System

Abstract

An object of a system according to an embodiment is to enable a user to easily grasp the credibility and other opinions of a news article.SOLUTION: A system according to the embodiment has a AI interpretation function. A AI interpretation function is added to the news article. The AI interpretation function displays a summarized interpretation of the credibility or other view of the news article. When the user clicks the AI interpretation function, the interpretation is displayed. The AI interpretation function has a pop design. The AI interpretation functionality includes repost functionality, citation repost functionality, and share functionality.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] Conventional technologies have had the problem that it is difficult for users to easily grasp the credibility of news articles and other opinions.

[0005] The system according to the embodiment aims to enable users to easily understand the credibility of news articles and other opinions. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI interpretation function. The AI ​​interpretation function is added to a news article. The AI ​​interpretation function displays an interpretation that summarizes the credibility of the news article or other opinions. The interpretation is displayed when a user clicks on the AI ​​interpretation function. The AI ​​interpretation function has a pop design. The AI ​​interpretation function includes a repost function, a quote repost function, and a share function. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily understand the credibility of news articles and other opinions. [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 interpretation system according to an embodiment of the present invention adds an AI interpretation function to a news article, and when a user clicks on the function, an AI interpretation summarizing the credibility of the news and other views is displayed. This allows the news interpretation system to provide users with information about the credibility of the news article and other views, thereby promoting information sharing.

[0029] A news interpretation system according to an embodiment includes an AI interpretation function, a generation AI, a repost function, a quote repost function, and a share function. The AI ​​interpretation function is added to a news article. For example, it analyzes the content of the news article and provides information about its credibility and other opinions. The generation AI analyzes the content of the news article and provides information about its credibility and other opinions. For example, it evaluates the credibility of the news article and summarizes opinions from other reliable sources. The input to the generation AI is a prompt containing the content of the news article, and the generation AI generates an interpretation based on the prompt. The repost function is a function for sharing the AI ​​interpretation with other users. For example, by pressing the repost button, a user can share the AI ​​interpretation with their followers. The quote repost function is a function for adding their own comments to the AI ​​interpretation and sharing it. For example, by pressing the quote repost button, a user can add their own comments and share the AI ​​interpretation. The share function is a function for sharing the AI ​​interpretation on other platforms. For example, by pressing the share button, a user can share the AI ​​interpretation on social media such as Facebook or Twitter. This allows the news interpretation system according to the embodiment to provide users with information about the credibility of news articles and other opinions, thereby promoting the sharing of information.

[0030] The AI ​​interpretation function can analyze the content of a news article as well as the article's author's past credibility assessments and biases to generate an overall credibility score. For example, when analyzing the content of a news article, the AI ​​interpretation function takes into account the author's past credibility assessments and biases. For example, it can retrieve from a database what kind of articles the author has written in the past and how credible those articles were, and generate an overall credibility score. This allows for more accurate assessment of the credibility of news articles.

[0031] The AI ​​interpretation function takes into account the publication date and update history of a news article and can provide an interpretation based on the latest information. For example, when the generation AI analyzes the content of a news article, it also takes into account the publication date and update history of the article and provides an interpretation based on the latest information. For example, if an article is updated, it analyzes the updated content and generates an interpretation based on the latest information. This makes it possible to provide an interpretation based on the latest information.

[0032] The AI ​​interpretation function analyzes not only news articles but also social media posts and comments, making it possible to provide interpretations from a wider range of sources. For example, the AI ​​interpretation function analyzes not only news articles but also social media posts and comments. For example, it collects Twitter and Facebook posts and generates interpretations based on that information. This makes it possible to provide interpretations from a wider range of sources.

[0033] The AI ​​interpretation function can also provide the interpretation analyzed by the generation AI in audio or video format, allowing information to be conveyed visually or aurally. For example, the AI ​​interpretation function can provide the interpretation analyzed by the generation AI in audio format. For example, a function to read out the interpretation of a news article aloud can be added, providing information to the visually impaired. This allows information to be conveyed visually or aurally.

[0034] The AI ​​interpretation function can provide individually customized pop designs based on the user's past click history and interests. For example, the AI ​​interpretation function analyzes the user's past click history and provides individually customized pop designs based on that data. For example, it can reflect the user's preferred colors and design elements. This makes it possible to provide customized designs based on the user's interests.

