System

Abstract

An object of a system according to an embodiment is to efficiently summarize a news article and provide the news article in a video format.SOLUTION: A system according to an embodiment includes a news article analysis unit, a summary generation unit, and a moving image generation unit. The news article analysis unit analyzes a news article. The summary generation unit extracts an important point from the news article analyzed by the news article analysis unit and generates a summary. The moving image generation unit generates a moving image based on the summary created by the summary generation unit.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 technology has made it difficult to efficiently summarize and explain news articles, and there has been a particular problem of a lack of provision of such information in video format.

[0005] The system according to the embodiment aims to efficiently summarize news articles and provide them in video format. [Means for solving the problem]

[0006] The system according to the embodiment includes a news article analysis unit, a summary generation unit, and a video generation unit. The news article analysis unit analyzes news articles. The summary generation unit extracts important points from the news articles analyzed by the news article analysis unit to create summaries. The video generation unit generates videos based on the summaries created by the summary generation unit. [Effects of the Invention]

[0007] An embodiment of the system can efficiently summarize news articles and provide them in video format. [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) The news summary video generation system according to an embodiment of the present invention automatically analyzes news articles, summarizes them using a generation AI, and generates videos based on the summaries. This allows users to efficiently understand popular news.

[0029] A news summary video generation system according to an embodiment includes a news article analysis unit, a summary generation unit, and a video generation unit. The news article analysis unit analyzes news articles. For example, the news article analysis unit collects trending news articles from Yahoo! News and LINE NEWS and analyzes them. The news article analysis unit can also understand the content of the news articles and extract important points. For example, the news article analysis unit performs analysis based on the URL and article text of the news article. The summary generation unit extracts important points from the news articles analyzed by the news article analysis unit to create summaries. For example, the summary generation unit analyzes news articles using a generation AI, extracts important points, and creates summaries. The summary generation unit can also understand the content of news articles and generate summaries using the generation AI. For example, the generation AI extracts key points from the news articles and creates concise summaries. The video generation unit generates videos based on the summaries created by the summary generation unit. For example, the video generation unit creates videos based on the summaries generated by the generation AI. The video generation unit can also generate videos that include text, images, animations, and the like to visually convey the summary content in an easy-to-understand manner. For example, the video generation unit creates explanatory videos using maps and related images based on the summary content. This allows the news summary video generation system according to the embodiment to efficiently summarize news articles and explain them in video format. For example, users can quickly understand trending news. The news summary video generation system can also make videos available for viewing through dedicated apps or websites. Furthermore, videos can be shared through social media and messaging apps.

[0030] The news article analysis unit can evaluate the reliability of news articles and include only highly reliable information in summaries. For example, when analyzing a news article, the news article analysis unit checks the reliability of the article's source and author so that the generation AI can evaluate the article's reliability. For example, information from highly reliable news sources can be prioritized for inclusion in summaries. The news article analysis unit can also analyze the content of news articles and extract only highly reliable information. For example, the news article analysis unit evaluates the accuracy and reliability of news articles and includes highly reliable information in summaries. This makes it possible to provide accurate information by including only highly reliable information in summaries.

[0031] The news article analysis unit can include articles in different languages ​​and generate summaries from an international perspective. The news article analysis unit, for example, includes articles in different languages ​​in the analysis of news articles and generates summaries from an international perspective. For example, news articles in English, French, Chinese, etc. are analyzed. The news article analysis unit can also analyze articles in different languages ​​and generate summaries from an international perspective. For example, the news article analysis unit analyzes articles in different languages ​​and generates summaries from an international perspective. This makes it possible to provide summaries from an international perspective by including articles in different languages.

[0032] The news article analysis unit can also refer to related past news articles to generate a summary that includes background information. For example, when analyzing a news article, the news article analysis unit can refer to related past news articles to generate a summary that includes background information. For example, similar incidents or events in the past can be referenced. Furthermore, when analyzing a news article, the news article analysis unit can also refer to related past news articles to generate a summary that includes background information. For example, the news article analysis unit can refer to past news articles to generate a summary that includes background information. In this way, by generating a summary that includes background information, it is possible to provide the user with a deeper understanding.

