified, for example, based on past browsing history and survey results. By recommending information based on user interests, the recommendation section improves the efficiency of information gathering. The recommendation section uses an algorithm to analyze the user's past browsing history and survey results to identify information that the user is likely to be interested in. This algorithm learns the user's behavior patterns and interest trends and recommends the most relevant information. Furthermore, the recommendation section provides personalized information through the user interface. Links and reference information are provided so that users can easily access the recommended information. In addition, the recommendation section can always provide the latest information by collecting user feedback and continuously improving the recommendation algorithm. This allows the recommendation section to efficiently collect information that users are interested in and improve the efficiency of information gathering. Furthermore, by providing information tailored to the user's interests, the recommendation section can improve user satisfaction. This allows the recommendation section to quickly and accurately provide the information that users need and improve the efficiency of information gathering.

[0068] The reception desk accepts users entering the URL of a news article or copying and pasting the text. For example, the reception desk accepts users entering the URL of a news article. By entering the URL of a news article, the user can send the news article to the reception desk. The reception desk also accepts users copying and pasting the text of a news article. By copying and pasting the text of a news article, the user can send the news article to the reception desk. This makes it easy for users to enter news articles. The URL of a news article may include, for example, an http format or a specific domain. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the URL of the news article entered by the user into a generating AI and have the generating AI retrieve the news article.

[0069] The analysis unit combines language analysis and image recognition to comprehensively analyze the content of news articles and assess their veracity. For example, the analysis unit uses language analysis to analyze the text of news articles. Language analysis can be performed using, for example, morphological analysis or grammatical analysis. The analysis unit also uses image recognition to analyze the images contained in news articles. Image recognition can be performed using, for example, object detection or face recognition. The analysis unit combines language analysis and image recognition to comprehensively analyze the content of news articles and assess their veracity. This comprehensive analysis of news content improves the accuracy of veracity assessment. Language analysis can be implemented using, for example, morphological analysis or grammatical analysis. Image recognition can be implemented using, for example, object detection or face recognition. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the text and images of news articles into a generating AI and have the generating AI perform the analysis of the news content.

[0070] The information provider identifies reliable sources of information based on the results obtained by the analysis unit and provides them to the user. The information provider identifies reliable sources of information based on the results obtained by the analysis unit, for example. Reliable sources of information include, for example, official websites and certified news organizations. The information provider identifies reliable sources of information and provides them to the user. This helps prevent the spread of misinformation by identifying and providing reliable sources of information to the user. Reliable sources of information include, for example, official websites and certified news organizations. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the results obtained by the analysis unit into a generating AI and have the generating AI perform the identification of reliable sources of information.

[0071] The recommendation unit recommends personalized information based on the user's interests. For example, the recommendation unit recommends personalized information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. The recommendation unit recommends information based on the user's interests. This improves the efficiency of information gathering by recommending information based on the user's interests. The user's interests are identified, for example, based on past browsing history or survey results. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input user interest data into a generating AI and have the generating AI recommend personalized information.

[0072] The reception unit estimates the user's emotions and adjusts the timing of news article input based on the estimated emotions. For example, if the user is stressed, the reception unit delays the input timing to allow the user to input in a relaxed state. If the user is excited, the reception unit prompts for input immediately, allowing the user to input the news article while their emotions are heightened. If the user is tired, the reception unit adjusts the input timing to allow the user to input after a break. By adjusting the input timing according to the user's emotions, news articles can be input at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk analyzes the user's past news article input history and selects the optimal input method. For example, the reception desk analyzes the user's past news article input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods that the user has frequently used in the past (such as URL input and text pasting). For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. For example, the reception desk analyzes the user's past input history and suggests the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Optimal input methods include, for example, voice input and handwriting input. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0074] The reception unit filters news articles based on the user's current areas of interest when they are entered. For example, the reception unit filters news articles based on the user's current areas of interest when they are entered. For example, the reception unit can prioritize the input of news articles related to the user's areas of interest. For example, the reception unit automatically filters relevant news articles based on the user's areas of interest. For example, the reception unit evaluates the relevance of the entered news articles based on the user's areas of interest and prioritizes them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's areas of interest. Areas of interest are identified based on, for example, past browsing history or survey results. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user interest data into a generating AI and have the generating AI perform the filtering of news articles.