[0035] The AI ​​interpretation function can add interactive elements to a design, so that different animations and effects are displayed each time the user clicks. For example, the AI ​​interpretation function can add interactive elements to a design, so that different animations are displayed each time the user clicks. For example, it can add a button that changes color or a moving icon each time it is clicked. This allows users to enjoy using the AI ​​interpretation function.

[0036] The AI ​​interpretation function automatically changes the design according to the season or event, always providing a fresh experience. The AI ​​interpretation function automatically changes the design according to the season, for example, displaying a cherry blossom design in spring and an ocean design in summer. This allows the design to automatically change according to the season or event.

[0037] The AI ​​interpretation function can add a function that allows users to customize designs, allowing them to create designs that suit their preferences. The AI ​​interpretation function can, for example, add a function that allows users to customize designs. For example, it can provide an interface that allows users to select colors and fonts. This allows users to create designs that suit their preferences.

[0038] The AI ​​interpretation function can make a comprehensive assessment of the credibility of a news article, taking into account the reliability of the article's citation source and references. For example, the AI ​​interpretation function can evaluate the credibility of a news article based on the credibility score of the citation source. This allows for more accurate assessment of the credibility of news articles.

[0039] The AI ​​interpretation function allows the generative AI to incorporate opinions from different perspectives and positions in a balanced manner when providing other views. The AI ​​interpretation function, for example, allows the generative AI to incorporate opinions from different perspectives and positions in a balanced manner when providing other views. For example, it can collect different opinions from political positions or fields of expertise and generate an interpretation based on them. This makes it possible to provide views that incorporate opinions from different perspectives and positions in a balanced manner.

[0040] The AI ​​interpretation function adds a feature that allows users to vote on the credibility of news articles in real time, making it possible to utilize collective intelligence. For example, the AI ​​interpretation function adds a feature that allows users to vote on the credibility of news articles in real time. For example, it provides an interface where users can vote on credibility scores. This allows credibility to be evaluated based on user votes.

[0041] When providing other views, the AI ​​interpretation function can also display related past news articles and data, making it easier to understand the background of the information.For example, when providing other views, the AI ​​interpretation function can also display related past news articles and data.For example, it can display links to past news articles to provide background to the information.This makes it easier to understand the background of the information.

[0042] The AI ​​interpretation function can provide an interface that allows users to easily add comments and tags when reposting or sharing. The AI ​​interpretation function can provide an interface that allows users to easily add comments when reposting or sharing. For example, it can display a comment input field so that users can freely add comments. This allows users to easily add comments and tags.

[0043] The AI ​​interpretation function tracks the spread of shared AI interpretations and can analyze which users have the most influence. The AI ​​interpretation function tracks the spread of shared AI interpretations, for example, by analyzing the number of times they have been shared and the number of reaches, and visualizing the spread. This makes it possible to track the spread of shared AI interpretations and identify influential users.

[0044] The AI ​​interpretation function strengthens collaboration between different social media platforms when reposting or sharing, facilitating the spread of information. The AI ​​interpretation function strengthens collaboration between different social media platforms when reposting or sharing, for example, by providing the ability to share to multiple platforms simultaneously, such as Twitter, Facebook, and Instagram. This strengthens collaboration between different social media platforms and promotes the spread of information.

[0045] The AI ​​interpretation function can collect feedback on shared AI interpretations and improve the accuracy of the interpretations based on that feedback. The AI ​​interpretation function, for example, collects feedback on shared AI interpretations. For example, it provides an interface where users can post comments and ratings. This makes it possible to improve the accuracy of the interpretations based on the feedback.

[0046] The AI ​​interpretation function can dynamically optimize the layout of the user interface based on the user's operation history. For example, the AI ​​interpretation function analyzes the user's operation history and dynamically optimizes the layout of the user interface based on that data. For example, it places frequently used functions in prominent positions. This makes it possible to optimize the interface layout based on the user's operation history.

[0047] The AI ​​interpretation function analyzes the user's gaze tracking data for each element of the interface and places important information in the position that attracts the most attention.The AI ​​interpretation function, for example, analyzes the user's gaze tracking data and places each element of the interface based on that data.For example, it places important information in the position that attracts the user's most attention.This allows important information to be placed in the optimal position based on the user's gaze tracking data.

