system

Abstract

The system according to this embodiment aims to efficiently collect and visually present news that matches the user's interests. [Solution] The system according to the embodiment comprises a curation unit, a summarization unit, a generation unit, and a provision unit. The curation unit searches for and selects news articles. The summarization unit summarizes the content of the news articles selected by the curation unit. The generation unit generates a video based on the information summarized by the summarization unit. The provision unit provides the video generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently collect news that matches their interests and visually understand it.

[0005] The system according to the embodiment aims to efficiently collect news that matches the user's interests and visually understand it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a curation unit, a summarization unit, a generation unit, and a provision unit. The curation unit searches for and selects news articles. The summarization unit summarizes the content of the news articles selected by the curation unit. The generation unit generates a video based on the information summarized by the summarization unit. The provision unit provides the video generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and visually present news that matches the user's interests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The news video generation system according to an embodiment of the present invention is a system that curates news articles based on the user's interests and makes them viewable in a 30-second short video format. This news video generation system allows users to view news with a UI / UX similar to a short video platform, and to jump to the original article if they wish to see a more detailed explanation. This system utilizes RAG (a mechanism that generates responses using AI based on the content of web pages and documents obtained through searches) and video generation AI. Specifically, it curates news articles from around the world based on the user's interests, summarizes the content of the selected news articles, and generates videos. In Phase 1, the system generates the text to be read, news headlines, and images using a large-scale language model, and provides them in a format read aloud by a virtual announcer. In Phase 2, it utilizes more advanced video generation technology to generate videos with a wider range of expression. For example, if the user is interested in "technology," the system searches for the latest technology-related news, summarizes it, and generates a 30-second short video. The video includes the news headlines, summarized content, and related images, and is read aloud by a virtual announcer. Users can easily grasp the latest news by watching videos, and access the original articles if more detailed information is needed. This system allows users to efficiently collect news that matches their interests and obtain important information in a short amount of time. Furthermore, because it is provided in video format, it is easy to understand visually and information can be easily digested. In Phase 2, by utilizing more advanced video generation technology, even more diverse expressions will be possible, improving the user experience. As a result, the news video generation system will be able to efficiently collect news articles based on the user's interests and provide important information in a short amount of time.

[0029] The news video generation system according to this embodiment comprises a curation unit, a summarization unit, a generation unit, and a provision unit. The curation unit searches for and selects news articles. For example, the curation unit can search for and select news articles based on the user's interests. The summarization unit summarizes the content of the news articles selected by the curation unit. For example, the summarization unit can summarize the content of the selected news articles and provide the information necessary for video generation. The generation unit generates a video based on the information summarized by the summarization unit. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model and provide them in a format read aloud by a virtual announcer. The generation unit can also utilize video generation AI to generate videos with a wide range of expressions. The provision unit provides the video generated by the generation unit to the user. For example, the provision unit can provide navigation based on the generated video and provide links to access the original articles. As a result, the news video generation system according to this embodiment can efficiently collect news articles based on the user's interests and provide important information in a short amount of time.

[0030] The generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model, and provide them in a format read aloud by a virtual announcer. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model. For instance, the generation unit can automatically generate news summaries, headlines, and images using large-scale language models such as GPT-4® or Gemini. Furthermore, the generation unit can provide the generated text and headlines in a format read aloud by a virtual announcer. For example, the generation unit can use speech synthesis technology to enable a virtual announcer to read the news in a natural voice. Thus, by using a large-scale language model, the generation unit can automatically generate news summaries, headlines, and images, and provide them in a format read aloud by a virtual announcer.

[0031] The generation unit can generate videos with diverse expressions by utilizing video generation AI. For example, the generation unit can generate videos with diverse expressions such as animation, live-action, and infographics by using video generation AI such as GAN (Generative Opposite Network) and RNN (Recurrent Neural Network). This enables the generation unit to generate videos with a wide range of expressions by utilizing video generation AI.

[0032] The service provider can provide navigation based on the generated video and offer links to access the original article. For example, the service provider can design the user interface and arrange the links to make it easy for users to access the original article. In this way, by providing navigation based on the generated video and offering links to access the original article, the service provider can easily access detailed information.

[0033] The curation department can search for and select news articles based on users' interests. For example, the curation department can identify users' interests using survey results or past browsing history, and then search for and select news articles based on those interests. In this way, the curation department can provide users with news that is highly relevant to them by searching for and selecting news articles based on their interests.

