System

Abstract

An object of a system according to an embodiment is to quickly provide a news article in a visually and aurally appealing manner.SOLUTION: A system according to an embodiment includes a news article collection unit, an analysis unit, a sound generation unit, a subtitle generation unit, an image generation unit, a short video creation unit, and a distribution unit. The news article collection unit collects news articles. The analysis unit analyzes the news article collected by the news article collection unit. The sound generation unit generates sound on the basis of the content of the news article analyzed by the analysis unit. The caption generation unit generates a caption on the basis of the content of the news article analyzed by the analysis unit. The image generation unit generates an image related to the content of the news article analyzed by the analysis unit. The short video creation section creates a short video by combining the sound, the subtitle, and the image generated by the sound generation section, the subtitle generation section, and the image generation section. The distribution unit distributes the short video created by the short video creation 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 technologies do not adequately provide news articles quickly in a visually and aurally appealing format, and there is room for improvement.

[0005] The system according to the embodiment aims to quickly provide news articles in a visually and aurally appealing format. [Means for solving the problem]

[0006] The system according to the embodiment includes a news article collection unit, an analysis unit, an audio generation unit, a subtitle generation unit, an image generation unit, a short video creation unit, and a distribution unit. The news article collection unit collects news articles. The analysis unit analyzes the news articles collected by the news article collection unit. The audio generation unit generates audio based on the content of the news articles analyzed by the analysis unit. The subtitle generation unit generates subtitles based on the content of the news articles analyzed by the analysis unit. The image generation unit generates images related to the content of the news articles analyzed by the analysis unit. The short video creation unit creates short videos by combining the audio, subtitles, and images generated by the audio generation unit, subtitle generation unit, and image generation unit. The distribution unit distributes the short videos created by the short video creation unit. [Effects of the Invention]

[0007] An embodiment of the system can quickly provide news articles in a visually and aurally appealing 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 platform according to an embodiment of the present invention is a system that converts the latest news articles into short videos in real time and provides information to users. This system uses a generative AI to generate audio, subtitles, and images based on web news articles, instantly creating and distributing visually and aurally appealing short videos. This allows the news platform to make it easier for users to understand the news more intuitively.

[0029] A news platform according to an embodiment includes a news article collection unit, an analysis unit, an audio generation unit, a subtitle generation unit, an image generation unit, a short video creation unit, and a distribution unit. The news article collection unit collects news articles. For example, it collects the latest news articles from online news sites. The analysis unit analyzes the news articles collected by the news article collection unit. For example, a generation AI uses natural language processing technology to extract key points and important information from the news articles. The audio generation unit generates audio based on the content of the news articles analyzed by the analysis unit. For example, the generation AI generates a narration summarizing the key points of the news articles. The subtitle generation unit generates subtitles based on the content of the news articles analyzed by the analysis unit. For example, it generates subtitles that match the content read aloud. The image generation unit generates images related to the content of the news articles analyzed by the analysis unit. For example, it generates images related to people, places, and events that appear in the news articles. The short video creation unit creates short videos by combining the audio, subtitles, and images generated by the audio generation unit, subtitle generation unit, and image generation unit. For example, the generative AI combines audio, subtitles, and images to create short videos that are visually and aurally appealing. The distribution unit distributes the short videos created by the short video creation unit, for example by sending a notification to the user's smartphone or tablet so that the video can be viewed. This enables news platforms to convert news articles into short videos in real time and provide information to users.

[0030] The news article aggregator can cross-reference multiple news sources, evaluate the reliability of the news sources, and generate a reliability score. For example, when collecting news articles, the news article aggregator collects articles on the same topic from multiple news sources and evaluates the reliability of each. For example, it collects information from major news sites and specialized news sources and calculates a reliability score. This allows the reliability of news articles to be evaluated and reliable information to be provided.

[0031] The news article collection unit can collect not only text but also multimedia content such as podcasts and video news. For example, the news article collection unit expands the scope of news article collection from text to multimedia content such as podcasts and video news. For example, it collects podcasts and video news from major news sites. This allows it to collect not only text but also multimedia content.