[0033] The summary generation unit can generate a video including interactive elements based on the summary content. For example, the generation AI in the summary generation unit generates a video including an interactive quiz based on the summary content. For example, after watching a news summary, a user can check their understanding in the form of a quiz. The summary generation unit can also generate a video including interactive elements based on the summary content. For example, the summary generation unit generates a video including a questionnaire or voting function based on the summary content. This can increase user engagement by generating a video including interactive elements.

[0034] The summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary content is explained through narration. The summary generation unit can also add narration to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. This allows information to be conveyed both visually and audibly, deepening the user's understanding.

[0035] The summary generation unit can incorporate a news feed that is updated in real time into the summary video to constantly provide the latest information. The summary generation unit, for example, incorporates a news feed that is updated in real time into the summary video to constantly provide the latest information. For example, the latest news is displayed in a sidebar of the video. The summary generation unit can also incorporate a news feed that is updated in real time into the summary video to constantly provide the latest information. For example, the summary generation unit displays the latest news in the summary video. This constantly provides the latest information, allowing the user to receive the latest news.

[0036] The summary generation unit can optimize the summarized video for different platforms and deliver it to a wide range of viewers. For example, the summary generation unit optimizes the summarized video for YouTube and delivers it to a wide range of viewers. For example, the summary generation unit generates a video that matches the YouTube format. The summary generation unit can also optimize the summarized video for different platforms and deliver it to a wide range of viewers. For example, the summary generation unit optimizes the summarized video for Instagram and delivers it to a wide range of viewers. In this way, by optimizing it for different platforms, it is possible to deliver the video to a wide range of viewers.

[0037] The video generation unit adds a function to the video distribution platform in which a generation AI automatically recommends related videos, thereby maintaining user interest. The video generation unit, for example, adds a function to the video distribution platform in which a generation AI automatically recommends related videos. For example, related videos are recommended based on videos that a user has watched. The video generation unit can also add a function to the video distribution platform in which a related video is recommended, thereby maintaining user interest. For example, the video generation unit recommends related videos based on the user's viewing history. This allows the user's interest to be maintained by recommending related videos.

[0038] The video generation unit can provide an individually customized video list by having the generation AI analyze the user's viewing history when distributing a video. For example, the video generation unit can analyze the user's viewing history when distributing a video and provide an individually customized video list. For example, the video generation unit can create a list based on videos that the user has viewed in the past. The video generation unit can also analyze the user's viewing history when distributing a video and provide an individually customized video list. For example, the video generation unit can provide an individually customized video list based on the user's viewing history. This makes it possible to continue to attract the user's interest by providing an individually customized video list.

[0039] A video distribution platform may add a feature that allows users to leave comments and feedback on videos in real time. For example, the video distribution platform may add a feature that allows users to leave comments and feedback on videos in real time to the video distribution platform. For example, the video distribution platform may provide a feature that allows users to post comments while watching a video. The video distribution platform may also add a feature that allows users to leave comments and feedback on videos in real time. For example, the video distribution platform may provide a feature that allows users to leave comments and feedback on videos in real time. This allows users to leave comments and feedback in real time, thereby providing an interactive viewing experience.

[0040] Video distribution platforms can be made compatible with different devices to diversify the viewing environment. For example, by making the video distribution platform compatible with smartphones, the viewing environment can be diversified. For example, an app for smartphones can be developed. Video distribution platforms can also be made compatible with different devices to diversify the viewing environment. For example, a video distribution platform can be made compatible with tablets and smart TVs. By making the platform compatible with different devices, the viewing environment can be diversified and user convenience can be improved.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The news article analyzer not only evaluates the reliability of news articles, but also detects bias in the articles and generates balanced summaries. For example, the news article analyzer collects information from different perspectives and reflects it in the summary. The news article analyzer can also integrate information from multiple sources to avoid bias toward a particular political position or opinion. This allows users to understand the news from multiple perspectives.

