System

Abstract

An object of a system according to an embodiment is to easily obtain opinions from different backgrounds and standpoints when reading a news article.SOLUTION: A system includes a news article analysis unit, an opinion generation unit, and a display unit. The news article analysis unit analyzes a news article. The opinion generation unit generates opinions from different backgrounds and points of view based on the content of the news article analyzed by the news article analysis unit. The display unit displays the opinion generated by the opinion generation unit together with the news article.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, when reading news articles, it was difficult to obtain opinions from different backgrounds and perspectives, making it difficult to understand the information from multiple perspectives.

[0005] The system according to the embodiment aims to easily obtain opinions from different backgrounds and standpoints when reading a news article. [Means for solving the problem]

[0006] A system according to an embodiment includes a news article analysis unit, an opinion generation unit, and a display unit. The news article analysis unit analyzes news articles. The opinion generation unit generates opinions from different backgrounds and perspectives based on the content of the news articles analyzed by the news article analysis unit. The display unit displays the opinions generated by the opinion generation unit together with the news article. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy to obtain opinions from different backgrounds and standpoints when reading news articles. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A news article analysis system according to an embodiment of the present invention automatically analyzes news articles, and a generation AI generates and displays opinions from different backgrounds and perspectives. This allows the news article analysis system to promote a multifaceted understanding of news articles.

[0029] A news article analysis system according to an embodiment includes a news article analysis unit, an opinion generation unit, and a display unit. The news article analysis unit analyzes news articles. For example, the news article analysis unit analyzes text data of news articles to understand the perspective and background from which the article was written. The news article analysis unit can also refer to the past writing history of the news article author to identify the author's consistent perspective and tendency. For example, the news article analysis unit identifies the perspective and viewpoint from which the author has written articles in the past and understands that tendency. Furthermore, the news article analysis unit can also perform analysis based on the historical background, taking into account the publication date and social context of the article. For example, the news article analysis unit analyzes articles taking into account important events that occurred during a specific period and social trends. The opinion generation unit generates opinions from different backgrounds and perspectives based on the content of the news article analyzed by the news article analysis unit. For example, the opinion generation unit uses a generation AI to analyze the content of a news article, understand the perspective and background from which the article was written, and generate opinions from different backgrounds and perspectives. The opinion generation unit can also cause the generation AI to refer to opinions on similar news articles in the past to generate more specific opinions. For example, the opinion generation unit generates a specific opinion on a current article based on opinions on past news articles. Furthermore, the opinion generation unit can cause the generation AI to refer to expert opinions and academic papers to generate scientifically based opinions. For example, the opinion generation unit generates an opinion on a current article by referring to expert opinions and academic papers. The display unit displays the opinions generated by the opinion generation unit along with the news article. For example, the display unit displays the opinions generated by the generation AI as "opinions from other perspectives" at the bottom of the news article. The display unit can also summarize the content of the news article and provide different perspectives based on the summary. For example, the display unit generates opinions from different positions or backgrounds based on the summary of the news article. Furthermore, the display unit can provide data and statistical information related to the news article to add an objective perspective. For example, the display unit provides data and statistical information related to the content of the news article.As a result, the news article analysis system according to the embodiment can promote a multifaceted understanding of a news article. For example, the news article analysis system analyzes the content of a news article and generates and displays opinions from different backgrounds and perspectives, allowing readers to have multiple perspectives on a single piece of news. The news article analysis system also summarizes the content of a news article and provides different perspectives based on the summaries, allowing readers to have multiple perspectives. Furthermore, the news article analysis system provides data and statistical information related to the news article and adds an objective perspective, allowing readers to have multiple perspectives.

[0030] The news article analysis unit can refer to the past writing history of the news article writer and identify the writer's consistent position and tendency. For example, the news article analysis unit uses the generative AI to collect the past writing history of the news article writer from a database and analyze that history. For example, the news article analysis unit identifies the position and perspective from which the writer has written articles in the past and understands those tendencies. This allows for a deeper understanding of the background of the article by identifying the position and tendency of the news article writer.

[0031] The news article analysis unit can perform analysis based on the historical context, taking into account the publication date and social background of the news article. For example, the generative AI identifies the publication date of a news article and collects the social background and events of that time from a database. For example, the news article analysis unit analyzes articles taking into account important events and social trends that occurred during a specific period. This allows for a more accurate understanding of the content of a news article by taking into account the historical context.

[0032] The news article analysis unit analyzes multimedia content such as images and videos contained in news articles, and is able to understand the position and background from visual and auditory information as well. For example, the news article analysis unit uses generative AI to analyze images and videos contained in news articles, and understands the position and background of the article from that visual and auditory information. For example, the news article analysis unit identifies the position of the article from the content of the images and the audio of the videos. This allows for a deeper understanding of the position and background of the news article by analyzing the multimedia content.

