system

Abstract

The system according to this embodiment aims to compare news from the perspectives of different countries and to understand news from a global perspective. [Solution] The system according to the embodiment comprises a collection unit, a translation unit, a comparison unit, and a display unit. The collection unit collects news. The translation unit translates and summarizes the news collected by the collection unit. The comparison unit compares the news translated and summarized by the translation unit. The display unit visually displays the news compared by the comparison unit.

Description

Technical Field

[0006] , , ,

[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, including 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] The conventional technology has a problem that it is difficult to compare news from the perspectives of different countries, and news has not been sufficiently grasped from a global perspective. <000… (The text seems to be cut off here. Please check and provide the complete content if needed.) The system according to the embodiment aims to compare news from the perspectives of different countries and grasp news from a global perspective.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a translation unit, a comparison unit, and a display unit. The collection unit collects news. The translation unit translates and summarizes the news collected by the collection unit. The comparison unit compares the news translated and summarized by the translation unit. The display unit visually displays the news compared by the comparison unit. [Effects of the Invention]

[0007] The system according to this embodiment can compare news from the perspectives of different countries and grasp news from a global perspective. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 gathering system according to an embodiment of the present invention is a system for capturing news from a global perspective. This news gathering system aggregates news from news organizations around the world and displays and compares perspectives from different countries. Furthermore, it utilizes generative AI to automatically translate news articles and provide summaries. It also provides an interactive dashboard that visually displays news trends and statistics. This allows users to understand news from multiple perspectives and access multinational news beyond language barriers. For example, if a user wants to know the perspectives of different countries on a particular news story, the news gathering system aggregates and displays multiple reports on that news story for comparison. The generative AI automatically translates each report and provides a summary. Users can visually check news trends and statistics through the interactive dashboard. This mechanism allows users to grasp news from multiple perspectives and broaden their horizons. In addition, because it allows access to multinational news beyond language barriers, it prevents information bias. Furthermore, by visually displaying news trends and statistics, users can easily grasp the overall picture of the news. In this way, the news gathering system enables users to understand news from multiple perspectives and access multinational news beyond language barriers.

[0029] The news gathering system according to this embodiment comprises a gathering unit, a translation unit, a comparison unit, and a display unit. The gathering unit gathers news. The gathering unit can gather news from, for example, news organizations around the world. The gathering unit can gather news from, for example, newspapers, television stations, online media, etc. The gathering unit can also automate news gathering using AI. The translation unit translates and summarizes the news gathered by the gathering unit. The translation unit automatically translates news articles using a generation AI and provides a summary. The generation AI translates news articles and generates summaries using, for example, a text generation AI (e.g., LLM). The translation unit can also extract and summarize important parts of news articles using the generation AI. The comparison unit compares the news translated and summarized by the translation unit. The comparison unit can, for example, compare news coverage from different countries on the same news. The comparison unit can also automate news comparison using AI. The display unit visually displays the news compared by the comparison unit. The display unit can, for example, visually display news trends and statistical information. The display unit can also automate the visual display of news using AI. This enables the news gathering system according to the embodiment to allow users to understand news from multiple perspectives and access multinational news across language barriers.

[0030] The collection unit collects news. For example, it can collect news from news organizations around the world. Specifically, it can collect news from newspapers, television stations, and online media. This includes methods such as using RSS feeds and APIs to automatically retrieve the latest news articles from each news organization. Furthermore, the collection unit can automate news collection using AI. AI uses natural language processing technology to analyze the content of news articles and select highly relevant articles. For example, it can filter news based on specific keywords or topics to collect only important news. The collection unit can also collect news from social media. This allows users to obtain news not only from official news organizations but also from the perspective of ordinary citizens. The collection unit centrally manages the news collected from these diverse sources and stores it in a database. This allows the collection unit to efficiently collect news from a wide range of news sources, improving the richness and reliability of information throughout the system.

[0031] The translation department translates and summarizes news collected by the data collection department. The translation department uses generative AI to automatically translate news articles and provide summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to translate news articles and generate summaries. Specifically, the generative AI analyzes the entire news article, understands the context, and then provides an appropriate translation. Furthermore, the generative AI can also extract and summarize the most important parts of the news article. For example, the generative AI can concisely summarize the main points of an article based on its headline, lead paragraph, and key keywords. This allows users to quickly grasp important information without having to read long articles. The translation department supports multiple languages ​​and can translate into any language according to user settings. Additionally, the translation department can utilize dictionaries of specialized terminology and proper nouns to improve translation accuracy. This enables the translation department to accurately and quickly translate and summarize news articles and provide them to users.