[0032] The news article collection unit can generate a personalized news feed based on the user's past browsing history and interests. The news article collection unit, for example, analyzes the user's past browsing history and interests and generates a personalized news feed based on the results. For example, news articles related to topics frequently viewed by the user are preferentially displayed. This makes it possible to provide a personalized news feed based on the user's interests.

[0033] The voice generation unit can generate multiple narrations with different voice qualities and speaking styles based on the content of the news article, allowing the user to select from them. The voice generation unit, for example, uses a generation AI to generate multiple narrations with different voice qualities and speaking styles based on the content of the news article. For example, it can generate male voices, female voices, young voices, elderly voices, etc. This allows the user to select their preferred narration.

[0034] The audio generation unit can automatically add background sounds and sound effects related to the content of a news article to enhance the sense of realism. For example, when generating audio, the audio generation unit automatically adds background sounds and sound effects related to the content of a news article. For example, the sound of wind and rain can be added to news about natural disasters. This can enhance the sense of realism of the news.

[0035] The voice generation unit can simultaneously generate narration in different languages ​​based on the content of the news article, making it possible to accommodate international users. The voice generation unit can simultaneously generate narration in different languages ​​based on the content of the news article, for example, using generation AI. For example, it can generate narration in multiple languages, such as English, French, and Chinese. This makes it possible to accommodate international users.

[0036] The subtitle generation unit simultaneously generates subtitles in multiple languages ​​based on the content of a news article, making it possible to accommodate international users. The subtitle generation unit simultaneously generates subtitles in multiple languages ​​based on the content of a news article, for example, using generation AI. For example, subtitles are generated in multiple languages, such as English, French, and Chinese. This makes it possible to accommodate international users.

[0037] The subtitle generation unit can highlight important keywords and phrases from news articles when generating subtitles, so that users do not miss important information. The subtitle generation unit can, for example, use generation AI to automatically extract important keywords and phrases from news articles and highlight them when generating subtitles. For example, it can highlight them by using bold or changing the color. This prevents users from missing important information.

[0038] The subtitle generation unit can automatically add infographics and icons related to the content of the news article when generating subtitles, thereby aiding visual understanding. The subtitle generation unit can automatically generate infographics and icons related to the content of the news article using, for example, generation AI, and add them to the subtitles. For example, it can generate graphs and icons that visually display statistical data, thereby aiding visual understanding.

[0039] The image generation unit can generate images from multiple viewpoints based on the content of a news article, allowing the user to select from them. The image generation unit generates images from multiple viewpoints based on the content of a news article, for example, using a generation AI. For example, it generates images from different angles or viewpoints for the same news article. This allows the user to select an image from a viewpoint they prefer.

[0040] The image generation unit can automatically add animations and GIFs related to the content of the news article when generating an image, thereby enhancing the visual impact. The image generation unit can, for example, use generation AI to automatically generate animations and GIFs related to the content of the news article and add them to the image. For example, it can generate animations that visually display important data. This can enhance the visual impact.

[0041] The image generation unit can generate localized visuals to accommodate different cultures and regions when generating images. The image generation unit uses, for example, generative AI to generate localized visuals that accommodate different cultures and regions based on the content of news articles. For example, it generates images that take into account cultural backgrounds and regional characteristics. This makes it possible to provide visuals that accommodate different cultures and regions.

[0042] The short video creation unit can simultaneously generate videos in different formats (vertical, horizontal, square) based on the content of the short video, allowing users to select. The short video creation unit, for example, uses generation AI to simultaneously generate videos in different formats (vertical, horizontal, square) based on the content of the short video. For example, it generates formats optimized for smartphones, tablets, and PCs. This allows users to watch videos in their preferred format.

[0043] The distribution unit can simultaneously distribute short videos on different platforms (SNS, email, apps) when distributing the short videos, thereby improving user convenience. The distribution unit, for example, builds a system that simultaneously distributes short videos on different platforms (SNS, email, apps) when distributing the short videos. For example, the distribution unit distributes videos to major SNS, email services, and apps. This can improve user convenience.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The news platform may further include a feedback collection unit that collects user feedback. The feedback collection unit may, for example, provide an interface that allows users to input ratings and comments on the short videos they have viewed. This allows users' opinions and impressions to be collected and used to improve the system. For example, the system may collect feedback on how users felt about a particular news topic and reflect this in future content generation. It may also be possible to adjust the selection of news articles and the presentation of videos based on user feedback.