[0075] The reception unit estimates the user's emotions and determines the priority of news articles to input based on the estimated emotions. For example, if the user is stressed, the reception unit prioritizes news articles that promote relaxation. If the user is excited, the reception unit prioritizes news articles that enhance their emotions. If the user is tired, the reception unit prioritizes news articles that promote relaxation. By prioritizing news articles according to the user's emotions, more appropriate news articles can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of news articles.

[0076] The reception unit prioritizes inputting news articles based on the user's geographical location information when the user inputs news articles. For example, the reception unit prioritizes inputting news articles based on the user's geographical location information when the user inputs news articles. For example, the reception unit prioritizes inputting news articles related to the user's current location. For example, the reception unit automatically filters highly relevant news articles based on the user's geographical location information. For example, the reception unit evaluates the relevance of the input news articles based on the user's geographical location information and assigns priority to them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's geographical location information. Geographical location information includes, for example, GPS data and IP addresses. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the filtering of news articles.

[0077] The reception unit analyzes the user's social media activity when inputting news articles and inputs relevant articles. For example, the reception unit analyzes the user's social media activity when inputting news articles and inputs relevant articles. The reception unit, for example, prioritizes inputting relevant news articles based on the user's social media activity. The reception unit, for example, analyzes the user's social media activity and automatically filters relevant news articles. The reception unit, for example, evaluates the relevance of the input news articles based on the user's social media activity and prioritizes them. This allows for the priority input of highly relevant news articles by filtering news articles based on the user's social media activity. Social media activity includes, for example, the content of posts and the number of followers. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of news articles.

[0078] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit adjusts the level of detail of the analysis based on the importance of the news article during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the news article. For example, the analysis unit performs a detailed analysis on news articles of high importance. For example, the analysis unit performs a concise analysis on news articles of low importance. For example, the analysis unit automatically adjusts the level of detail of the analysis based on the importance of the news article. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the news article. The importance of a news article is evaluated based on, for example, the number of views or its impact. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit applies different analysis algorithms depending on the category of the news article during analysis. For example, the analysis unit applies a specific analysis algorithm to political news. For example, the analysis unit applies a specific analysis algorithm to economic news. For example, the analysis unit applies a specific analysis algorithm to sports news. By applying different analysis algorithms depending on the category of the news article, more appropriate analysis results can be provided. News article categories are classified into, for example, politics, economics, sports, etc. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit determines the priority of analysis based on the submission date of news articles during analysis. For example, the analysis unit prioritizes the analysis based on the submission date of news articles. For example, the analysis unit prioritizes the analysis of the most recent news articles. For example, the analysis unit postpones the analysis of older news articles. For example, the analysis unit automatically determines the priority of analysis based on the submission date of news articles. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission date of news articles. The submission date of news articles is obtained based on, for example, the posting date and time or the publication date and time. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input news article submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0083] The analysis unit adjusts the order of analysis based on the relevance of news articles during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of news articles. For example, the analysis unit prioritizes the analysis of highly relevant news articles. For example, the analysis unit postpones the analysis of less relevant news articles. For example, the analysis unit automatically adjusts the order of analysis based on the relevance of news articles. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of news articles. The relevance of news articles is evaluated based on, for example, similarity of content or common keywords. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The service provider estimates the user's emotions and determines the priority of the information sources to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed information sources. If the user is in a hurry, the service provider will prioritize concise information sources. If the user is excited, the service provider will prioritize visually stimulating information sources. By prioritizing information sources according to the user's emotions, more appropriate information sources can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of information sources.

[0085] The service provider adjusts the level of detail of the information sources provided based on the reliability of the news article at the time of provision. For example, the service provider adjusts the level of detail of the information sources provided based on the reliability of the news article. For example, the service provider provides detailed information sources for news articles with high reliability. For example, the service provider provides concise information sources for news articles with low reliability. The service provider automatically adjusts the level of detail of the information sources provided based on the reliability of the news article. This allows for the provision of more appropriate information sources by adjusting the level of detail of the information sources based on the reliability of the news article. The reliability of a news article is evaluated based on, for example, fact-checking results or reliability scores. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input news article reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information sources.