[0043] The summary generator can generate infographics that attract the user's attention based on the summary content. For example, it can create graphs or charts to make the summary content visually easier to understand. The summary generator can also generate infographics based on the summary content to allow the user to intuitively understand the information. This makes it easier for the user to visually grasp the information.

[0044] The summary generator can suggest related news that may be of interest to the user based on the summary content. For example, it can display past news articles related to the summary content. The summary generator can also suggest topics that may be of interest to the user based on the summary content. This allows the user to grasp related information at once.

[0045] The summary generator can suggest related videos that may be of interest to the user based on the summary content. For example, it can display documentaries or interview videos related to the summary content. The summary generator can also suggest entertainment videos that may be of interest to the user based on the summary content. This allows the user to watch related videos at once.

[0046] The summary generator can suggest related books and papers that may be of interest to the user based on the summary content. For example, it can display books and academic papers related to the summary content. The summary generator can also suggest research reports that may be of interest to the user based on the summary content. This allows the user to grasp related literature all at once.

[0047] The summary generation unit can suggest related events and seminars that the user may be interested in based on the summary content. For example, it can display information about events and seminars related to the summary content. The summary generation unit can also suggest workshops and lectures that the user may be interested in based on the summary content. This allows the user to grasp related event information at a glance.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The news article analysis unit analyzes the news article. For example, the news article analysis unit collects trending news articles from Yahoo! News and LINE NEWS and analyzes them. The news article analysis unit can also understand the content of the news article and extract important points. Analysis is performed based on the URL of the news article and the article text. Step 2: The summary generation unit extracts important points from the news article analyzed by the news article analysis unit to create a summary. For example, the summary generation unit uses generation AI to analyze the news article, extract important points, and create a summary. The generation AI extracts the main points of the news article and creates a concise summary. Step 3: The video generation unit generates a video based on the summary created by the summary generation unit. For example, the video generation unit creates a video based on the summary generated by the generation AI. It can also generate videos that include text, images, animations, etc. to convey the summary content in a visually easy-to-understand manner. For example, it creates an explanatory video using maps and related images based on the summary content.

[0050] (Example 2) The news summary video generation system according to an embodiment of the present invention automatically analyzes news articles, summarizes them using a generation AI, and generates videos based on the summaries. This allows users to efficiently understand popular news.

[0051] A news summary video generation system according to an embodiment includes a news article analysis unit, a summary generation unit, and a video generation unit. The news article analysis unit analyzes news articles. For example, the news article analysis unit collects trending news articles from Yahoo! News and LINE NEWS and analyzes them. The news article analysis unit can also understand the content of the news articles and extract important points. For example, the news article analysis unit performs analysis based on the URL and article text of the news article. The summary generation unit extracts important points from the news articles analyzed by the news article analysis unit to create summaries. For example, the summary generation unit analyzes news articles using a generation AI, extracts important points, and creates summaries. The summary generation unit can also understand the content of news articles and generate summaries using the generation AI. For example, the generation AI extracts key points from the news articles and creates concise summaries. The video generation unit generates videos based on the summaries created by the summary generation unit. For example, the video generation unit creates videos based on the summaries generated by the generation AI. The video generation unit can also generate videos that include text, images, animations, and the like to visually convey the summary content in an easy-to-understand manner. For example, the video generation unit creates explanatory videos using maps and related images based on the summary content. This allows the news summary video generation system according to the embodiment to efficiently summarize news articles and explain them in video format. For example, users can quickly understand trending news. The news summary video generation system can also make videos available for viewing through dedicated apps or websites. Furthermore, videos can be shared through social media and messaging apps.

[0052] The news article analysis unit can evaluate the reliability of news articles and include only highly reliable information in summaries. For example, when analyzing a news article, the news article analysis unit checks the reliability of the article's source and author so that the generation AI can evaluate the article's reliability. For example, information from highly reliable news sources can be prioritized for inclusion in summaries. The news article analysis unit can also analyze the content of news articles and extract only highly reliable information. For example, the news article analysis unit evaluates the accuracy and reliability of news articles and includes highly reliable information in summaries. This makes it possible to provide accurate information by including only highly reliable information in summaries.