[0046] The news platform may further include a learning management unit that manages the user's learning history. The learning management unit, for example, records news content that the user has previously viewed and topics that the user has studied, and manages the user's learning history. This allows news related to topics that interest the user to be provided preferentially. For example, if a user frequently views news related to a particular field, the latest news related to that field can be delivered preferentially. The learning management unit can also visualize the user's learning progress and suggest the next topic to study.

[0047] The news platform may further include a location information utilization unit that utilizes the user's location information. The location information utilization unit, for example, prioritizes the collection and distribution of local news based on the user's current location. This allows the provision of news related to the area where the user lives or where the user is currently staying. For example, if the user is staying in a particular city, the latest news related to that city can be distributed preferentially. The location information utilization unit may also provide news related to the user's destination in advance when the user moves.

[0048] The news platform may further include a subscription management unit that manages a user's subscription history. The subscription management unit manages, for example, newsletters and paid content to which the user subscribes. This allows the latest information from news sources to which the user subscribes to to be provided preferentially. For example, if a user subscribes to a specific newsletter, the latest articles from that newsletter can be delivered preferentially. The subscription management unit can also suggest related paid content based on the user's subscription history.

[0049] The news platform may further include an eye-tracking unit that tracks the user's gaze. For example, the eye-tracking unit may grasp in real time which part of the screen the user is looking at. This allows the news platform to identify news content that the user is particularly interested in and provide personalized news based on that information. For example, if a user looks at part of a particular news article for a long time, news related to that topic may be delivered preferentially. The eye-tracking unit may also optimize the layout of news content based on the user's gaze data.

[0050] The news platform may further include a device optimization unit that performs optimization according to the user's device environment. For example, the device optimization unit optimizes the display of news content according to the screen size and resolution of the device used by the user. This allows the user to comfortably watch news regardless of the device they are using. For example, a portrait layout can be automatically applied to smartphones and a landscape layout to tablets. The device optimization unit can also adjust the video quality according to the user's internet connection status.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The news article collection unit collects news articles, for example, the latest news articles from news sites on the Internet. Step 2: The analysis unit analyzes the news articles collected by the news article collection unit. For example, the generation AI uses natural language processing technology to extract the main points and important information from the news articles. Step 3: The voice generation unit generates voice based on the content of the news article analyzed by the analysis unit. For example, the generation AI generates a narration summarizing the main points of the news article. Step 4: The subtitle generator generates subtitles based on the content of the news article analyzed by the analyzer, for example, generating subtitles that match the content read aloud. Step 5: The image generation unit generates images related to the content of the news article analyzed by the analysis unit, for example, images of people, places, and events that appear in the news article. Step 6: The short video creation unit creates a short video by combining the audio, subtitles, and images generated by the audio generation unit, subtitle generation unit, and image generation unit. For example, the generation AI combines audio, subtitles, and images to create a short video that is visually and auditorily appealing. Step 7: The distribution unit distributes the short video created by the short video creation unit, for example, by sending a notification to the user's smartphone or tablet so that the video can be viewed.

[0053] (Example 2) A news platform according to an embodiment of the present invention is a system that converts the latest news articles into short videos in real time and provides information to users. This system uses a generative AI to generate audio, subtitles, and images based on web news articles, instantly creating and distributing visually and aurally appealing short videos. This allows the news platform to make it easier for users to understand the news more intuitively.