[0086] The service provider applies different information sources depending on the category of the news article when providing it. For example, the service provider applies different information sources depending on the category of the news article. For example, the service provider provides specific information sources for political news. For example, the service provider provides specific information sources for economic news. For example, the service provider provides specific information sources for sports news. This makes it possible to provide appropriate information sources depending on the category of the news article. News article categories are classified into, for example, politics, economics, sports, etc. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input news article category data into a generating AI and have the generating AI perform the application of information sources.

[0087] The service provider estimates the user's emotions and adjusts how the information sources are displayed based on the estimated emotions. For example, the service provider estimates the user's emotions and adjusts how the information sources are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider displays detailed information sources. For example, if the user is in a hurry, the service provider displays concise information sources. For example, if the user is excited, the service provider displays visually stimulating information sources. This allows for the provision of more appropriate information sources by adjusting how the information sources are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the information sources are displayed.

[0088] The information provider determines the priority of information sources based on the submission date of the news article at the time of provision. For example, the information provider determines the priority of information sources based on the submission date of the news article. For example, the information provider provides information sources preferentially for the most recent news article. For example, the information provider postpones providing information sources for older news articles. For example, the information provider automatically determines the priority of information sources based on the submission date of the news article. This allows for the provision of more appropriate information sources by determining the priority of information sources based on the submission date of the news article. The submission date of the news article is obtained based on, for example, the posting date and time or the publication date and time. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input news article submission date data into a generating AI and have the generating AI perform the determination of information source priority.

[0089] The information provider adjusts the order of information sources based on the relevance of the news articles when providing them. For example, the information provider adjusts the order of information sources based on the relevance of the news articles. For example, the information provider prioritizes providing information sources to highly relevant news articles. For example, the information provider postpones providing information sources to less relevant news articles. For example, the information provider automatically adjusts the order of information sources based on the relevance of the news articles. This allows for the provision of more appropriate information sources by adjusting the order of information sources based on the relevance of the news articles. The relevance of news articles is evaluated based on, for example, similarity of content or common keywords. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the order of information sources.

[0090] The recommendation unit estimates the user's emotions and determines the priority of recommended information based on the estimated emotions. For example, if the user is relaxed, the recommendation unit will prioritize recommending detailed information. If the user is in a hurry, the recommendation unit will prioritize recommending concise information. If the user is excited, the recommendation unit will prioritize recommending visually stimulating information. By prioritizing information according to the user's emotions, more appropriate information can be recommended. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0091] The recommendation unit analyzes the user's past interest history to select the most relevant information when making recommendations. For example, the recommendation unit analyzes the user's past interest history to select the most relevant information. For example, the recommendation unit recommends the most relevant information based on news articles the user has previously been interested in. For example, the recommendation unit analyzes the user's past interest history and recommends relevant information. For example, the recommendation unit recommends the most relevant information based on the user's past interest history. This allows the recommendation unit to recommend the most relevant information by analyzing the user's past interest history. Past interest history is obtained based on, for example, browsing history and search history. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's past interest history data into a generating AI and have the generating AI select the most relevant information.

[0092] The recommendation unit customizes the recommendation method based on the user's current areas of interest when making recommendations. For example, the recommendation unit customizes the recommendation method based on the user's current areas of interest. For example, the recommendation unit provides the optimal recommendation method based on the areas the user is currently interested in. For example, the recommendation unit recommends relevant information based on the user's current areas of interest. For example, the recommendation unit recommends the most relevant information based on the user's current areas of interest. This allows for the recommendation of more appropriate information by customizing the recommendation method based on the user's current areas of interest. Current areas of interest are identified based on, for example, past browsing history or survey results. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the recommendation method.

[0093] The recommendation unit estimates the user's emotions and adjusts how recommended information is displayed based on the estimated emotions. For example, the recommendation unit estimates the user's emotions and adjusts how recommended information is displayed based on the estimated emotions. For example, if the user is relaxed, the recommendation unit displays detailed information. For example, if the user is in a hurry, the recommendation unit displays concise information. For example, if the user is excited, the recommendation unit displays visually stimulating information. This allows for the recommendation of more appropriate information by adjusting how information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust how information is displayed.