[0048] The AI ​​interpretation function can optimize the user interface for different devices (smartphones, tablets, PCs) to provide a seamless experience.The AI ​​interpretation function can optimize the user interface for different devices, for example, by providing a layout that is suitable for each device (smartphone, tablet, PC).This allows the user to provide an interface that is optimized for different devices, providing a seamless experience.

[0049] The AI ​​interpretation function can add functionality that allows users to freely change the interface theme or color scheme. For example, the AI ​​interpretation function can provide an interface that allows users to freely change the interface theme or color scheme. This allows users to freely change the interface theme or color scheme.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The news interpretation system can analyze a user's past news browsing history and provide an individually customized news feed. For example, it can prioritize relevant news articles based on the user's past interest in news categories or topics. It can also prioritize news from specific news sources if the user prefers them. Furthermore, it can automatically recommend related news articles based on the user's browsing history.

[0052] The news interpretation system can evaluate the credibility of news articles comprehensively, taking into account the specialty and qualifications of the article's author. For example, if the author has expertise in a particular field, news articles related to that field will be rated highly credible. It can also retrieve from a database what qualifications the author has previously obtained and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on the author's specialty.

[0053] The news interpretation system can evaluate the credibility of news articles by taking into account the credibility of the platform on which the article is published, making a comprehensive evaluation. For example, it can rate news articles from highly reliable platforms highly, and rate news articles from less reliable platforms low. It can also retrieve the past credibility ratings of publishing platforms from a database and use that information to evaluate credibility. Furthermore, it can add a function to preferentially display news articles from highly reliable platforms.

[0054] The news interpretation system can evaluate the credibility of news articles by taking into account expert opinions on the article's content and making a comprehensive evaluation. For example, experts in a specific field can evaluate the content of an article and determine its credibility based on that evaluation. It can also retrieve expert opinions from a database and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on expert opinions.

[0055] The news interpretation system can evaluate the credibility of news articles by taking into account reader feedback on the article's content and making a comprehensive assessment. For example, readers can post comments and ratings on articles, and the credibility can be judged based on that feedback. It can also obtain reader feedback from a database and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on reader feedback.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: An AI interpretation function is attached to a news article. It analyzes the content of the news article and provides information about its credibility and other opinions. The generative AI analyzes the content of the news article and provides information about its credibility and other opinions. For example, it may assess the credibility of the news article and summarize opinions from other reliable sources. The input to the generative AI is a prompt containing the content of the news article, and the generative AI generates an interpretation based on the prompt. Step 2: The user clicks on the AI ​​interpretation feature, which displays the generated interpretation, allowing the user to easily check the credibility of the news article and other information about its views. Step 3: The AI ​​interpretation function has a pop design, allowing users to see the interpretation through a visually appealing interface. Step 4: The repost function allows you to share your AI interpretation with other users. For example, you can share your AI interpretation with your followers by pressing the repost button. Step 5: The quote repost function allows you to add your own comments to the AI ​​interpretation and share it. For example, by pressing the quote repost button, you can add your own comments and share the AI ​​interpretation. Step 6: The Share function allows you to share your AI interpretation on other platforms. For example, you can share your AI interpretation on social media such as Facebook or Twitter by pressing the Share button.

[0058] (Example 2) A news interpretation system according to an embodiment of the present invention adds an AI interpretation function to a news article, and when a user clicks on the function, an AI interpretation summarizing the credibility of the news and other views is displayed. This allows the news interpretation system to provide users with information about the credibility of the news article and other views, thereby promoting information sharing.

[0059] A news interpretation system according to an embodiment includes an AI interpretation function, a generation AI, a repost function, a quote repost function, and a share function. The AI ​​interpretation function is added to a news article. For example, it analyzes the content of the news article and provides information about its credibility and other opinions. The generation AI analyzes the content of the news article and provides information about its credibility and other opinions. For example, it evaluates the credibility of the news article and summarizes opinions from other reliable sources. The input to the generation AI is a prompt containing the content of the news article, and the generation AI generates an interpretation based on the prompt. The repost function is a function for sharing the AI ​​interpretation with other users. For example, by pressing the repost button, a user can share the AI ​​interpretation with their followers. The quote repost function is a function for adding their own comments to the AI ​​interpretation and sharing it. For example, by pressing the quote repost button, a user can add their own comments and share the AI ​​interpretation. The share function is a function for sharing the AI ​​interpretation on other platforms. For example, by pressing the share button, a user can share the AI ​​interpretation on social media such as Facebook or Twitter. This allows the news interpretation system according to the embodiment to provide users with information about the credibility of news articles and other opinions, thereby promoting the sharing of information.