[0054] A news platform according to an embodiment includes a news article collection unit, an analysis unit, an audio generation unit, a subtitle generation unit, an image generation unit, a short video creation unit, and a distribution unit. The news article collection unit collects news articles. For example, it collects the latest news articles from online news sites. The analysis unit analyzes the news articles collected by the news article collection unit. For example, a generation AI uses natural language processing technology to extract key points and important information from the news articles. The audio generation unit generates audio based on the content of the news articles analyzed by the analysis unit. For example, the generation AI generates a narration summarizing the key points of the news articles. The subtitle generation unit generates subtitles based on the content of the news articles analyzed by the analysis unit. For example, it generates subtitles that match the content read aloud. The image generation unit generates images related to the content of the news articles analyzed by the analysis unit. For example, it generates images related to people, places, and events that appear in the news articles. The short video creation unit creates short videos by combining the audio, subtitles, and images generated by the audio generation unit, subtitle generation unit, and image generation unit. For example, the generative AI combines audio, subtitles, and images to create short videos that are visually and aurally appealing. The distribution unit distributes the short videos created by the short video creation unit, for example by sending a notification to the user's smartphone or tablet so that the video can be viewed. This enables news platforms to convert news articles into short videos in real time and provide information to users.

[0055] The news article aggregator can cross-reference multiple news sources, evaluate the reliability of the news sources, and generate a reliability score. For example, when collecting news articles, the news article aggregator collects articles on the same topic from multiple news sources and evaluates the reliability of each. For example, it collects information from major news sites and specialized news sources and calculates a reliability score. This allows the reliability of news articles to be evaluated and reliable information to be provided.

[0056] The analysis unit can analyze the emotional tone of news articles and classify them based on positive, negative, or neutral emotions. For example, the analysis unit uses generative AI to analyze the emotional tone of collected news articles and classify them into positive, negative, or neutral emotions. For example, it analyzes the context and keywords of the article and calculates an emotional score. This allows news articles to be classified based on their emotional tone and provides appropriate information to users.

[0057] The analysis unit can use the emotion estimation function to predict the emotional impact that the content of a news article will have on the user and preferentially collect articles that match the user's emotions. For example, the analysis unit can use the emotion estimation function to predict the emotional impact that the content of a news article will have on the user. For example, it can evaluate whether the content of the article will evoke positive emotions in the user. This makes it possible to provide news articles that match the user's emotions.

[0058] The news article collection unit can collect not only text but also multimedia content such as podcasts and video news. For example, the news article collection unit expands the scope of news article collection from text to multimedia content such as podcasts and video news. For example, it collects podcasts and video news from major news sites. This allows it to collect not only text but also multimedia content.

[0059] The news article collection unit can generate a personalized news feed based on the user's past browsing history and interests. The news article collection unit, for example, analyzes the user's past browsing history and interests and generates a personalized news feed based on the results. For example, news articles related to topics frequently viewed by the user are preferentially displayed. This makes it possible to provide a personalized news feed based on the user's interests.

[0060] The analysis unit can use the emotion estimation function to estimate in real time which news article the user is most interested in and collect those news articles preferentially. The analysis unit, for example, uses the emotion estimation function to estimate in real time which news article the user is most interested in. For example, articles with a high degree of interest are preferentially collected based on the user's emotion score. This makes it possible to provide the news article that the user is most interested in in real time.

[0061] The voice generation unit can generate multiple narrations with different voice qualities and speaking styles based on the content of the news article, allowing the user to select from them. The voice generation unit, for example, uses a generation AI to generate multiple narrations with different voice qualities and speaking styles based on the content of the news article. For example, it can generate male voices, female voices, young voices, elderly voices, etc. This allows the user to select their preferred narration.

[0062] The voice generation unit generates an emotional narration according to the content of the news article, and can provide a voice that appeals to the user's emotions. The voice generation unit generates an emotional narration according to the content of the news article, for example, using a generation AI. For example, a sad voice is used for sad news, and a cheerful voice is used for happy news. This makes it possible to provide a voice that appeals to the user's emotions.

[0063] The voice generation unit uses the emotion estimation function to generate narration according to the user's emotional state, and can provide voice that matches the user's emotions. The voice generation unit uses, for example, the emotion estimation function to generate narration according to the user's emotional state. For example, if the user is relaxed, a calm voice is used, and if the user is nervous, a subdued voice is used. This makes it possible to provide voice that matches the user's emotions.

[0064] The audio generation unit can automatically add background sounds and sound effects related to the content of a news article to enhance the sense of realism. For example, when generating audio, the audio generation unit automatically adds background sounds and sound effects related to the content of a news article. For example, the sound of wind and rain can be added to news about natural disasters. This can enhance the sense of realism of the news.