[0033] The news article analysis unit analyzes the same news article written in different languages ​​and can identify differences in nuance between the languages. For example, the generative AI collects the same news article written in different languages ​​and analyzes its content. For example, the news article analysis unit compares the English and Japanese versions of a news article and identifies differences in nuance between the languages. This makes it possible to understand differences in nuance between languages ​​by analyzing news articles in different languages.

[0034] The news article analysis unit can analyze the citation sources and references of news articles and evaluate their reliability. For example, the news article analysis unit uses a generative AI to collect the citation sources and references of news articles from a database and evaluate their reliability. For example, the news article analysis unit calculates a reliability score for the citation source and evaluates the reliability of the article. This improves the reliability of the article by evaluating the reliability of the citation sources and references of the news article.

[0035] The news article analysis unit can provide historical context by referencing related past news articles and events. For example, the generative AI collects related past news articles and events from a database and uses that information to understand the position and background of the article. For example, the news article analysis unit provides historical context by referencing past events and news articles. This allows the historical context of a news article to be understood by referring to past news articles and events.

[0036] The news article analysis unit can incorporate perspectives from different cultural spheres and regions. For example, the news article analysis unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultural spheres and regions. For example, the news article analysis unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows for a multifaceted understanding of the position and background of a news article by incorporating perspectives from different cultural spheres and regions.

[0037] The news article analysis unit analyzes social media posts and comments and can reflect the opinions of ordinary citizens. For example, the generative AI collects social media posts and comments from a database and uses that information to understand the position and background of a news article. For example, the news article analysis unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, it is possible to reflect the opinions of ordinary citizens in news articles.

[0038] The opinion generation unit can refer to opinions of similar past news articles and generate specific opinions. For example, the generation AI collects similar past news articles from a database and refers to those opinions. For example, the opinion generation unit generates specific opinions about the current article based on opinions of past news articles. This makes it possible to generate more specific opinions by referring to opinions of similar past news articles.

[0039] The opinion generation unit can refer to expert opinions and academic papers to generate opinions based on scientific evidence. For example, the generative AI collects expert opinions and academic papers from a database and generates opinions based on scientific evidence based on that information. For example, the opinion generation unit generates opinions about the current article by referring to expert opinions and academic papers. This makes it possible to generate opinions based on scientific evidence by referring to expert opinions and academic papers.

[0040] The opinion generation unit can incorporate the opinions of experts in different industries and fields. For example, the generation AI collects opinions of experts in different industries and fields from a database, and generates opinions from different backgrounds and perspectives based on that information. For example, the opinion generation unit generates opinions on the current article by referring to the opinions of experts in different industries. In this way, by incorporating the opinions of experts in different industries and fields, it is possible to generate multifaceted opinions on a news article.

[0041] The opinion generation unit can also generate opinions in different languages ​​to provide an international perspective. For example, the generation AI generates opinions in different languages ​​and provides an international perspective based on that information. For example, the opinion generation unit generates opinions in multiple languages, such as English, French, and Chinese. This makes it possible to provide an international perspective on a news article by generating opinions in different languages.

[0042] The display unit can summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit uses a generation AI to summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit generates opinions from different positions and backgrounds based on the article summary. This allows readers to have multiple perspectives by summarizing the content of a news article and providing different perspectives based on that summary.

[0043] The display unit can provide data and statistical information related to the news article and add an objective perspective. For example, the generation AI collects data and statistical information related to the news article from a database, and the display unit provides an objective perspective based on that information. For example, the display unit provides data and statistical information related to the content of the article. This allows the provision of data and statistical information related to the news article to add an objective perspective.

[0044] The display unit can incorporate perspectives from different cultures and regions. For example, the display unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the display unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows the perspectives and backgrounds of news articles to be understood from multiple angles.

[0045] The display unit can analyze social media posts and comments and reflect the opinions of ordinary citizens. For example, the display unit uses a generative AI to collect social media posts and comments from a database and understand the position and background of a news article based on that information. For example, the display unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, the opinions of ordinary citizens can be reflected in news articles.

[0046] The news article analysis unit can verify the content of news articles and perform fact-checking. For example, the news article analysis unit uses a generation AI to verify the content of news articles and perform fact-checking based on that information. For example, the news article analysis unit checks whether the content of the article is based on facts. In this way, verifying the content of news articles and performing fact-checking improves the reliability of the articles.

[0047] The news article analysis unit can analyze the citation sources and references of news articles and evaluate their reliability. For example, the news article analysis unit uses a generative AI to collect the citation sources and references of news articles from a database and evaluate their reliability. For example, the news article analysis unit calculates a reliability score for the citation source and evaluates the reliability of the article. This improves the reliability of the article by evaluating the reliability of the citation sources and references of the news article.

[0048] The news article analysis unit can incorporate perspectives from different cultures and regions. For example, the news article analysis unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the news article analysis unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows for a multifaceted understanding of the position and background of a news article by incorporating perspectives from different cultures and regions.