[0060] The AI ​​interpretation function can analyze the content of a news article as well as the article's author's past credibility assessments and biases to generate an overall credibility score. For example, when analyzing the content of a news article, the AI ​​interpretation function takes into account the author's past credibility assessments and biases. For example, it can retrieve from a database what kind of articles the author has written in the past and how credible those articles were, and generate an overall credibility score. This allows for more accurate assessment of the credibility of news articles.

[0061] The AI ​​interpretation function takes into account the publication date and update history of a news article and can provide an interpretation based on the latest information. For example, when the generation AI analyzes the content of a news article, it also takes into account the publication date and update history of the article and provides an interpretation based on the latest information. For example, if an article is updated, it analyzes the updated content and generates an interpretation based on the latest information. This makes it possible to provide an interpretation based on the latest information.

[0062] The AI ​​interpretation function uses the emotion estimation function to analyze readers' emotional reactions to the content of a news article and can evaluate its credibility based on those emotions. For example, the AI ​​interpretation function uses the emotion estimation function to analyze readers' emotional reactions to the content of a news article. For example, it can collect emotional data when readers read an article and evaluate its credibility based on that data. This allows it to evaluate its credibility based on readers' emotional reactions.

[0063] The AI ​​interpretation function analyzes not only news articles but also social media posts and comments, making it possible to provide interpretations from a wider range of sources. For example, the AI ​​interpretation function analyzes not only news articles but also social media posts and comments. For example, it collects Twitter and Facebook posts and generates interpretations based on that information. This makes it possible to provide interpretations from a wider range of sources.

[0064] The AI ​​interpretation function can also provide the interpretation analyzed by the generation AI in audio or video format, allowing information to be conveyed visually or aurally. For example, the AI ​​interpretation function can provide the interpretation analyzed by the generation AI in audio format. For example, a function to read out the interpretation of a news article aloud can be added, providing information to the visually impaired. This allows information to be conveyed visually or aurally.

[0065] The AI ​​interpretation function uses the emotion estimation function to analyze the user's emotions regarding the content of a news article in real time and can provide an interpretation based on those emotions. The AI ​​interpretation function, for example, uses the emotion estimation function to analyze the user's emotions regarding the content of a news article in real time. For example, it analyzes the user's facial expressions and voice and generates an interpretation based on that emotional data. This makes it possible to provide an interpretation based on the user's emotions in real time.

[0066] The AI ​​interpretation function can provide individually customized pop designs based on the user's past click history and interests. For example, the AI ​​interpretation function analyzes the user's past click history and provides individually customized pop designs based on that data. For example, it can reflect the user's preferred colors and design elements. This makes it possible to provide customized designs based on the user's interests.

[0067] The AI ​​interpretation function can add interactive elements to a design, so that different animations and effects are displayed each time the user clicks. For example, the AI ​​interpretation function can add interactive elements to a design, so that different animations are displayed each time the user clicks. For example, it can add a button that changes color or a moving icon each time it is clicked. This allows users to enjoy using the AI ​​interpretation function.

[0068] The AI ​​interpretation function uses the emotion estimation function to change the design in real time according to the user's emotional state, thereby eliciting positive emotions. For example, the AI ​​interpretation function uses the emotion estimation function to change the design in real time according to the user's emotional state. For example, if the user is feeling negative, bright colors and fun animations are displayed. This allows for design changes in real time that elicit positive emotions from the user.

[0069] The AI ​​interpretation function automatically changes the design according to the season or event, always providing a fresh experience. The AI ​​interpretation function automatically changes the design according to the season, for example, displaying a cherry blossom design in spring and an ocean design in summer. This allows the design to automatically change according to the season or event.

[0070] The AI ​​interpretation function can add a function that allows users to customize designs, allowing them to create designs that suit their preferences. The AI ​​interpretation function can, for example, add a function that allows users to customize designs. For example, it can provide an interface that allows users to select colors and fonts. This allows users to create designs that suit their preferences.