[0065] The voice generation unit can simultaneously generate narration in different languages ​​based on the content of the news article, making it possible to accommodate international users. The voice generation unit can simultaneously generate narration in different languages ​​based on the content of the news article, for example, using generation AI. For example, it can generate narration in multiple languages, such as English, French, and Chinese. This makes it possible to accommodate international users.

[0066] The voice generation unit can use the emotion estimation function to learn the narration style that the user most likes and provide personalized voice. For example, the voice generation unit uses the emotion estimation function to learn the narration style that the user most likes. For example, the voice generation unit adjusts the narration style based on the user's past emotional response data. This makes it possible to provide the narration style that the user most likes.

[0067] The subtitle generation unit simultaneously generates subtitles in multiple languages ​​based on the content of a news article, making it possible to accommodate international users. The subtitle generation unit simultaneously generates subtitles in multiple languages ​​based on the content of a news article, for example, using generation AI. For example, subtitles are generated in multiple languages, such as English, French, and Chinese. This makes it possible to accommodate international users.

[0068] The subtitle generation unit can highlight important keywords and phrases from news articles when generating subtitles, so that users do not miss important information. The subtitle generation unit can, for example, use generation AI to automatically extract important keywords and phrases from news articles and highlight them when generating subtitles. For example, it can highlight them by using bold or changing the color. This prevents users from missing important information.

[0069] The subtitle generation unit can use the emotion estimation function to automatically adjust the style and font of subtitles according to the emotional state of the user. For example, the subtitle generation unit uses the emotion estimation function to automatically adjust the style and font of subtitles according to the emotional state of the user. For example, if the user is relaxed, a soft font is used. This makes it possible to provide subtitles according to the emotional state of the user.

[0070] The subtitle generation unit can automatically add infographics and icons related to the content of the news article when generating subtitles, thereby aiding visual understanding. The subtitle generation unit can automatically generate infographics and icons related to the content of the news article using, for example, generation AI, and add them to the subtitles. For example, it can generate graphs and icons that visually display statistical data, thereby aiding visual understanding.

[0071] The subtitle generation unit can use the emotion estimation function to learn the subtitle style that is easiest for the user to understand and provide personalized subtitles. For example, the subtitle generation unit uses the emotion estimation function to learn the subtitle style that is easiest for the user to understand. For example, the subtitle style is adjusted based on the user's past emotional response data. This makes it possible to provide subtitles that are easiest for the user to understand.

[0072] The image generation unit can generate images from multiple viewpoints based on the content of a news article, allowing the user to select from them. The image generation unit generates images from multiple viewpoints based on the content of a news article, for example, using a generation AI. For example, it generates images from different angles or viewpoints for the same news article. This allows the user to select an image from a viewpoint they prefer.

[0073] The image generation unit uses the emotion estimation function to generate an image according to the user's emotional state, and can provide visuals that match the user's emotions. The image generation unit uses, for example, the emotion estimation function to generate an image according to the user's emotional state. For example, if the user is relaxed, a calm image is used, and if the user is tense, a subdued image is used. This makes it possible to provide visuals that match the user's emotions.

[0074] The image generation unit can automatically add animations and GIFs related to the content of the news article when generating an image, thereby enhancing the visual impact. The image generation unit can, for example, use generation AI to automatically generate animations and GIFs related to the content of the news article and add them to the image. For example, it can generate animations that visually display important data. This can enhance the visual impact.

[0075] The image generation unit can generate localized visuals to accommodate different cultures and regions when generating images. The image generation unit uses, for example, generative AI to generate localized visuals that accommodate different cultures and regions based on the content of news articles. For example, it generates images that take into account cultural backgrounds and regional characteristics. This makes it possible to provide visuals that accommodate different cultures and regions.

[0076] The image generation unit can use the emotion estimation function to learn the visual style that the user most likes and provide personalized images. For example, the image generation unit uses the emotion estimation function to learn the visual style that the user most likes. For example, the visual style is adjusted based on the user's past emotional response data. This makes it possible to provide the visual style that the user most likes.