[0034] The summarization unit can summarize the content of selected news articles and provide the information necessary for video generation. For example, the summarization unit can summarize the content of selected news articles and provide the information necessary for video generation. For example, the summarization unit can perform summarization based on the length of the news article and the importance of the information to be summarized. As a result, the summarization unit can efficiently generate videos by summarizing the content of selected news articles and providing the information necessary for video generation.

[0035] The curation department can analyze a user's past browsing history and select appropriate news articles. For example, the curation department can collect and analyze a user's past browsing history using cookies and log data. Furthermore, the curation department can prioritize selecting news articles from categories that the user has frequently viewed in the past. In this way, the curation department can select the most relevant news articles for a user by analyzing their past browsing history.

[0036] The curation department can filter news articles by considering users' current interests and trends. For example, the curation department can identify users' current interests and trends using social media trends and search engine data. Furthermore, the curation department can prioritize news articles related to keywords recently searched by the user. This allows the curation department to provide highly relevant news articles by considering users' current interests and trends.

[0037] The curation department can prioritize selecting news articles that are highly relevant, taking into account the user's geographical location. For example, the curation department can collect the user's geographical location information using GPS data or IP addresses. Furthermore, the curation department can prioritize selecting local news relevant to the user's current location. This allows the curation department to provide highly relevant local news by considering the user's geographical location.

[0038] The curation department can analyze users' social media activity when selecting news articles and choose relevant articles. For example, the curation department can collect and analyze users' social media activity using information such as post content, the number of likes, and the number of followers. The curation department can also select news articles related to articles that users have shared on social media. In this way, the curation department can provide highly relevant news articles by analyzing users' social media activity.

[0039] The summarization function can adjust the level of detail in a summary based on the importance of the news article during summary generation. For example, the summarization function can evaluate the importance of a news article using factors such as the number of views, shares, and expert ratings. Furthermore, the summarization function can provide detailed summaries for highly important news articles and concise summaries for less important news articles. In this way, the summarization function can provide detailed summaries of important news by adjusting the level of detail in the summary based on the importance of the news article.

[0040] The summarization function can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization function can classify news articles into categories such as politics, economics, sports, and entertainment, and apply a summarization algorithm appropriate for each. Furthermore, the summarization function can apply a technical summarization algorithm to technology-related articles and an entertainment-focused summarization algorithm to entertainment-related articles. In this way, the summarization function can provide summaries appropriate to each category by applying different summarization algorithms depending on the category of the news article.

[0041] The summarization unit can determine the priority of summaries based on the publication date of the news articles when generating summaries. For example, the summarization unit can evaluate the publication date of news articles using the publication date or update date. The summarization unit can also prioritize summarizing the latest news articles and postpone summarizing older news articles. In this way, the summarization unit can prioritize summarizing the latest news by determining the priority of summaries based on the publication date of the news articles.

[0042] The summarization unit can adjust the order of summaries based on the relevance of news articles during summary generation. For example, the summarization unit can evaluate the relevance of news articles using common keywords or matching topics. Furthermore, the summarization unit can prioritize summarizing news articles relevant to the user's interests. This allows the summarization unit to prioritize summarizing highly relevant news by adjusting the order of summaries based on their relevance.

[0043] The generation unit can adjust the level of detail in a video based on the importance of the news article during video generation. For example, the generation unit can evaluate the importance of a news article using factors such as the number of views, shares, and expert ratings. Furthermore, the generation unit can generate detailed videos for highly important news articles and concise videos for less important news articles. In this way, the generation unit can create detailed videos for important news by adjusting the level of detail in the video based on the importance of the news article.

[0044] The generation unit can apply different video generation algorithms depending on the category of the news article when generating videos. For example, the generation unit can classify news articles into categories such as politics, economics, sports, and entertainment, and apply a video generation algorithm suitable for each. Furthermore, the generation unit can apply a technical video generation algorithm to technology-related articles and an entertainment-focused video generation algorithm to entertainment-related articles. In this way, the generation unit can provide videos appropriate to each category by applying different video generation algorithms depending on the category of the news article.

[0045] The generation unit can determine the priority of videos based on the publication date of the news article when generating them. For example, the generation unit can evaluate the publication date of a news article using the publication date or update date. The generation unit can also prioritize creating videos from the latest news articles and postpone creating videos from older news articles. In this way, the generation unit can prioritize creating videos from the latest news by determining the priority of videos based on the publication date of the news article.