[0071] The AI ​​interpretation function uses the emotion estimation function to identify the design elements that are likely to interest the user and highlight those elements. The AI ​​interpretation function, for example, uses the emotion estimation function to identify the design elements that are likely to interest the user. For example, it analyzes the user's facial expressions and voice to identify the design elements that are likely to interest the user. This makes it possible to highlight the design elements that are likely to interest the user.

[0072] The AI ​​interpretation function can make a comprehensive assessment of the credibility of a news article, taking into account the reliability of the article's citation source and references. For example, the AI ​​interpretation function can evaluate the credibility of a news article based on the credibility score of the citation source. This allows for more accurate assessment of the credibility of news articles.

[0073] The AI ​​interpretation function allows the generative AI to incorporate opinions from different perspectives and positions in a balanced manner when providing other views. The AI ​​interpretation function, for example, allows the generative AI to incorporate opinions from different perspectives and positions in a balanced manner when providing other views. For example, it can collect different opinions from political positions or fields of expertise and generate an interpretation based on them. This makes it possible to provide views that incorporate opinions from different perspectives and positions in a balanced manner.

[0074] The AI ​​interpretation function uses the emotion estimation function to analyze a reader's emotional response to a news article and can provide an opinion based on that emotion. The AI ​​interpretation function, for example, uses the emotion estimation function to analyze a reader's emotional response to a news article. For example, it analyzes the reader's facial expressions and voice and generates an opinion based on that emotional data. This makes it possible to provide an opinion based on the reader's emotional response.

[0075] The AI ​​interpretation function adds a feature that allows users to vote on the credibility of news articles in real time, making it possible to utilize collective intelligence. For example, the AI ​​interpretation function adds a feature that allows users to vote on the credibility of news articles in real time. For example, it provides an interface where users can vote on credibility scores. This allows credibility to be evaluated based on user votes.

[0076] When providing other views, the AI ​​interpretation function can also display related past news articles and data, making it easier to understand the background of the information.For example, when providing other views, the AI ​​interpretation function can also display related past news articles and data.For example, it can display links to past news articles to provide background to the information.This makes it easier to understand the background of the information.

[0077] The AI ​​interpretation function can use the emotion estimation function to identify the views that the user is most interested in and display those views preferentially. The AI ​​interpretation function can, for example, use the emotion estimation function to identify the views that the user is most interested in. For example, it can analyze the user's facial expressions and voice to identify views that are likely to interest the user. This makes it possible to display the views that the user is most interested in preferentially.

[0078] The AI ​​interpretation function can provide an interface that allows users to easily add comments and tags when reposting or sharing. The AI ​​interpretation function can provide an interface that allows users to easily add comments when reposting or sharing. For example, it can display a comment input field so that users can freely add comments. This allows users to easily add comments and tags.

[0079] The AI ​​interpretation function tracks the spread of shared AI interpretations and can analyze which users have the most influence. The AI ​​interpretation function tracks the spread of shared AI interpretations, for example, by analyzing the number of times they have been shared and the number of reaches, and visualizing the spread. This makes it possible to track the spread of shared AI interpretations and identify influential users.

[0080] The AI ​​interpretation function uses the emotion estimation function to analyze the recipient's emotional reaction to the shared AI interpretation and can evaluate the effectiveness of sharing based on that reaction. The AI ​​interpretation function, for example, uses the emotion estimation function to analyze the recipient's emotional reaction to the shared AI interpretation. For example, it analyzes the recipient's facial expressions and voice and evaluates the effectiveness of sharing based on that emotional data. This makes it possible to evaluate the effectiveness of sharing based on the recipient's emotional reaction.

[0081] The AI ​​interpretation function strengthens collaboration between different social media platforms when reposting or sharing, facilitating the spread of information. The AI ​​interpretation function strengthens collaboration between different social media platforms when reposting or sharing, for example, by providing the ability to share to multiple platforms simultaneously, such as Twitter, Facebook, and Instagram. This strengthens collaboration between different social media platforms and promotes the spread of information.

[0082] The AI ​​interpretation function can collect feedback on shared AI interpretations and improve the accuracy of the interpretations based on that feedback. The AI ​​interpretation function, for example, collects feedback on shared AI interpretations. For example, it provides an interface where users can post comments and ratings. This makes it possible to improve the accuracy of the interpretations based on the feedback.