[0077] The short video creation unit can simultaneously generate videos in different formats (vertical, horizontal, square) based on the content of the short video, allowing users to select. The short video creation unit, for example, uses generation AI to simultaneously generate videos in different formats (vertical, horizontal, square) based on the content of the short video. For example, it generates formats optimized for smartphones, tablets, and PCs. This allows users to watch videos in their preferred format.

[0078] The short video creation unit can add emotional effects and productions according to the content of the short video, providing a video that appeals to the user's emotions. The short video creation unit can add emotional effects and productions according to the content of the short video, for example, using a generation AI. For example, sad music and effects can be used for sad news. This allows the creation of a video that appeals to the user's emotions.

[0079] The short video creation unit uses the emotion estimation function to generate a video according to the user's emotional state, and can provide content that matches the user's emotions. The short video creation unit uses, for example, the emotion estimation function to generate a video that matches the user's emotional state. For example, if the user is relaxed, a calm video is used, and if the user is nervous, a calm video is used. This makes it possible to provide content that matches the user's emotions.

[0080] The distribution unit can simultaneously distribute short videos on different platforms (SNS, email, apps) when distributing the short videos, thereby improving user convenience. The distribution unit, for example, builds a system that simultaneously distributes short videos on different platforms (SNS, email, apps) when distributing the short videos. For example, the distribution unit distributes videos to major SNS, email services, and apps. This can improve user convenience.

[0081] The distribution unit can use the emotion estimation function to estimate in real time the video content in which the user is most interested and distribute that video content preferentially. The distribution unit, for example, uses the emotion estimation function to estimate in real time the video content in which the user is most interested. For example, videos with a high degree of interest are distributed preferentially based on the user's emotion score. This makes it possible to provide the video content in which the user is most interested.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The news platform may further include a feedback collection unit that collects user feedback. The feedback collection unit may, for example, provide an interface that allows users to input ratings and comments on the short videos they have viewed. This allows users' opinions and impressions to be collected and used to improve the system. For example, the system may collect feedback on how users felt about a particular news topic and reflect this in future content generation. It may also be possible to adjust the selection of news articles and the presentation of videos based on user feedback.

[0084] The news platform may further include a health monitoring unit that monitors the user's health status. The health monitoring unit may, for example, link with a device that measures the user's heart rate or stress level to grasp the user's health status in real time. This allows news content to be provided according to the user's health status. For example, if the user is in a high-stress state, relaxing news or positive news may be delivered preferentially. The health monitoring unit may also issue an alert to prevent the user from watching for long periods of time based on the user's health data.

[0085] The news platform may further include a learning management unit that manages the user's learning history. The learning management unit, for example, records news content that the user has previously viewed and topics that the user has studied, and manages the user's learning history. This allows news related to topics that interest the user to be provided preferentially. For example, if a user frequently views news related to a particular field, the latest news related to that field can be delivered preferentially. The learning management unit can also visualize the user's learning progress and suggest the next topic to study.

[0086] The news platform may further include a social networking unit that links with the user's social network. For example, the social networking unit may link with the user's social networking account and collect news content shared by the user's friends and followers. This allows the platform to provide news that is likely to interest the user. For example, news articles shared by the user's friends may be displayed preferentially to attract the user's interest. The social networking unit may also provide a function that allows the user to share news content that they have viewed on social networking sites.

[0087] The news platform may further include a location information utilization unit that utilizes the user's location information. The location information utilization unit, for example, prioritizes the collection and distribution of local news based on the user's current location. This allows the provision of news related to the area where the user lives or where the user is currently staying. For example, if the user is staying in a particular city, the latest news related to that city can be distributed preferentially. The location information utilization unit may also provide news related to the user's destination in advance when the user moves.

[0088] The news platform may further include a subscription management unit that manages a user's subscription history. The subscription management unit manages, for example, newsletters and paid content to which the user subscribes. This allows the latest information from news sources to which the user subscribes to to be provided preferentially. For example, if a user subscribes to a specific newsletter, the latest articles from that newsletter can be delivered preferentially. The subscription management unit can also suggest related paid content based on the user's subscription history.