[0046] The generation unit can adjust the order of videos based on the relevance of news articles during video generation. For example, the generation unit can evaluate the relevance of news articles using common keywords or matching topics. Furthermore, the generation unit can prioritize creating videos from news articles related to the user's interests. This allows the generation unit to prioritize creating videos from highly relevant news by adjusting the order of videos based on the relevance of news articles.

[0047] The service provider can select the optimal display method when providing videos by referring to the user's past viewing history. For example, the service provider can collect and analyze the user's past viewing history using data such as the number of views and viewing time. Furthermore, the service provider can select the optimal display method based on the display methods used for videos the user has watched in the past. In this way, the service provider can provide the optimal display method for the user by referring to the user's past viewing history.

[0048] The service provider can select the optimal display method when providing videos, taking into account the user's device information. For example, the service provider can collect user device information using the device type, screen size, OS, etc. Furthermore, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. In this way, the service provider can provide a display method optimized for the device by taking into account the user's device information.

[0049] The service provider can provide optimal navigation when delivering videos, taking into account the user's geographical location information. For example, the service provider can collect the user's geographical location information using GPS data or IP addresses. Furthermore, the service provider can provide navigation to local news relevant to the user's current location. This allows the service provider to provide highly relevant navigation by considering the user's geographical location information.

[0050] The service provider can analyze users' social media activity and provide relevant navigation when providing videos. For example, the service provider can collect and analyze users' social media activity using information such as post content, number of likes, and number of followers. Furthermore, the service provider can provide navigation to news related to articles shared by users on social media. This allows the service provider to provide highly relevant navigation by analyzing users' social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The news video generation system can also include a feedback unit to collect user feedback. This feedback unit provides an interface that allows users to input ratings and comments on the videos they watch. For example, users can rate a video as "helpful" or "interesting" after watching it. Furthermore, the feedback unit can analyze user ratings and comments to improve the overall system. This allows the news video generation system to improve content quality based on user feedback and provide a more satisfying service.

[0053] The generation unit can be equipped with a function to recommend videos to watch next based on the user's viewing history. For example, the generation unit can analyze the content and viewing time of videos the user has watched in the past and automatically recommend highly relevant news videos. In addition, the generation unit can prioritize generating videos related to specific categories or topics based on the user's viewing history. This allows the generation unit to provide more personalized video recommendations by utilizing the user's viewing history.

[0054] The generation unit can add interactive elements to news videos. For example, it can insert quizzes or surveys into videos, allowing users to answer them while watching. It can also generate interactive videos where the story branches based on the user's choices at specific scenes. This allows the generation unit to encourage user participation and deliver more engaging video content.

[0055] The service provider can include a function to automatically adjust the video resolution and format according to the user's device. For example, if the user is using a smartphone, the service provider can provide a lower resolution video to save data usage. Conversely, if the user is using a device with a high-resolution display, the service provider can provide a Full HD or 4K resolution video. This allows the service provider to provide a video viewing experience optimized for the user's device.

[0056] The curation section can be equipped with a function to evaluate the reliability of news articles. For example, the curation section can evaluate the reliability of the sources and authors of news articles and prioritize the selection of highly reliable articles. In addition, the curation section can implement algorithms to detect fake news and misinformation and eliminate unreliable articles. In this way, the curation section can provide users with reliable news.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The curation department searches for and selects news articles. For example, the curation department can search for and select news articles based on the user's interests. Step 2: The summarization section summarizes the content of the news articles selected by the curation section. For example, the summarization section can summarize the content of the selected news articles and provide the information necessary for video generation. Step 3: The generation unit generates a video based on the information summarized by the summarization unit. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model, and provide them in a format read aloud by a virtual announcer. The generation unit can also utilize video generation AI to generate videos with a wide range of expressions. Step 4: The provider unit provides the user with the video generated by the generator unit. For example, the provider unit can provide navigation based on the generated video and a link to access the original article.