[0094] The recommendation unit selects the most relevant information by considering the user's geographical location when making recommendations. For example, the recommendation unit selects the most relevant information by considering the user's geographical location. For example, the recommendation unit prioritizes recommending information related to the user's current location. For example, the recommendation unit recommends relevant information based on the user's geographical location. For example, the recommendation unit recommends the most relevant information based on the user's geographical location. This allows for the provision of more appropriate information by recommending the most relevant information based on the user's geographical location. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant information.

[0095] The recommendation unit analyzes the user's social media activity and proposes a recommendation method when making a recommendation. For example, the recommendation unit analyzes the user's social media activity and proposes a recommendation method. For example, the recommendation unit provides the optimal recommendation method based on the user's social media activity. For example, the recommendation unit analyzes the user's social media activity and recommends relevant information. For example, the recommendation unit recommends the most relevant information based on the user's social media activity. This allows for the recommendation of more appropriate information by providing the optimal recommendation method based on the user's social media activity. Social media activity is analyzed based on, for example, the content of posts and the number of followers. Recommendation methods include, for example, email notifications and app notifications. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI propose recommendation methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception unit can also accept voice input when users enter news articles. For example, if a user reads the content of a news article aloud, the reception unit can convert the audio into text and send it to the analysis unit. This makes it easy to enter news articles even when hands are busy. The reception unit can also accept news articles scanned by users as images. For example, if a user takes a picture of a newspaper or magazine article with their smartphone camera and sends the image to the reception unit, the reception unit can use image recognition technology to convert it into text and send it to the analysis unit. Furthermore, the reception unit can also accept news articles entered by hand. For example, if a user writes a news article by hand on a tablet device, the reception unit can convert the handwritten text into text and send it to the analysis unit. This allows users to enter news articles in a variety of ways, improving convenience.

[0098] The analysis unit can also evaluate the reliability of the article's author when analyzing the content of a news article. For example, it can calculate an author reliability score by considering the author's past writing history, expertise, and the reliability of the media outlet they belong to. This allows for a more accurate assessment of the veracity of the news article. The analysis unit can also verify whether the content of a news article is consistent with other reliable sources. For example, it can assess the veracity by collecting information from multiple reliable sources on the same news story and comparing that information with the content of the input news article. Furthermore, the analysis unit can verify whether the content of a news article is consistent with past data. For example, it can evaluate the reliability of a news article by comparing it with past news articles and information stored in the database to check for inconsistencies. This allows for a multifaceted evaluation of the veracity of a news article.

[0099] The service provider can visually indicate the reliability of news articles to users based on the results obtained by the analysis unit. For example, reliable news articles can be shown with a green icon, and unreliable news articles with a red icon, allowing users to judge reliability at a glance. The service provider can also generate and provide users with detailed reports on the reliability of news articles. For example, by generating and providing users with reports that include the reliability score of news articles, the criteria used to evaluate reliability, and detailed analysis results, users can gain a deeper understanding of the reliability of news articles. Furthermore, the service provider can provide a function that allows users to save reliable news articles for later reference. For example, saving reliable news articles as bookmarks for easy access later can improve the efficiency of users' information gathering.

[0100] The recommendation section can also consider the user's current activity when recommending personalized information based on their interests. For example, if the user is at work, news articles related to work can be prioritized. If the user is on a break, entertainment news that helps them relax can be recommended. Furthermore, if the user is traveling, news articles related to their travel destination can be recommended. This allows for the provision of information that is optimal according to the user's current activity, further improving the efficiency of information gathering for the user. The recommendation section can also customize the format of the information recommended based on the user's interests. For example, if the user prefers visual information, news articles containing images and videos can be prioritized. On the other hand, if the user prefers text information, detailed text articles can be recommended. This allows for the provision of information tailored to the user's preferences, improving user satisfaction.