[0049] The news article analysis unit analyzes social media posts and comments and can reflect the opinions of ordinary citizens. For example, the generative AI collects social media posts and comments from a database and uses that information to understand the position and background of a news article. For example, the news article analysis unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, it is possible to reflect the opinions of ordinary citizens in news articles.

[0050] The display unit can summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit uses a generation AI to summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit generates opinions from different positions and backgrounds based on the article summary. This allows readers to have multiple perspectives by summarizing the content of a news article and providing different perspectives based on that summary.

[0051] The display unit can provide data and statistical information related to the news article and add an objective perspective. For example, the generation AI collects data and statistical information related to the news article from a database, and the display unit provides an objective perspective based on that information. For example, the display unit provides data and statistical information related to the content of the article. This allows the provision of data and statistical information related to the news article to add an objective perspective.

[0052] The display unit can incorporate perspectives from different cultures and regions. For example, the display unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the display unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows the perspectives and backgrounds of news articles to be understood from multiple angles.

[0053] The display unit can analyze social media posts and comments and reflect the opinions of the general public. For example, the generation AI collects social media posts and comments from a database, and uses that information to reflect the opinions of the general public when using the information for educational purposes. For example, the display unit adjusts lesson content based on opinions on social media. In this way, by analyzing social media posts and comments, the opinions of the general public can be reflected in news articles.

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

[0055] The news article analysis system can further include a reliability evaluation unit that evaluates the reliability of a news article. The reliability evaluation unit analyzes the citation sources and references of a news article to evaluate their reliability. For example, the reliability evaluation unit can calculate a reliability score for the citation source and evaluate the reliability of the article. The reliability evaluation unit can also verify the content of the news article and perform fact-checking. For example, the reliability evaluation unit can confirm whether the content of the article is based on facts. Furthermore, the reliability evaluation unit can refer to the past writing history of the news article author and evaluate its consistency. This can improve the reliability of the news article.

[0056] The news article analysis system may further include a fact-checking unit that verifies the content of a news article. The fact-checking unit verifies the content of the news article and performs fact-checking based on the information. For example, the fact-checking unit may confirm whether the content of the article is based on facts. The fact-checking unit may also analyze the citations and references of the news article to evaluate their reliability. For example, the fact-checking unit may calculate a reliability score for the citations and evaluate the reliability of the article. Furthermore, the fact-checking unit may refer to the past writing history of the news article author and evaluate its consistency. This may improve the reliability of the news article.

[0057] The news article analysis system may further include a summarizing unit that summarizes the content of the news article. The summarizing unit summarizes the content of the news article and provides different perspectives based on the summaries. For example, the summarizing unit may generate opinions from different positions or backgrounds based on the article summaries. The summarizing unit may also provide data and statistical information related to the news article and add an objective perspective. For example, the summarizing unit may provide data and statistical information related to the content of the news article. Furthermore, the summarizing unit may summarize the content of the news article and provide different perspectives based on the summaries, thereby allowing readers to have multiple perspectives. Thus, by summarizing the content of the news article and providing different perspectives based on the summaries, readers may have multiple perspectives.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The news article analysis unit analyzes the news article. For example, the news article analysis unit analyzes the text data of the news article to understand the position and background from which the article was written. The news article analysis unit can also refer to the past writing history of the news article author to identify the author's consistent position and tendencies. Furthermore, the news article analysis unit can also perform an analysis based on the historical background, taking into account the publication date and social context of the article. Step 2: The opinion generation unit generates opinions from different backgrounds and positions based on the content of the news article analyzed by the news article analysis unit. For example, the opinion generation unit uses a generation AI to analyze the content of a news article, understand the position and background from which the article was written, and generate opinions from different backgrounds and positions. The opinion generation unit can also have the generation AI refer to opinions from similar news articles in the past to generate more specific opinions. Furthermore, the opinion generation unit can have the generation AI refer to expert opinions and academic papers to generate opinions based on scientific evidence. Step 3: The display unit displays the opinions generated by the opinion generation unit along with the news article. For example, the display unit displays the opinions generated by the generation AI as "opinions from other perspectives" at the bottom of the news article. The display unit can also summarize the content of the news article and provide different perspectives based on that summary. Furthermore, the display unit can provide related data and statistical information for the news article to add an objective perspective.

[0060] (Example 2) A news article analysis system according to an embodiment of the present invention automatically analyzes news articles, and a generation AI generates and displays opinions from different backgrounds and perspectives. This allows the news article analysis system to promote a multifaceted understanding of news articles.