[0089] The news platform may further include a voice recognition unit that accepts voice commands from users. The voice recognition unit provides an interface that allows users to, for example, search for and play news by voice. This allows users to operate news content without using their hands. For example, if a user issues a voice command such as "Play the latest sports news," the voice recognition unit can analyze the command and play the corresponding news content. The voice recognition unit can also learn the user's voice commands to perform more accurate recognition.

[0090] The news platform may further include an eye-tracking unit that tracks the user's gaze. For example, the eye-tracking unit may grasp in real time which part of the screen the user is looking at. This allows the news platform to identify news content that the user is particularly interested in and provide personalized news based on that information. For example, if a user looks at part of a particular news article for a long time, news related to that topic may be delivered preferentially. The eye-tracking unit may also optimize the layout of news content based on the user's gaze data.

[0091] The news platform may further include an emotion adjustment unit that estimates a user's emotion and adjusts news content based on the estimated emotion. The emotion adjustment unit may estimate the user's emotion from, for example, the user's facial expression or voice, and provide news content according to that emotion. For example, if the user is sad, positive news may be preferentially delivered. The emotion adjustment unit may also adjust the tone and presentation of the news content based on the user's emotion data. This makes it possible to provide a news experience that matches the user's emotion.

[0092] The news platform may further include a device optimization unit that performs optimization according to the user's device environment. For example, the device optimization unit optimizes the display of news content according to the screen size and resolution of the device used by the user. This allows the user to comfortably watch news regardless of the device they are using. For example, a portrait layout can be automatically applied to smartphones and a landscape layout to tablets. The device optimization unit can also adjust the video quality according to the user's internet connection status.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The news article collection unit collects news articles, for example, the latest news articles from news sites on the Internet. Step 2: The analysis unit analyzes the news articles collected by the news article collection unit. For example, the generation AI uses natural language processing technology to extract the main points and important information from the news articles. Step 3: The voice generation unit generates voice based on the content of the news article analyzed by the analysis unit. For example, the generation AI generates a narration summarizing the main points of the news article. Step 4: The subtitle generator generates subtitles based on the content of the news article analyzed by the analyzer, for example, generating subtitles that match the content read aloud. Step 5: The image generation unit generates images related to the content of the news article analyzed by the analysis unit, for example, images of people, places, and events that appear in the news article. Step 6: The short video creation unit creates a short video by combining the audio, subtitles, and images generated by the audio generation unit, subtitle generation unit, and image generation unit. For example, the generation AI combines audio, subtitles, and images to create a short video that is visually and auditorily appealing. Step 7: The distribution unit distributes the short video created by the short video creation unit, for example, by sending a notification to the user's smartphone or tablet so that the video can be viewed.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

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

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

[0139] In the 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.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0143] The data processing system 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 collection unit that collects news articles; an analysis unit that analyzes the news articles collected by the news article collection unit; a voice generating unit that generates voice based on the content of the news article analyzed by the analyzing unit; a subtitle generation unit that generates subtitles based on the content of the news article analyzed by the analysis unit; an image generation unit that generates an image related to the content of the news article analyzed by the analysis unit; a short video creation unit that creates a short video by combining the audio, subtitles, and images generated by the audio generation unit, the subtitle generation unit, and the image generation unit; a distribution unit that distributes the short video created by the short video creation unit. A system characterized by:

2. The news article gathering unit Cross-referencing multiple news sources and evaluating the reliability of said news sources to generate a reliability score The system of claim 1 .

3. The news article gathering unit Generate a personalized news feed based on a user's past browsing history and interests The system of claim 1 .

4. The voice generation unit A plurality of narrations with different voice qualities and speaking styles are generated based on the content of the news article, and the user can select one. The system of claim 1 .

5. The subtitle generation unit The subtitles are simultaneously generated in multiple languages ​​based on the content of the news article, to accommodate international users. The system of claim 1 .

6. The analysis unit Using an emotion estimation function, the emotional impact of the content of the news article on the user is predicted, and articles that correspond to the user's emotions are preferentially collected. The system of claim 1 .

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