[0101] The input section can provide an auto-completion function when users enter news articles. For example, when a user enters part of a news article, the input section can analyze the content and suggest relevant suggestions, making it easier for the user to complete the input. The input section can also analyze the content of the news article entered by the user in real time and provide a function to automatically correct typos and grammatical errors. This allows users to enter accurate news articles and improves the accuracy of the analysis by the analysis section. Furthermore, the input section can also provide a function to automatically search for and suggest related news articles when a user enters a news article. For example, when a user enters a news article on a specific topic, the input section can search for other news articles related to that topic and suggest them to the user, allowing the user to obtain more information. This improves the efficiency of the user's information gathering.

[0102] The reception desk can estimate the user's emotions and suggest methods for inputting news articles based on those emotions. For example, if the user is stressed, it can suggest voice input, allowing the user to input news articles in a relaxed state. If the user is excited, it can suggest handwriting input, allowing them to input news articles while calming their emotions. Furthermore, if the user is tired, it can present simple options, allowing the user to input news articles without feeling burdened. This improves user convenience by suggesting the optimal input method according to the user's emotions. The reception desk can also adjust the auto-completion function of the input content based on the user's emotions. For example, if the user is in a hurry, the auto-completion function can be enhanced to shorten the input time. On the other hand, if the user is relaxed, the auto-completion function can be toned down, allowing the user to input at their own pace. This enables flexible input support that responds to the user's emotions.

[0103] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is relaxed, detailed analysis results can be provided to help them understand the content of a news article more deeply. If the user is in a hurry, concise analysis results can be provided to help them obtain the necessary information quickly. Furthermore, if the user is excited, visually stimulating analysis results can be provided to keep the user interested. By adjusting the depth of the analysis according to the user's emotions, more appropriate analysis results can be provided. The analysis unit can also adjust how the analysis results are presented based on the user's emotions. For example, if the user is relaxed, a detailed text report can be provided. On the other hand, if the user is in a hurry, a bulleted report summarizing the key points can be provided. This enables flexible presentation of analysis results that are tailored to the user's emotions.

[0104] The information provider can estimate the user's emotions and adjust the reliability of the information sources provided based on those emotions. For example, if the user is relaxed, detailed information sources can be provided to allow the user to understand the information more deeply. If the user is in a hurry, concise information sources can be provided to allow the user to obtain the necessary information in a short amount of time. Furthermore, if the user is excited, visually stimulating information sources can be provided to keep the user interested. In this way, by adjusting the reliability of the information sources provided according to the user's emotions, more appropriate information sources can be provided. The information provider can also adjust how information sources are displayed based on the user's emotions. For example, if the user is relaxed, a detailed text report can be displayed. On the other hand, if the user is in a hurry, a summary bulleted report can be displayed. This enables the provision of flexible information sources that respond to the user's emotions.

[0105] The recommendation section can estimate the user's emotions and adjust the format of the recommended information based on those emotions. For example, if the user is relaxed, detailed text information can be recommended to allow the user to understand the information more deeply. If the user is in a hurry, information in a concise bulleted list format can be recommended to allow the user to obtain the necessary information quickly. Furthermore, if the user is excited, information including visually stimulating images and videos can be recommended to keep the user interested. In this way, by adjusting the format of the recommended information according to the user's emotions, more appropriate information can be provided. The recommendation section can also adjust the content of the recommended information based on the user's emotions. For example, if the user is relaxed, news articles with detailed background information can be recommended. On the other hand, if the user is in a hurry, breaking news summaries can be recommended. This enables flexible information delivery that responds to the user's emotions.