[0061] A news article analysis system according to an embodiment includes a news article analysis unit, an opinion generation unit, and a display unit. The news article analysis unit analyzes news articles. For example, the news article analysis unit analyzes text data of news articles to understand the perspective and background from which the article was written. The news article analysis unit can also refer to the past writing history of the news article author to identify the author's consistent perspective and tendency. For example, the news article analysis unit identifies the perspective and viewpoint from which the author has written articles in the past and understands that tendency. Furthermore, the news article analysis unit can also perform analysis based on the historical background, taking into account the publication date and social context of the article. For example, the news article analysis unit analyzes articles taking into account important events that occurred during a specific period and social trends. The opinion generation unit generates opinions from different backgrounds and perspectives based on the content of the news article analyzed by the news article analysis unit. For example, the opinion generation unit uses a generation AI to analyze the content of a news article, understand the perspective and background from which the article was written, and generate opinions from different backgrounds and perspectives. The opinion generation unit can also cause the generation AI to refer to opinions on similar news articles in the past to generate more specific opinions. For example, the opinion generation unit generates a specific opinion on a current article based on opinions on past news articles. Furthermore, the opinion generation unit can cause the generation AI to refer to expert opinions and academic papers to generate scientifically based opinions. For example, the opinion generation unit generates an opinion on a current article by referring to expert opinions and academic papers. The display unit displays the opinions generated by the opinion generation unit along with the news article. For example, the display unit displays the opinions generated by the generation AI as "opinions from other perspectives" at the bottom of the news article. The display unit can also summarize the content of the news article and provide different perspectives based on the summary. For example, the display unit generates opinions from different positions or backgrounds based on the summary of the news article. Furthermore, the display unit can provide data and statistical information related to the news article to add an objective perspective. For example, the display unit provides data and statistical information related to the content of the news article.As a result, the news article analysis system according to the embodiment can promote a multifaceted understanding of a news article. For example, the news article analysis system analyzes the content of a news article and generates and displays opinions from different backgrounds and perspectives, allowing readers to have multiple perspectives on a single piece of news. The news article analysis system also summarizes the content of a news article and provides different perspectives based on the summaries, allowing readers to have multiple perspectives. Furthermore, the news article analysis system provides data and statistical information related to the news article and adds an objective perspective, allowing readers to have multiple perspectives.

[0062] The news article analysis unit can refer to the past writing history of the news article writer and identify the writer's consistent position and tendency. For example, the news article analysis unit uses the generative AI to collect the past writing history of the news article writer from a database and analyze that history. For example, the news article analysis unit identifies the position and perspective from which the writer has written articles in the past and understands those tendencies. This allows for a deeper understanding of the background of the article by identifying the position and tendency of the news article writer.

[0063] The news article analysis unit can perform analysis based on the historical context, taking into account the publication date and social background of the news article. For example, the generative AI identifies the publication date of a news article and collects the social background and events of that time from a database. For example, the news article analysis unit analyzes articles taking into account important events and social trends that occurred during a specific period. This allows for a more accurate understanding of the content of a news article by taking into account the historical context.

[0064] The news article analysis unit can use the emotion estimation function to estimate the emotions of the writer of a news article and analyze how those emotions affect the content of the article. For example, the news article analysis unit uses a generative AI to analyze the text data of a news article and estimate the writer's emotions. For example, the news article analysis unit identifies the writer's emotions from the article's writing style and expressions, and analyzes how those emotions affect the content of the article. This allows for a deeper understanding of the content of the article by analyzing the writer's emotions.

[0065] The news article analysis unit analyzes multimedia content such as images and videos contained in news articles, and is able to understand the position and background from visual and auditory information as well. For example, the news article analysis unit uses generative AI to analyze images and videos contained in news articles, and understands the position and background of the article from that visual and auditory information. For example, the news article analysis unit identifies the position of the article from the content of the images and the audio of the videos. This allows for a deeper understanding of the position and background of the news article by analyzing the multimedia content.

[0066] The news article analysis unit analyzes the same news article written in different languages ​​and can identify differences in nuance between the languages. For example, the generative AI collects the same news article written in different languages ​​and analyzes its content. For example, the news article analysis unit compares the English and Japanese versions of a news article and identifies differences in nuance between the languages. This makes it possible to understand differences in nuance between languages ​​by analyzing news articles in different languages.

[0067] The news article analysis unit can use the emotion estimation function to collect readers' emotional reactions to news articles in real time and reflect that data in the analysis. For example, the news article analysis unit uses a generative AI to collect readers' emotional reactions to news articles in real time and analyzes the article based on that data. For example, the news article analysis unit identifies the position and perspective of an article based on the reader's emotion score. This allows readers' emotional reactions to be collected in real time and reflected in the analysis, making it possible to understand how the content of a news article is being received.

[0068] The news article analysis unit can analyze the citation sources and references of news articles and evaluate their reliability. For example, the news article analysis unit uses a generative AI to collect the citation sources and references of news articles from a database and evaluate their reliability. For example, the news article analysis unit calculates a reliability score for the citation source and evaluates the reliability of the article. This improves the reliability of the article by evaluating the reliability of the citation sources and references of the news article.