[0059] (Example of form 2) The news video generation system according to an embodiment of the present invention is a system that curates news articles based on the user's interests and makes them viewable in a 30-second short video format. This news video generation system allows users to view news with a UI / UX similar to a short video platform, and to jump to the original article if they wish to see a more detailed explanation. This system utilizes RAG (a mechanism that generates responses using AI based on the content of web pages and documents obtained through searches) and video generation AI. Specifically, it curates news articles from around the world based on the user's interests, summarizes the content of the selected news articles, and generates videos. In Phase 1, the system generates the text to be read, news headlines, and images using a large-scale language model, and provides them in a format read aloud by a virtual announcer. In Phase 2, it utilizes more advanced video generation technology to generate videos with a wider range of expression. For example, if the user is interested in "technology," the system searches for the latest technology-related news, summarizes it, and generates a 30-second short video. The video includes the news headlines, summarized content, and related images, and is read aloud by a virtual announcer. Users can easily grasp the latest news by watching videos, and access the original articles if more detailed information is needed. This system allows users to efficiently collect news that matches their interests and obtain important information in a short amount of time. Furthermore, because it is provided in video format, it is easy to understand visually and information can be easily digested. In Phase 2, by utilizing more advanced video generation technology, even more diverse expressions will be possible, improving the user experience. As a result, the news video generation system will be able to efficiently collect news articles based on the user's interests and provide important information in a short amount of time.

[0060] The news video generation system according to this embodiment comprises a curation unit, a summarization unit, a generation unit, and a provision unit. The curation unit searches for and selects news articles. For example, the curation unit can search for and select news articles based on the user's interests. The summarization unit summarizes the content of the news articles selected by the curation unit. For example, the summarization unit can summarize the content of the selected news articles and provide the information necessary for video generation. The generation unit generates a video based on the information summarized by the summarization unit. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model and provide them in a format read aloud by a virtual announcer. The generation unit can also utilize video generation AI to generate videos with a wide range of expressions. The provision unit provides the video generated by the generation unit to the user. For example, the provision unit can provide navigation based on the generated video and provide links to access the original articles. As a result, the news video generation system according to this embodiment can efficiently collect news articles based on the user's interests and provide important information in a short amount of time.

[0061] The generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model, and provide them in a format read aloud by a virtual announcer. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model. For instance, the generation unit can automatically generate news summaries, headlines, and images using large-scale language models such as GPT-4 or Gemini. Furthermore, the generation unit can provide the generated text and headlines in a format read aloud by a virtual announcer. For example, the generation unit can use speech synthesis technology to have a virtual announcer read the news in a natural voice. Thus, by using a large-scale language model, the generation unit can automatically generate news summaries, headlines, and images, and provide them in a format read aloud by a virtual announcer.

[0062] The generation unit can generate videos with diverse expressions by utilizing video generation AI. For example, the generation unit can generate videos with diverse expressions such as animation, live-action, and infographics by using video generation AI such as GAN (Generative Opposite Network) and RNN (Recurrent Neural Network). This enables the generation unit to generate videos with a wide range of expressions by utilizing video generation AI.

[0063] The service provider can provide navigation based on the generated video and offer links to access the original article. For example, the service provider can design the user interface and arrange the links to make it easy for users to access the original article. In this way, by providing navigation based on the generated video and offering links to access the original article, the service provider can easily access detailed information.

[0064] The curation department can search for and select news articles based on users' interests. For example, the curation department can identify users' interests using survey results or past browsing history, and then search for and select news articles based on those interests. In this way, the curation department can provide users with news that is highly relevant to them by searching for and selecting news articles based on their interests.

[0065] The summarization unit can summarize the content of selected news articles and provide the information necessary for video generation. For example, the summarization unit can summarize the content of selected news articles and provide the information necessary for video generation. For example, the summarization unit can perform summarization based on the length of the news article and the importance of the information to be summarized. As a result, the summarization unit can efficiently generate videos by summarizing the content of selected news articles and providing the information necessary for video generation.

[0066] The curation department can estimate the user's emotions and adjust the criteria for selecting news articles based on those estimated emotions. For example, the curation department can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Furthermore, the curation department can adjust the criteria for selecting news articles based on the estimated emotions. For example, if a user is feeling stressed, the curation department can prioritize selecting news articles with relaxing content. In this way, the curation department can provide news that matches the user's emotions by adjusting the criteria for selecting news articles based on those emotions.

[0067] The curation department can analyze a user's past browsing history and select appropriate news articles. For example, the curation department can collect and analyze a user's past browsing history using cookies and log data. Furthermore, the curation department can prioritize selecting news articles from categories that the user has frequently viewed in the past. In this way, the curation department can select the most relevant news articles for a user by analyzing their past browsing history.