[0106] The recommendation section can estimate the user's emotions and adjust the timing of recommended information based on those emotions. For example, if the user is relaxed, the timing of providing detailed information can be adjusted to allow the user to understand the information more deeply. If the user is in a hurry, concise information can be provided immediately, allowing the user to obtain the necessary information in a short time. Furthermore, if the user is excited, timely provision of visually stimulating information can keep the user interested. In this way, by adjusting the timing of recommended information according to the user's emotions, more appropriate information can be provided. The recommendation section can also adjust the frequency of recommended information based on the user's emotions. For example, if the user is relaxed, increasing the frequency of information provision can allow the user to obtain more information. On the other hand, if the user is in a hurry, decreasing the frequency of information provision can prevent the user from being overwhelmed with information. This enables flexible information provision that responds to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception department accepts news article input. For example, it accepts users entering the URL of a news article or copying and pasting the text. The reception department receives the news article entered by the user and sends it to the analysis department. Step 2: The analysis unit analyzes the news article and assesses its veracity. For example, it combines language analysis and image recognition to analyze the news content from multiple angles. Language analysis uses morphological and grammatical analysis to analyze the text of the news article, while image recognition uses object detection and face recognition to analyze the images contained in the news article. Step 3: The provider provides reliable sources of information based on the results obtained by the analysis department. For example, they identify and provide reliable sources to users. Reliable sources include official websites and certified news organizations. Step 4: The recommendation section recommends information tailored to the user's interests based on the information provided by the service provider. For example, it recommends personalized information based on the user's interests. The user's interests are identified based on past browsing history, survey results, etc.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the user entering a URL of a news article or copying and pasting text. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the news from multiple angles by combining language analysis and image recognition. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies reliable sources and provides them to the user. The recommendation unit is implemented by the control unit 46A of the smart device 14 and recommends personalized information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user entering a URL of a news article or copying and pasting text. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the news from multiple angles by combining language analysis and image recognition. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies reliable sources and provides them to the user. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and recommends personalized information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user entering a URL of a news article or copying and pasting text. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the news from multiple angles by combining language analysis and image recognition. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies reliable sources and provides them to the user. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and recommends personalized information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts the user entering a URL of a news article or copying and pasting text. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content of the news from multiple angles by combining language analysis and image recognition. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies reliable sources and provides them to the user. The recommendation unit is implemented by, for example, the control unit 46A of the robot 414 and recommends personalized information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The reception area for receiving news articles, The analysis unit analyzes news articles received by the aforementioned reception unit and assesses their authenticity, A providing unit that provides reliable information sources based on the results obtained by the analysis unit, The system includes a recommendation unit that recommends information tailored to the user's interests based on the information provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts users to enter the URL of a news article or copy and paste the text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By combining language analysis and image recognition, news content is analyzed from multiple perspectives to assess its veracity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the results obtained by the analysis unit, reliable information sources are identified and provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation section is, Recommend personalized information based on user interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates user sentiment and adjusts the timing of news article publication based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past news article input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering news articles, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of news articles to input based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering news articles, the system prioritizes displaying articles that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering news articles, the system analyzes the user's social media activity and inputs relevant articles. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the news articles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the information sources to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing news articles, we adjust the level of detail of the sources we provide based on their reliability. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing news articles, different sources will be applied depending on the category of the article. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We estimate the user's sentiment and adjust how the information sources we provide are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we prioritize sources based on when the news articles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we adjust the order of sources based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation section is, It estimates the user's emotions and prioritizes the information to recommend based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation section is, When making recommendations, the system analyzes the user's past interests to select the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation section is, When making recommendations, customize the recommendation method based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation section is, It estimates the user's emotions and adjusts how recommended information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation section is, When making recommendations, the system selects the most suitable information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation section is, When making recommendations, the system analyzes the user's social media activity to suggest appropriate methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for receiving news articles, The analysis unit analyzes news articles received by the aforementioned reception unit and assesses their authenticity, A providing unit that provides reliable information sources based on the results obtained by the analysis unit, The system includes a recommendation unit that recommends information tailored to the user's interests based on the information provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts users to enter the URL of a news article or copy and paste the text. The system according to feature 1.

3. The aforementioned analysis unit, By combining language analysis and image recognition, news content is analyzed from multiple perspectives to assess its veracity. The system according to feature 1.

4. The aforementioned supply unit is, Based on the results obtained by the aforementioned analysis unit, reliable information sources are identified and provided to the user. The system according to feature 1.

5. The aforementioned recommendation section is, Recommend personalized information based on user interests. The system according to feature 1.

6. The aforementioned reception unit is The system estimates user sentiment and adjusts the timing of news article publication based on the estimated sentiment. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past news article input history and selects the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering news articles, filtering is performed based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's sentiment and determines the priority of news articles to input based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned reception unit is When entering news articles, the system prioritizes displaying articles that are highly relevant based on the user's geographical location. The system according to feature 1.

Images