[0069] The news article analysis unit can provide historical context by referencing related past news articles and events. For example, the generative AI collects related past news articles and events from a database and uses that information to understand the position and background of the article. For example, the news article analysis unit provides historical context by referencing past events and news articles. This allows the historical context of a news article to be understood by referring to past news articles and events.

[0070] The news article analysis unit uses the emotion estimation function to analyze readers' emotional reactions to the position and background of a news article, and can deepen understanding of the position and background based on those reactions. For example, the news article analysis unit uses a generative AI to collect readers' emotional reactions to the position and background of a news article in real time, and understands the position and background of the article based on that data. For example, the news article analysis unit identifies the position of an article based on the reader's emotion score. This makes it possible to deepen understanding of the position and background of a news article by analyzing readers' emotional reactions.

[0071] The news article analysis unit can incorporate perspectives from different cultural spheres and regions. For example, the news article analysis unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultural spheres and regions. For example, the news article analysis unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows for a multifaceted understanding of the position and background of a news article by incorporating perspectives from different cultural spheres and regions.

[0072] The news article analysis unit analyzes social media posts and comments and can reflect the opinions of ordinary citizens. For example, the generative AI collects social media posts and comments from a database and uses that information to understand the position and background of a news article. For example, the news article analysis unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, it is possible to reflect the opinions of ordinary citizens in news articles.

[0073] The news article analysis unit can use the emotion estimation function to collect emotional reactions from different age groups and genders to the position and background of a news article and reflect that data in the analysis. For example, the news article analysis unit uses a generative AI to collect emotional reactions from different age groups and genders to the position and background of a news article in real time, and understands the position and background of the article based on that data. For example, the news article analysis unit identifies the position of an article based on the emotion scores of different age groups and genders. This allows for a deeper understanding of the position and background of a news article by collecting emotional reactions from different age groups and genders and reflecting them in the analysis.

[0074] The opinion generation unit can refer to opinions of similar past news articles and generate specific opinions. For example, the generation AI collects similar past news articles from a database and refers to those opinions. For example, the opinion generation unit generates specific opinions about the current article based on opinions of past news articles. This makes it possible to generate more specific opinions by referring to opinions of similar past news articles.

[0075] The opinion generation unit can refer to expert opinions and academic papers to generate opinions based on scientific evidence. For example, the generative AI collects expert opinions and academic papers from a database and generates opinions based on scientific evidence based on that information. For example, the opinion generation unit generates opinions about the current article by referring to expert opinions and academic papers. This makes it possible to generate opinions based on scientific evidence by referring to expert opinions and academic papers.

[0076] The opinion generation unit uses the emotion estimation function to predict readers' emotional reactions when generating opinions from different backgrounds and positions, and can adjust opinions based on those reactions. For example, the generative AI in the opinion generation unit predicts readers' emotional reactions to a news article, and generates opinions from different backgrounds and positions based on that data. For example, the opinion generation unit adjusts opinions based on the reader's emotional score. This makes it possible to generate more appropriate opinions by predicting readers' emotional reactions and adjusting opinions based on those reactions.

[0077] The opinion generation unit can incorporate the opinions of experts in different industries and fields. For example, the generation AI collects opinions of experts in different industries and fields from a database, and generates opinions from different backgrounds and perspectives based on that information. For example, the opinion generation unit generates opinions on the current article by referring to the opinions of experts in different industries. In this way, by incorporating the opinions of experts in different industries and fields, it is possible to generate multifaceted opinions on a news article.

[0078] The opinion generation unit can also generate opinions in different languages ​​to provide an international perspective. For example, the generation AI generates opinions in different languages ​​and provides an international perspective based on that information. For example, the opinion generation unit generates opinions in multiple languages, such as English, French, and Chinese. This makes it possible to provide an international perspective on a news article by generating opinions in different languages.

[0079] The opinion generation unit uses the emotion estimation function to monitor readers' emotional reactions in real time when generating opinions from different backgrounds and positions, and can generate optimal opinions. For example, the opinion generation unit uses a generation AI to monitor readers' emotional reactions to news articles in real time and generate opinions from different backgrounds and positions based on that data. For example, the opinion generation unit adjusts opinions based on the reader's emotional score. This makes it possible to provide more appropriate opinions by monitoring readers' emotional reactions in real time and generating optimal opinions based on those reactions.

[0080] The display unit can summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit uses a generation AI to summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit generates opinions from different positions and backgrounds based on the article summary. This allows readers to have multiple perspectives by summarizing the content of a news article and providing different perspectives based on that summary.

[0081] The display unit can provide data and statistical information related to the news article and add an objective perspective. For example, the generation AI collects data and statistical information related to the news article from a database, and the display unit provides an objective perspective based on that information. For example, the display unit provides data and statistical information related to the content of the article. This allows the provision of data and statistical information related to the news article to add an objective perspective.