[0053] The news article analysis unit can analyze the emotional tone of a news article and adjust the summary based on whether the tone is positive, negative, or neutral. For example, the news article analysis unit can analyze the emotional tone of a news article and adjust the summary based on whether the tone is positive, negative, or neutral. For example, articles with a negative tone can be changed to neutral expressions. The news article analysis unit can also analyze the emotional tone of a news article and adjust the content of the summary. For example, the news article analysis unit can analyze the emotional tone of a news article and reflect articles with a positive tone directly in the summary. In this way, by adjusting the summary based on the emotional tone, appropriate information can be provided to the user.

[0054] The news article analysis unit can use the emotion estimation function to identify the news topic in which the user is most interested and prioritize analyzing articles related to that topic. The news article analysis unit, for example, uses the emotion estimation function to identify the news topic in which the user is most interested. For example, it analyzes the user's past browsing history and search history. The news article analysis unit can also identify a news topic based on the user's interest and prioritize analyzing articles related to that topic. For example, the news article analysis unit prioritizes analyzing articles related to a specific news topic based on the user's interest. In this way, useful information can be provided to the user by identifying and prioritize analyzing news topics based on the user's interest.

[0055] The news article analysis unit can include articles in different languages ​​and generate summaries from an international perspective. The news article analysis unit, for example, includes articles in different languages ​​in the analysis of news articles and generates summaries from an international perspective. For example, news articles in English, French, Chinese, etc. are analyzed. The news article analysis unit can also analyze articles in different languages ​​and generate summaries from an international perspective. For example, the news article analysis unit analyzes articles in different languages ​​and generates summaries from an international perspective. This makes it possible to provide summaries from an international perspective by including articles in different languages.

[0056] The news article analysis unit can also refer to related past news articles to generate a summary that includes background information. For example, when analyzing a news article, the news article analysis unit can refer to related past news articles to generate a summary that includes background information. For example, similar incidents or events in the past can be referenced. Furthermore, when analyzing a news article, the news article analysis unit can also refer to related past news articles to generate a summary that includes background information. For example, the news article analysis unit can refer to past news articles to generate a summary that includes background information. In this way, by generating a summary that includes background information, it is possible to provide the user with a deeper understanding.

[0057] The news article analysis unit can use the emotion estimation function to determine the priority of news articles based on the user's emotion and analyze the most interesting article. The news article analysis unit, for example, uses the emotion estimation function to determine the priority of news articles based on the user's emotion. For example, it preferentially analyzes articles in which the user expressed positive emotion. The news article analysis unit can also determine the priority of news articles based on the user's emotion and analyze the most interesting article. For example, the news article analysis unit preferentially analyzes specific news articles based on the user's emotion. In this way, by determining the priority of news articles based on the user's emotion, it is possible to provide information that is of the most interest to the user.

[0058] The summary generation unit can generate a video including interactive elements based on the summary content. For example, the generation AI in the summary generation unit generates a video including an interactive quiz based on the summary content. For example, after watching a news summary, a user can check their understanding in the form of a quiz. The summary generation unit can also generate a video including interactive elements based on the summary content. For example, the summary generation unit generates a video including a questionnaire or voting function based on the summary content. This can increase user engagement by generating a video including interactive elements.

[0059] The summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary content is explained through narration. The summary generation unit can also add narration to the summary video, allowing information to be conveyed both visually and audibly. For example, the summary generation unit adds narration automatically generated by the generation AI to the summary video, allowing information to be conveyed both visually and audibly. This allows information to be conveyed both visually and audibly, deepening the user's understanding.

[0060] The summary generation unit can use the emotion estimation function to select a video style according to the user's emotion. The summary generation unit, for example, uses the emotion estimation function to select a video style according to the user's emotion. For example, if the user is relaxed, the summary generation unit generates a video with a calm tone. The summary generation unit can also use the emotion estimation function to select a video style according to the user's emotion. For example, the summary generation unit generates a video with an energetic tone according to the user's emotion. This makes it possible to optimize the viewing experience by selecting a video style according to the user's emotion.