[0068] The curation department can filter news articles by considering users' current interests and trends. For example, the curation department can identify users' current interests and trends using social media trends and search engine data. Furthermore, the curation department can prioritize news articles related to keywords recently searched by the user. This allows the curation department to provide highly relevant news articles by considering users' current interests and trends.

[0069] The curation unit can estimate the user's emotions and prioritize news articles based on those emotions. For example, the curation unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Furthermore, the curation unit can prioritize news articles based on the estimated user emotions. For example, if the user is relaxed, it can prioritize displaying news articles that promote relaxation. In this way, the curation unit can prioritize news that matches the user's emotions by prioritizing news articles based on those emotions.

[0070] The curation department can prioritize selecting news articles that are highly relevant, taking into account the user's geographical location. For example, the curation department can collect the user's geographical location information using GPS data or IP addresses. Furthermore, the curation department can prioritize selecting local news relevant to the user's current location. This allows the curation department to provide highly relevant local news by considering the user's geographical location.

[0071] The curation department can analyze users' social media activity when selecting news articles and choose relevant articles. For example, the curation department can collect and analyze users' social media activity using information such as post content, the number of likes, and the number of followers. The curation department can also select news articles related to articles that users have shared on social media. In this way, the curation department can provide highly relevant news articles by analyzing users' social media activity.

[0072] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, the summarization unit can estimate the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. Furthermore, the summarization unit can adjust the way the summary is presented based on the estimated user emotions. For example, if the user is relaxed, the summary can be presented in a softer tone. In this way, the summarization unit can provide a summary that is easy for the user to understand by adjusting the way the summary is presented based on the user's emotions.

[0073] The summarization function can adjust the level of detail in a summary based on the importance of the news article during summary generation. For example, the summarization function can evaluate the importance of a news article using factors such as the number of views, shares, and expert ratings. Furthermore, the summarization function can provide detailed summaries for highly important news articles and concise summaries for less important news articles. In this way, the summarization function can provide detailed summaries of important news by adjusting the level of detail in the summary based on the importance of the news article.

[0074] The summarization function can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization function can classify news articles into categories such as politics, economics, sports, and entertainment, and apply a summarization algorithm appropriate for each. Furthermore, the summarization function can apply a technical summarization algorithm to technology-related articles and an entertainment-focused summarization algorithm to entertainment-related articles. In this way, the summarization function can provide summaries appropriate to each category by applying different summarization algorithms depending on the category of the news article.

[0075] The summarization unit can estimate the user's emotions and adjust the length of the summary based on those emotions. For example, the summarization unit can estimate the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The summarization unit can also adjust the length of the summary based on the estimated emotions. For example, it can provide a short summary if the user is in a hurry and a detailed summary if the user is relaxed. In this way, the summarization unit can provide a summary that is appropriate to the user's situation by adjusting the length of the summary based on the user's emotions.

[0076] The summarization unit can determine the priority of summaries based on the publication date of the news articles when generating summaries. For example, the summarization unit can evaluate the publication date of news articles using the publication date or update date. The summarization unit can also prioritize summarizing the latest news articles and postpone summarizing older news articles. In this way, the summarization unit can prioritize summarizing the latest news by determining the priority of summaries based on the publication date of the news articles.

[0077] The summarization unit can adjust the order of summaries based on the relevance of news articles during summary generation. For example, the summarization unit can evaluate the relevance of news articles using common keywords or matching topics. Furthermore, the summarization unit can prioritize summarizing news articles relevant to the user's interests. This allows the summarization unit to prioritize summarizing highly relevant news by adjusting the order of summaries based on their relevance.

[0078] The generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, the generation unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Furthermore, the generation unit can adjust the video's presentation based on the estimated user emotions. For example, if the user is relaxed, it can generate a video with a soft tone; if the user is in a hurry, it can generate a concise and to-the-point video. In this way, the generation unit can provide the user with a visually appropriate video by adjusting the video's presentation based on the user's emotions.

[0079] The generation unit can adjust the level of detail in a video based on the importance of the news article during video generation. For example, the generation unit can evaluate the importance of a news article using factors such as the number of views, shares, and expert ratings. Furthermore, the generation unit can generate detailed videos for highly important news articles and concise videos for less important news articles. In this way, the generation unit can create detailed videos for important news by adjusting the level of detail in the video based on the importance of the news article.