[0082] The display unit can use the emotion estimation function to analyze the reader's emotional response to the content of a news article and adjust the perspective based on that response. For example, the display unit uses a generative AI to collect readers' emotional responses to a news article in real time and provide a multifaceted perspective based on that data. For example, the display unit adjusts the perspective based on the reader's emotion score. This allows the display unit to provide a more appropriate perspective by analyzing the reader's emotional response and adjusting the perspective based on that response.

[0083] The display unit can incorporate perspectives from different cultures and regions. For example, the display unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the display unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows the perspectives and backgrounds of news articles to be understood from multiple angles.

[0084] The display unit can analyze social media posts and comments and reflect the opinions of ordinary citizens. For example, the display unit uses a generative AI to collect social media posts and comments from a database and understand the position and background of a news article based on that information. For example, the display unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, the opinions of ordinary citizens can be reflected in news articles.

[0085] The display unit can use the emotion estimation function to collect emotional reactions from different age groups and genders to the content of a news article and reflect that data in providing perspectives. For example, the display unit uses a generation AI to collect emotional reactions from different age groups and genders to a news article in real time and provide a multifaceted perspective based on that data. For example, the display unit adjusts the perspective based on the emotion scores of different age groups and genders. This makes it possible to provide a more multifaceted perspective by collecting emotional reactions from different age groups and genders and reflecting that data in providing perspectives.

[0086] The news article analysis unit can verify the content of news articles and perform fact-checking. For example, the news article analysis unit uses a generation AI to verify the content of news articles and perform fact-checking based on that information. For example, the news article analysis unit checks whether the content of the article is based on facts. In this way, verifying the content of news articles and performing fact-checking improves the reliability of the articles.

[0087] The news article analysis unit can analyze the citation sources and references of news articles and evaluate their reliability. For example, the news article analysis unit uses a generative AI to collect the citation sources and references of news articles from a database and evaluate their reliability. For example, the news article analysis unit calculates a reliability score for the citation source and evaluates the reliability of the article. This improves the reliability of the article by evaluating the reliability of the citation sources and references of the news article.

[0088] The news article analysis unit uses the emotion estimation function to analyze readers' emotional reactions to news articles, identify parts that are likely to lead to bias or misunderstanding, and provide supplementary explanations for those parts. For example, the news article analysis unit uses the generative AI to collect readers' emotional reactions to news articles in real time, and identifies parts that are likely to lead to bias or misunderstanding based on that data. For example, the news article analysis unit identifies parts that are likely to lead to bias or misunderstanding based on the reader's emotion score. This allows the reader's emotional reactions to be analyzed, parts that are likely to lead to bias or misunderstanding to be identified, and supplementary explanations for those parts to deepen understanding of the article.

[0089] The news article analysis unit can incorporate perspectives from different cultures and regions. For example, the news article analysis unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the news article analysis unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows for a multifaceted understanding of the position and background of a news article by incorporating perspectives from different cultures and regions.

[0090] The news article analysis unit analyzes social media posts and comments and can reflect the opinions of ordinary citizens. For example, the generative AI collects social media posts and comments from a database and uses that information to understand the position and background of a news article. For example, the news article analysis unit reflects the opinions of ordinary citizens. In this way, by analyzing social media posts and comments, it is possible to reflect the opinions of ordinary citizens in news articles.

[0091] The news article analysis unit uses the emotion estimation function to monitor readers' emotional reactions to news articles in real time, identify parts that are likely to lead to bias or misunderstanding, and provide supplementary explanations for those parts. For example, the news article analysis unit uses the generative AI to collect readers' emotional reactions to news articles in real time and identify parts that are likely to lead to bias or misunderstanding based on that data. For example, the news article analysis unit identifies parts that are likely to lead to bias or misunderstanding based on the reader's emotion score. This allows readers' emotional reactions to be monitored in real time, identify parts that are likely to lead to bias or misunderstanding, and provide supplementary explanations for those parts, thereby deepening their understanding of the article.

[0092] The display unit can summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit uses a generation AI to summarize the content of a news article and provide different perspectives based on that summary. For example, the display unit generates opinions from different positions and backgrounds based on the article summary. This allows readers to have multiple perspectives by summarizing the content of a news article and providing different perspectives based on that summary.

[0093] The display unit can provide data and statistical information related to the news article and add an objective perspective. For example, the generation AI collects data and statistical information related to the news article from a database, and the display unit provides an objective perspective based on that information. For example, the display unit provides data and statistical information related to the content of the article. This allows the provision of data and statistical information related to the news article to add an objective perspective.

[0094] The display unit can use the emotion estimation function to analyze students' emotional reactions to the content of a news article and adjust the viewpoint based on that reaction. For example, the display unit uses a generative AI to collect students' emotional reactions to a news article in real time and provides a viewpoint for educational use based on that data. For example, the display unit adjusts the viewpoint based on the student's emotion score. This allows the display unit to provide a more appropriate viewpoint by analyzing students' emotional reactions and adjusting the viewpoint based on those reactions.