[0083] The AI ​​interpretation function uses the emotion estimation function to monitor the user's emotional response to the shared AI interpretation in real time, and can suggest the optimal timing for sharing. The AI ​​interpretation function, for example, uses the emotion estimation function to monitor the user's emotional response to the shared AI interpretation in real time. For example, it analyzes the user's facial expressions and voice and suggests the optimal timing for sharing based on that emotional data. This makes it possible to suggest the optimal timing for sharing.

[0084] The AI ​​interpretation function can dynamically optimize the layout of the user interface based on the user's operation history. For example, the AI ​​interpretation function analyzes the user's operation history and dynamically optimizes the layout of the user interface based on that data. For example, it places frequently used functions in prominent positions. This makes it possible to optimize the interface layout based on the user's operation history.

[0085] The AI ​​interpretation function analyzes the user's gaze tracking data for each element of the interface and places important information in the position that attracts the most attention.The AI ​​interpretation function, for example, analyzes the user's gaze tracking data and places each element of the interface based on that data.For example, it places important information in the position that attracts the user's most attention.This allows important information to be placed in the optimal position based on the user's gaze tracking data.

[0086] The AI ​​interpretation function uses the emotion estimation function to customize the interface according to the user's emotional state, thereby reducing stress. The AI ​​interpretation function, for example, uses the emotion estimation function to customize the interface according to the user's emotional state. For example, if the user is feeling stressed, the design will be changed to a simpler one. This allows the interface to be customized according to the user's emotional state, reducing stress.

[0087] The AI ​​interpretation function can optimize the user interface for different devices (smartphones, tablets, PCs) to provide a seamless experience.The AI ​​interpretation function can optimize the user interface for different devices, for example, by providing a layout that is suitable for each device (smartphone, tablet, PC).This allows the user to provide an interface that is optimized for different devices, providing a seamless experience.

[0088] The AI ​​interpretation function can add functionality that allows users to freely change the interface theme or color scheme. For example, the AI ​​interpretation function can provide an interface that allows users to freely change the interface theme or color scheme. This allows users to freely change the interface theme or color scheme.

[0089] The AI ​​interpretation function can use the emotion estimation function to identify the interface design that the user feels most comfortable with and recommend that design. The AI ​​interpretation function can, for example, use the emotion estimation function to identify the interface design that the user feels most comfortable with. For example, it can analyze the user's facial expressions and voice to identify a comfortable design. This makes it possible to identify the interface design that the user feels most comfortable with and recommend that design.

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

[0091] The news interpretation system can analyze a user's past news browsing history and provide an individually customized news feed. For example, it can prioritize relevant news articles based on the user's past interest in news categories or topics. It can also prioritize news from specific news sources if the user prefers them. Furthermore, it can automatically recommend related news articles based on the user's browsing history.

[0092] Using emotion estimation, the news interpretation system can analyze a user's emotional response to news articles and customize the news feed based on those emotions. For example, it can prioritize news articles that evoke positive emotions in the user. It can also tailor the feed to avoid news articles that evoke negative emotions in the user. Furthermore, it can dynamically change the order in which news articles are displayed based on the user's emotional state.

[0093] The news interpretation system can evaluate the credibility of news articles comprehensively, taking into account the specialty and qualifications of the article's author. For example, if the author has expertise in a particular field, news articles related to that field will be rated highly credible. It can also retrieve from a database what qualifications the author has previously obtained and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on the author's specialty.

[0094] The news interpretation system can use emotion estimation to analyze readers' emotional reactions to news articles and evaluate their credibility based on those emotions. For example, articles that elicit positive emotions from readers can be rated as highly credible, while articles that elicit negative emotions can be rated as less credible. It can also collect reader emotion data and use that data to improve its credibility evaluation algorithm. Furthermore, it can add a function to dynamically change the display order of news articles based on emotional reactions.

[0095] The news interpretation system can evaluate the credibility of news articles by taking into account the credibility of the platform on which the article is published, making a comprehensive evaluation. For example, it can rate news articles from highly reliable platforms highly, and rate news articles from less reliable platforms low. It can also retrieve the past credibility ratings of publishing platforms from a database and use that information to evaluate credibility. Furthermore, it can add a function to preferentially display news articles from highly reliable platforms.