[0080] The generation unit can apply different video generation algorithms depending on the category of the news article when generating videos. For example, the generation unit can classify news articles into categories such as politics, economics, sports, and entertainment, and apply a video generation algorithm suitable for each. Furthermore, the generation unit can apply a technical video generation algorithm to technology-related articles and an entertainment-focused video generation algorithm to entertainment-related articles. In this way, the generation unit can provide videos appropriate to each category by applying different video generation algorithms depending on the category of the news article.

[0081] The generation unit can estimate the user's emotions and adjust the video length based on those emotions. For example, the generation unit can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The generation unit can also adjust the video length based on the estimated user emotions. For example, if the user is in a hurry, it can generate a short video, and if the user is relaxed, it can generate a detailed video. In this way, the generation unit can provide videos that are appropriate to the user's situation by adjusting the video length based on the user's emotions.

[0082] The generation unit can determine the priority of videos based on the publication date of the news article when generating them. For example, the generation unit can evaluate the publication date of a news article using the publication date or update date. The generation unit can also prioritize creating videos from the latest news articles and postpone creating videos from older news articles. In this way, the generation unit can prioritize creating videos from the latest news by determining the priority of videos based on the publication date of the news article.

[0083] The generation unit can adjust the order of videos based on the relevance of news articles during video generation. For example, the generation unit can evaluate the relevance of news articles using common keywords or matching topics. Furthermore, the generation unit can prioritize creating videos from news articles related to the user's interests. This allows the generation unit to prioritize creating videos from highly relevant news by adjusting the order of videos based on the relevance of news articles.

[0084] The service provider can estimate the user's emotions and adjust the video display method based on the estimated emotions. For example, the service provider can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Furthermore, the service provider can adjust the video display method based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method, and if the user is relaxed, it can provide a display method that includes detailed information. In this way, by adjusting the video display method based on the user's emotions, the service provider can provide a highly visible display method for the user.

[0085] The service provider can select the optimal display method when providing videos by referring to the user's past viewing history. For example, the service provider can collect and analyze the user's past viewing history using data such as the number of views and viewing time. Furthermore, the service provider can select the optimal display method based on the display methods used for videos the user has watched in the past. In this way, the service provider can provide the optimal display method for the user by referring to the user's past viewing history.

[0086] The service provider can select the optimal display method when providing videos, taking into account the user's device information. For example, the service provider can collect user device information using the device type, screen size, OS, etc. Furthermore, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. In this way, the service provider can provide a display method optimized for the device by taking into account the user's device information.

[0087] The service provider can estimate the user's emotions and adjust the video navigation based on those emotions. For example, the service provider can estimate the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Furthermore, the service provider can adjust the video navigation based on the estimated user emotions. For example, if the user is tense, it can provide simple and easy-to-understand navigation, and if the user is relaxed, it can provide navigation that includes detailed information. In this way, by adjusting the video navigation based on the user's emotions, the service provider can provide navigation that is easy for the user to understand.

[0088] The service provider can provide optimal navigation when delivering videos, taking into account the user's geographical location information. For example, the service provider can collect the user's geographical location information using GPS data or IP addresses. Furthermore, the service provider can provide navigation to local news relevant to the user's current location. This allows the service provider to provide highly relevant navigation by considering the user's geographical location information.

[0089] The service provider can analyze users' social media activity and provide relevant navigation when providing videos. For example, the service provider can collect and analyze users' social media activity using information such as post content, number of likes, and number of followers. Furthermore, the service provider can provide navigation to news related to articles shared by users on social media. This allows the service provider to provide highly relevant navigation by analyzing users' social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the curation unit, summarization unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the curation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a video using video generation AI. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides the generated video to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the curation unit, summarization unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the curation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a video using video generation AI. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides the generated video to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the curation unit, summarization unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the curation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a video using video generation AI. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides the generated video to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the curation unit, summarization unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the curation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a video using video generation AI. The provision unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides the generated video to the user.

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

[0091] The news video generation system can also include a feedback unit to collect user feedback. This feedback unit provides an interface that allows users to input ratings and comments on the videos they watch. For example, users can rate a video as "helpful" or "interesting" after watching it. Furthermore, the feedback unit can analyze user ratings and comments to improve the overall system. This allows the news video generation system to improve content quality based on user feedback and provide a more satisfying service.

[0092] The generation unit can be equipped with a function to recommend videos to watch next based on the user's viewing history. For example, the generation unit can analyze the content and viewing time of videos the user has watched in the past and automatically recommend highly relevant news videos. In addition, the generation unit can prioritize generating videos related to specific categories or topics based on the user's viewing history. This allows the generation unit to provide more personalized video recommendations by utilizing the user's viewing history.