[0095] The display unit can incorporate perspectives from different cultures and regions. For example, the display unit collects news articles and cultural backgrounds from a database so that the generation AI can incorporate perspectives from different cultures and regions. For example, the display unit analyzes news articles from different cultural spheres and incorporates their perspectives. This allows the perspectives and backgrounds of news articles to be understood from multiple angles.

[0096] The display unit can analyze social media posts and comments and reflect the opinions of the general public. For example, the generation AI collects social media posts and comments from a database, and uses that information to reflect the opinions of the general public when using the information for educational purposes. For example, the display unit adjusts lesson content based on opinions on social media. In this way, by analyzing social media posts and comments, the opinions of the general public can be reflected in news articles.

[0097] The display unit can use the emotion estimation function to collect emotional responses from different age groups and genders to the content of a news article and reflect that data in providing perspectives. For example, the display unit uses a generation AI to collect emotional responses from different age groups and genders to a news article in real time and provides perspectives for educational use based on that data. For example, the display unit adjusts the perspective based on the emotion scores of different age groups and genders. This allows for the provision of more diverse perspectives by collecting emotional responses from different age groups and genders and reflecting that data in providing perspectives.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The news article analysis system can further include a reliability evaluation unit that evaluates the reliability of a news article. The reliability evaluation unit analyzes the citation sources and references of a news article to evaluate their reliability. For example, the reliability evaluation unit can calculate a reliability score for the citation source and evaluate the reliability of the article. The reliability evaluation unit can also verify the content of the news article and perform fact-checking. For example, the reliability evaluation unit can confirm whether the content of the article is based on facts. Furthermore, the reliability evaluation unit can refer to the past writing history of the news article author and evaluate its consistency. This can improve the reliability of the news article.

[0100] The news article analysis system may further include a fact-checking unit that verifies the content of a news article. The fact-checking unit verifies the content of the news article and performs fact-checking based on the information. For example, the fact-checking unit may confirm whether the content of the article is based on facts. The fact-checking unit may also analyze the citations and references of the news article to evaluate their reliability. For example, the fact-checking unit may calculate a reliability score for the citations and evaluate the reliability of the article. Furthermore, the fact-checking unit may refer to the past writing history of the news article author and evaluate its consistency. This may improve the reliability of the news article.

[0101] The news article analysis system may further include a summarizing unit that summarizes the content of the news article. The summarizing unit summarizes the content of the news article and provides different perspectives based on the summaries. For example, the summarizing unit may generate opinions from different positions or backgrounds based on the article summaries. The summarizing unit may also provide data and statistical information related to the news article and add an objective perspective. For example, the summarizing unit may provide data and statistical information related to the content of the news article. Furthermore, the summarizing unit may summarize the content of the news article and provide different perspectives based on the summaries, thereby allowing readers to have multiple perspectives. Thus, by summarizing the content of the news article and providing different perspectives based on the summaries, readers may have multiple perspectives.

[0102] The news article analysis system can further include an emotion collection unit that collects readers' emotional reactions to the content of a news article. The emotion collection unit collects readers' emotional reactions to a news article in real time and analyzes the article based on the data. For example, the emotion collection unit can identify the position or viewpoint of the article based on the reader's emotion score. The emotion collection unit can also analyze the reader's emotional reaction to the content of the news article and adjust the viewpoint based on the reaction. For example, the emotion collection unit adjusts the viewpoint based on the reader's emotion score. Furthermore, the emotion collection unit can understand how the content of the news article is being received by collecting readers' emotional reactions to the content of the news article and analyzing the article based on the data.

[0103] The news article analysis system may further include an emotion collection unit that collects emotional reactions of different age groups and genders to the content of a news article. The emotion collection unit collects the emotional reactions of different age groups and genders to the news article in real time and analyzes the article based on the data. For example, the emotion collection unit may identify the position or viewpoint of the article based on the emotion scores of different age groups and genders. The emotion collection unit may also analyze the emotional reactions of different age groups and genders to the content of the news article and adjust the viewpoint based on the reactions. For example, the emotion collection unit may adjust the viewpoint based on the emotion scores of different age groups and genders. The emotion collection unit may also collect the emotional reactions of different age groups and genders to the content of the news article and analyze the article based on the data, thereby understanding how the content of the news article is being received.

[0104] The news article analysis system may further include an emotion monitoring unit that monitors readers' emotional reactions to the content of a news article in real time. The emotion monitoring unit collects readers' emotional reactions to a news article in real time and analyzes the article based on that data. For example, the emotion monitoring unit may identify the position or viewpoint of the article based on the reader's emotion score. The emotion monitoring unit may also analyze readers' emotional reactions to the content of the news article and adjust the viewpoint based on that reaction. For example, the emotion monitoring unit may adjust the viewpoint based on the reader's emotion score. Furthermore, the emotion monitoring unit may collect readers' emotional reactions to the content of the news article in real time and analyze the article based on that data, thereby understanding how the content of the news article is being received.