[0096] Using emotion estimation, the news interpretation system can analyze a user's emotional response to news articles in real time and customize the news feed based on that emotion. For example, it can prioritize news articles that evoke positive emotions in the user. It can also tailor the feed to avoid news articles that evoke negative emotions in the user. Furthermore, it can dynamically change the order in which news articles are displayed based on the user's emotional state.

[0097] The news interpretation system can evaluate the credibility of news articles by taking into account expert opinions on the article's content and making a comprehensive evaluation. For example, experts in a specific field can evaluate the content of an article and determine its credibility based on that evaluation. It can also retrieve expert opinions from a database and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on expert opinions.

[0098] The news interpretation system can use emotion estimation to analyze readers' emotional reactions to news articles and evaluate their credibility based on those emotions. For example, articles that elicit positive emotions from readers can be rated as highly credible, while articles that elicit negative emotions can be rated as less credible. It can also collect reader emotion data and use that data to improve its credibility evaluation algorithm. Furthermore, it can add a function to dynamically change the display order of news articles based on emotional reactions.

[0099] The news interpretation system can evaluate the credibility of news articles by taking into account reader feedback on the article's content and making a comprehensive assessment. For example, readers can post comments and ratings on articles, and the credibility can be judged based on that feedback. It can also obtain reader feedback from a database and use that information to evaluate credibility. Furthermore, it can add a function to recommend related news articles based on reader feedback.

[0100] Using emotion estimation, the news interpretation system can analyze a user's emotional response to news articles in real time and customize the news feed based on that emotion. For example, it can prioritize news articles that evoke positive emotions in the user. It can also tailor the feed to avoid news articles that evoke negative emotions in the user. Furthermore, it can dynamically change the order in which news articles are displayed based on the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: An AI interpretation function is attached to a news article. It analyzes the content of the news article and provides information about its credibility and other opinions. The generative AI analyzes the content of the news article and provides information about its credibility and other opinions. For example, it may assess the credibility of the news article and summarize opinions from other reliable sources. The input to the generative AI is a prompt containing the content of the news article, and the generative AI generates an interpretation based on the prompt. Step 2: The user clicks on the AI ​​interpretation feature, which displays the generated interpretation, allowing the user to easily check the credibility of the news article and other information about its views. Step 3: The AI ​​interpretation function has a pop design, allowing users to see the interpretation through a visually appealing interface. Step 4: The repost function allows you to share your AI interpretation with other users. For example, you can share your AI interpretation with your followers by pressing the repost button. Step 5: The quote repost function allows you to add your own comments to the AI ​​interpretation and share it. For example, by pressing the quote repost button, you can add your own comments and share the AI ​​interpretation. Step 6: The Share function allows you to share your AI interpretation on other platforms. For example, you can share your AI interpretation on social media such as Facebook or Twitter by pressing the Share button.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. Adding AI interpretation capabilities to news articles, The AI ​​interpretation function: displaying a summary interpretation of the credibility or other opinions of the news article; The user clicks on the AI ​​interpretation function to display the interpretation; The AI ​​interpretation function: A pop design, Includes repost, quote repost, and share functions A system characterized by:

2. The AI ​​interpretation function: Analysis will include not only the news article but also social media posts or comments to provide interpretations from a wider range of sources.

2. The system of claim 1.

3. The AI ​​interpretation function: Providing the pop design that is individually customized based on the user's past click history or interests 2. The system of claim 1.

4. The AI ​​interpretation function: The credibility of the news article is evaluated comprehensively, taking into account the reliability of the article's source or references.

2. The system of claim 1.

5. The AI ​​interpretation function: When reposting or sharing, provide an interface that allows the user to easily add comments or tags 2. The system of claim 1.

6. The AI ​​interpretation function: The interface is customized according to the user's emotional state to reduce stress.

2. The system of claim 1.

7. The AI ​​interpretation function: Analyzing readers' emotional reactions to the content of the news article and evaluating its credibility based on those emotions 2. The system of claim 1.

8. The AI ​​interpretation function: Analyzing the recipient's emotional response to the shared AI interpretation and evaluating the effectiveness of the sharing based on the response.

2. The system of claim 1.

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