[0093] The generation unit can add interactive elements to news videos. For example, it can insert quizzes or surveys into videos, allowing users to answer them while watching. It can also generate interactive videos where the story branches based on the user's choices at specific scenes. This allows the generation unit to encourage user participation and deliver more engaging video content.

[0094] The service provider can include a function to automatically adjust the video resolution and format according to the user's device. For example, if the user is using a smartphone, the service provider can provide a lower resolution video to save data usage. Conversely, if the user is using a device with a high-resolution display, the service provider can provide a Full HD or 4K resolution video. This allows the service provider to provide a video viewing experience optimized for the user's device.

[0095] The curation section can be equipped with a function to evaluate the reliability of news articles. For example, the curation section can evaluate the reliability of the sources and authors of news articles and prioritize the selection of highly reliable articles. In addition, the curation section can implement algorithms to detect fake news and misinformation and eliminate unreliable articles. In this way, the curation section can provide users with reliable news.

[0096] The summarization function can estimate the user's emotions and adjust the tone of the summary based on those emotions. For example, if the user is sad, the summarization function can use a gentle tone; if the user is excited, it can use an energetic tone. Furthermore, the summarization function can prioritize summarizing positive news based on the user's emotions. This allows the summarization function to provide summaries that are sensitive to the user's feelings.

[0097] The curation system can estimate the user's emotions and adjust the criteria for selecting news articles based on those estimated emotions. For example, if a user is feeling stressed, the curation system can prioritize selecting news articles with relaxing content. Conversely, if a user is feeling excited, the curation system can select energetic news articles. In this way, the curation system can provide news that matches the user's emotions.

[0098] The generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, the generation unit can generate a video with a soft tone; if the user is in a hurry, it can generate a concise and to-the-point video. Furthermore, if the user is excited, the generation unit can generate a video with an energetic presentation. In this way, the generation unit can adjust the video's presentation based on the user's emotions.

[0099] The service provider can estimate the user's emotions and adjust how the video is displayed based on those emotions. For example, if the user is tense, the service provider can provide a simple and easy-to-understand display method; if the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is excited, the service provider can provide an energetic display method. In this way, the service provider can adjust how the video is displayed based on the user's emotions.

[0100] The system can estimate the user's emotions and adjust the video navigation based on those emotions. For example, if the user is tense, the system can provide simple, easy-to-follow navigation; if the user is relaxed, it can provide navigation with more detailed information. Furthermore, if the user is excited, it can provide energetic navigation. In this way, the system can adjust the video navigation based on the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The curation department searches for and selects news articles. For example, the curation department can search for and select news articles based on the user's interests. Step 2: The summarization section summarizes the content of the news articles selected by the curation section. For example, the summarization section can summarize the content of the selected news articles and provide the information necessary for video generation. Step 3: The generation unit generates a video based on the information summarized by the summarization unit. For example, the generation unit can generate text to be read aloud, news headlines, and images using a large-scale language model, and provide them in a format read aloud by a virtual announcer. The generation unit can also utilize video generation AI to generate videos with a wide range of expressions. Step 4: The provider unit provides the user with the video generated by the generator unit. For example, the provider unit can provide navigation based on the generated video and a link to access the original article.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The curation department searches for and selects news articles, A summarization section that summarizes the content of news articles selected by the aforementioned curation section, A generation unit generates a video based on the information summarized by the summarization unit, The system includes a providing unit that provides the video generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is The system generates text or news headlines and images to be read aloud using a large-scale language model, and then provides them in a format read aloud by a virtual announcer. The system according to feature 1.

3. The generating unit is We utilize AI for video generation to create videos with diverse forms of expression. The system according to feature 1.

4. The aforementioned supply unit is, Navigate based on the generated video and provide a link to access the original article. The system according to feature 1.

5. The curation unit described above is: Search and select news articles based on the user's interests. The system according to feature 1.

6. The summary section above is, Summarize the content of selected news articles and provide the information necessary for video generation. The system according to feature 1.

7. The curation unit described above is: We estimate user sentiment and adjust the criteria for selecting news articles based on that estimated sentiment. The system according to feature 1.

8. The curation unit described above is: Analyze the user's past browsing history to select appropriate news articles. The system according to feature 1.

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