[0105] The news article analysis system can further include an emotion prediction unit that predicts a reader's emotional response to the content of a news article. The emotion prediction unit predicts a reader's emotional response to a news article and analyzes the article based on the data. For example, the emotion prediction unit can identify the position or viewpoint of the article based on the reader's emotion score. The emotion prediction unit can also analyze the reader's emotional response to the content of the news article and adjust the viewpoint based on the reaction. For example, the emotion prediction unit adjusts the viewpoint based on the reader's emotion score. Furthermore, the emotion prediction unit can predict a reader's emotional response to the content of the news article and analyze the article based on the data, thereby understanding how the content of the news article is being received.

[0106] The news article analysis system may further include a sentiment analysis unit that analyzes readers' emotional reactions to the content of a news article. The sentiment analysis unit collects readers' emotional reactions to a news article in real time and analyzes the article based on that data. For example, the sentiment analysis unit may identify the position or viewpoint of the article based on the reader's emotional score. The sentiment analysis unit may also analyze readers' emotional reactions to the content of the news article and adjust the viewpoint based on that reaction. For example, the sentiment analysis unit may adjust the viewpoint based on the reader's emotional score. Furthermore, the sentiment analysis unit may collect readers' emotional reactions to the content of the news article and analyze the article based on that data, thereby understanding how the content of the news article is being received.

[0107] The news article analysis system can further include an emotion collection unit that collects readers' emotional reactions to the content of a news article. The emotion collection unit collects readers' emotional reactions to a news article in real time and analyzes the article based on the data. For example, the emotion collection unit can identify the position or viewpoint of the article based on the reader's emotion score. The emotion collection unit can also analyze the reader's emotional reaction to the content of the news article and adjust the viewpoint based on the reaction. For example, the emotion collection unit adjusts the viewpoint based on the reader's emotion score. Furthermore, the emotion collection unit can understand how the content of the news article is being received by collecting readers' emotional reactions to the content of the news article and analyzing the article based on the data.

[0108] The news article analysis system may further include an emotion monitoring unit that monitors readers' emotional reactions to the content of a news article in real time. The emotion monitoring unit collects readers' emotional reactions to a news article in real time and analyzes the article based on that data. For example, the emotion monitoring unit may identify the position or viewpoint of the article based on the reader's emotion score. The emotion monitoring unit may also analyze readers' emotional reactions to the content of the news article and adjust the viewpoint based on that reaction. For example, the emotion monitoring unit may adjust the viewpoint based on the reader's emotion score. Furthermore, the emotion monitoring unit may collect readers' emotional reactions to the content of the news article in real time and analyze the article based on that data, thereby understanding how the content of the news article is being received.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The news article analysis unit analyzes the news article. For example, the news article analysis unit analyzes the text data of the news article to understand the position and background from which the article was written. The news article analysis unit can also refer to the past writing history of the news article author to identify the author's consistent position and tendencies. Furthermore, the news article analysis unit can also perform an analysis based on the historical background, taking into account the publication date and social context of the article. Step 2: The opinion generation unit generates opinions from different backgrounds and positions based on the content of the news article analyzed by the news article analysis unit. For example, the opinion generation unit uses a generation AI to analyze the content of a news article, understand the position and background from which the article was written, and generate opinions from different backgrounds and positions. The opinion generation unit can also have the generation AI refer to opinions from similar news articles in the past to generate more specific opinions. Furthermore, the opinion generation unit can have the generation AI refer to expert opinions and academic papers to generate opinions based on scientific evidence. Step 3: The display unit displays the opinions generated by the opinion generation unit along with the news article. For example, the display unit displays the opinions generated by the generation AI as "opinions from other perspectives" at the bottom of the news article. The display unit can also summarize the content of the news article and provide different perspectives based on that summary. Furthermore, the display unit can provide related data and statistical information for the news article to add an objective perspective.

[0111] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

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

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

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

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

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A department that analyzes news articles, an opinion generation unit that generates opinions from different backgrounds and standpoints based on the content of the news article analyzed by the news article analysis unit; a display unit that displays the opinions generated by the opinion generation unit together with news articles. A system characterized by:

2. The news article analysis department Analyze the multimedia content, such as images and videos, contained in the news article to understand the position and background from visual and auditory information.

2. The system of claim 1.

3. The opinion generation unit Generate a specific opinion by referring to the opinions of similar past news articles.

2. The system of claim 1.

4. The display is Summarizing the content of the news article and providing different perspectives based on the summaries 2. The system of claim 1.

5. The news article analysis department The emotions of the writer of the news article are estimated, and how the emotions affect the content of the news article is analyzed.

2. The system of claim 1.

Images