[0061] The summary generation unit can incorporate a news feed that is updated in real time into the summary video to constantly provide the latest information. The summary generation unit, for example, incorporates a news feed that is updated in real time into the summary video to constantly provide the latest information. For example, the latest news is displayed in a sidebar of the video. The summary generation unit can also incorporate a news feed that is updated in real time into the summary video to constantly provide the latest information. For example, the summary generation unit displays the latest news in the summary video. This constantly provides the latest information, allowing the user to receive the latest news.

[0062] The summary generation unit can optimize the summarized video for different platforms and deliver it to a wide range of viewers. For example, the summary generation unit optimizes the summarized video for YouTube and delivers it to a wide range of viewers. For example, the summary generation unit generates a video that matches the YouTube format. The summary generation unit can also optimize the summarized video for different platforms and deliver it to a wide range of viewers. For example, the summary generation unit optimizes the summarized video for Instagram and delivers it to a wide range of viewers. In this way, by optimizing it for different platforms, it is possible to deliver the video to a wide range of viewers.

[0063] The video generation unit adds a function to the video distribution platform in which a generation AI automatically recommends related videos, thereby maintaining user interest. The video generation unit, for example, adds a function to the video distribution platform in which a generation AI automatically recommends related videos. For example, related videos are recommended based on videos that a user has watched. The video generation unit can also add a function to the video distribution platform in which a related video is recommended, thereby maintaining user interest. For example, the video generation unit recommends related videos based on the user's viewing history. This allows the user's interest to be maintained by recommending related videos.

[0064] The video generation unit can provide an individually customized video list by having the generation AI analyze the user's viewing history when distributing a video. For example, the video generation unit can analyze the user's viewing history when distributing a video and provide an individually customized video list. For example, the video generation unit can create a list based on videos that the user has viewed in the past. The video generation unit can also analyze the user's viewing history when distributing a video and provide an individually customized video list. For example, the video generation unit can provide an individually customized video list based on the user's viewing history. This makes it possible to continue to attract the user's interest by providing an individually customized video list.

[0065] A video distribution platform may add a feature that allows users to leave comments and feedback on videos in real time. For example, the video distribution platform may add a feature that allows users to leave comments and feedback on videos in real time to the video distribution platform. For example, the video distribution platform may provide a feature that allows users to post comments while watching a video. The video distribution platform may also add a feature that allows users to leave comments and feedback on videos in real time. For example, the video distribution platform may provide a feature that allows users to leave comments and feedback on videos in real time. This allows users to leave comments and feedback in real time, thereby providing an interactive viewing experience.

[0066] Video distribution platforms can be made compatible with different devices to diversify the viewing environment. For example, by making the video distribution platform compatible with smartphones, the viewing environment can be diversified. For example, an app for smartphones can be developed. Video distribution platforms can also be made compatible with different devices to diversify the viewing environment. For example, a video distribution platform can be made compatible with tablets and smart TVs. By making the platform compatible with different devices, the viewing environment can be diversified and user convenience can be improved.

[0067] A video distribution platform can use an emotion estimation function to automatically generate thumbnails and titles for videos based on the user's emotions, thereby improving click-through rates. For example, the video distribution platform uses the emotion estimation function to automatically generate thumbnails for videos based on the user's emotions. For example, an image that is likely to interest the user is used as the thumbnail. The video distribution platform can also use the emotion estimation function to automatically generate titles for videos based on the user's emotions. For example, the video distribution platform generates an interesting title based on the user's emotions. In this way, the click-through rate can be improved by automatically generating thumbnails and titles for videos.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The news article analyzer not only evaluates the reliability of news articles, but also detects bias in the articles and generates balanced summaries. For example, the news article analyzer collects information from different perspectives and reflects it in the summary. The news article analyzer can also integrate information from multiple sources to avoid bias toward a particular political position or opinion. This allows users to understand the news from multiple perspectives.

[0070] The summary generator can generate infographics that attract the user's attention based on the summary content. For example, it can create graphs or charts to make the summary content visually easier to understand. The summary generator can also generate infographics based on the summary content to allow the user to intuitively understand the information. This makes it easier for the user to visually grasp the information.

[0071] The news article analysis unit can use the emotion estimation function to adjust the display order of news articles based on the user's emotions. For example, if the user is feeling stressed, it can prioritize displaying positive news that will help them relax. The news article analysis unit can also display negative news less based on the user's emotions. This makes it possible to provide news that takes the user's emotions into consideration.

[0072] The summary generator can suggest related news that may be of interest to the user based on the summary content. For example, it can display past news articles related to the summary content. The summary generator can also suggest topics that may be of interest to the user based on the summary content. This allows the user to grasp related information at once.

[0073] The news article analysis unit can use the emotion estimation function to customize a news article summary based on the user's emotions. For example, if the user expresses positive emotions, the unit generates a summary that emphasizes the positive elements. The news article analysis unit can also generate a summary that tones down the negative elements based on the user's emotions. This makes it possible to provide a summary that matches the user's emotions.

[0074] The summary generator can suggest related videos that may be of interest to the user based on the summary content. For example, it can display documentaries or interview videos related to the summary content. The summary generator can also suggest entertainment videos that may be of interest to the user based on the summary content. This allows the user to watch related videos at once.

[0075] The news article analysis unit can use the emotion estimation function to adjust the summary of a news article based on the user's emotions. For example, if the user is sad, the unit generates a summary that emphasizes positive elements. The news article analysis unit can also generate a summary that tones down negative elements based on the user's emotions. This makes it possible to provide a summary that takes the user's emotions into consideration.

[0076] The summary generator can suggest related books and papers that may be of interest to the user based on the summary content. For example, it can display books and academic papers related to the summary content. The summary generator can also suggest research reports that may be of interest to the user based on the summary content. This allows the user to grasp related literature all at once.

[0077] The news article analysis unit can use the emotion estimation function to personalize the news article summary based on the user's emotion. For example, if the user is excited, an energetic summary is generated. The news article analysis unit can also generate a relaxed summary based on the user's emotion. This makes it possible to provide a personalized summary according to the user's emotion.

[0078] The summary generation unit can suggest related events and seminars that the user may be interested in based on the summary content. For example, it can display information about events and seminars related to the summary content. The summary generation unit can also suggest workshops and lectures that the user may be interested in based on the summary content. This allows the user to grasp related event information at a glance.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The news article analysis unit analyzes the news article. For example, the news article analysis unit collects trending news articles from Yahoo! News and LINE NEWS and analyzes them. The news article analysis unit can also understand the content of the news article and extract important points. Analysis is performed based on the URL of the news article and the article text. Step 2: The summary generation unit extracts important points from the news article analyzed by the news article analysis unit to create a summary. For example, the summary generation unit uses generation AI to analyze the news article, extract important points, and create a summary. The generation AI extracts the main points of the news article and creates a concise summary. Step 3: The video generation unit generates a video based on the summary created by the summary generation unit. For example, the video generation unit creates a video based on the summary generated by the generation AI. It can also generate videos that include text, images, animations, etc. to convey the summary content in a visually easy-to-understand manner. For example, it creates an explanatory video using maps and related images based on the summary content.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[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 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.

[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 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).

[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] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 news article analysis unit that analyzes news articles; a summary generation unit that extracts important points from the news articles analyzed by the news article analysis unit and creates summaries; a video generation unit that generates a video based on the summary created by the summary generation unit; A system characterized by:

2. The news article analysis unit Generate summaries from an international perspective, including articles in different languages 2. The system of claim 1.

3. The summary generation unit Generate interactive videos based on the summary 2. The system of claim 1.

4. The video generation unit Adding a feature to the video streaming platform that automatically recommends related videos using generative AI to keep users engaged 2. The system of claim 1.

5. The news article analysis unit Identify the news topics that users are most interested in and prioritize articles related to those topics.

2. The system of claim 1.

Images