[0032] The comparison unit compares news articles translated and summarized by the translation unit. For example, the comparison unit can compare news coverage from different countries about the same news story. Specifically, the comparison unit can automate news comparisons using AI. The AI ​​uses natural language processing technology to analyze the content of news articles and identify similarities and differences. For instance, it can compare how different countries' news organizations report on the same event, revealing differences in reporting perspectives and emphasis. Furthermore, the comparison unit can evaluate the reliability and bias of news articles. Based on historical data and statistics, the AI ​​evaluates the reliability of each news organization and provides this evaluation to the user. This allows the user to understand the news from different perspectives and obtain more objective information. The comparison unit stores these comparison results in a database for later reference. This enables the comparison unit to provide a multifaceted analysis of the news and support the user's information gathering.

[0033] The display unit visually displays news compared by the comparison unit. The display unit can, for example, visually display news trends and statistics. Specifically, it uses graphs, charts, heatmaps, etc., to visually represent news trends and statistics. This allows users to intuitively understand the overall picture of the news. Furthermore, the display unit can automate the visual display of news using AI. The AI ​​analyzes the collected data and selects the optimal visualization method. For example, it can display the frequency and regional distribution of news on a specific topic using a heatmap, providing users with visual insights. The display unit can also provide customized displays according to user settings. For example, it can provide a filtering function that displays only news on specific topics or regions. This helps the display unit help users quickly find information of interest. The display unit can update these visual displays in real time, providing the latest information. This allows the display unit to enable users to understand news from multiple perspectives and access multinational news across language barriers.

[0034] The data collection unit can gather news from news organizations around the world. For example, it can collect news from newspapers, television stations, and online media. The data collection unit can also automate news collection using AI. For instance, it can use AI to automatically crawl news websites on the internet and collect the latest news. Furthermore, the data collection unit can use AI to collect news from social media. For example, it can use AI to analyze trends on social media and collect relevant news. This allows for the collection of news from around the world, providing a global perspective.

[0035] The translation department can automatically translate news articles using generative AI and provide summaries. For example, the translation department can automatically translate news articles using generative AI. The generative AI can translate news articles and generate summaries using, for example, text generation AI (e.g., LLM). The translation department can also use generative AI to extract and summarize the important parts of news articles. For example, the translation department can input a prompt to the generative AI such as "Summarize the main points of this news article," and the generative AI will extract the main points and generate a summary. In this way, the translation and summarization of news articles are automated by using generative AI.

[0036] The comparison unit can compare news coverage from different countries about the same news story. For example, it can collect news coverage from different countries about the same news story and compare them. The comparison unit can also automate news comparison using AI. For example, it can use AI to analyze the content of news articles and extract differences in coverage from different countries. Furthermore, the comparison unit can use AI to perform sentiment analysis on news articles and compare the differences in sentiment in coverage from different countries. This allows for a multifaceted understanding of the news by comparing coverage from different countries.

[0037] The display unit can visually display news trends and statistics. For example, it can visually display news trends and statistics using graphs, charts, infographics, etc. The display unit can also automate the visual display of news using AI. For example, the display unit can use AI to analyze news trends and visually display the results. The display unit can also use AI to analyze news statistics and visually display the results. This makes it easier for users to grasp the overall picture of the news by visually displaying news trends and statistics.

[0038] It includes a customization section. This customization section provides users with the ability to customize news. For example, the customization section allows users to filter news based on their interests. The customization section can also automate news customization using AI. For instance, the customization section can use AI to analyze a user's past news browsing history and customize news based on their interests. Furthermore, the customization section can use AI to filter news based on the user's current areas of interest. This allows users to customize news to their own interests.

[0039] The system includes an analysis unit. The analysis unit analyzes news trends using a generative AI. For example, the analysis unit uses a generative AI to analyze news trends. The generative AI analyzes news articles using a text generation AI (e.g., LLM) and extracts trends. The analysis unit can also use the generative AI to extract important parts of news articles and analyze their trends. For example, the analysis unit inputs a prompt to the generative AI, "Please analyze the trends of this news article," and the generative AI extracts and analyzes the trends. In this way, news trend analysis is automated by using a generative AI.

[0040] The data collection unit can analyze the user's past news browsing history and select the optimal data collection method. For example, the data collection unit may prioritize collecting news categories that the user has frequently viewed in the past. For example, the data collection unit may collect from news sources that the user has viewed for extended periods in the past. For example, the data collection unit may collect based on news that the user has previously given a high rating to. In this way, by analyzing the user's past news browsing history, the optimal news collection method can be provided. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past news browsing history into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter news based on the user's current areas of interest. For example, the data collection unit may prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit may filter news based on keywords the user has recently searched for. For example, the data collection unit may collect news related to topics the user follows on social media. This allows for the provision of more relevant news by filtering news based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the news filtering.

[0042] The data collection unit can prioritize collecting highly relevant news by considering the user's geographical location information when collecting news. For example, the data collection unit may prioritize collecting news related to the area where the user is currently located. For example, the data collection unit may collect news related to places the user has visited in the past. For example, the data collection unit may collect news related to places the user plans to visit in the future. This allows the system to provide highly relevant news by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​perform the task of collecting highly relevant news.

[0043] The data collection unit can analyze a user's social media activity and collect relevant news when gathering news. For example, the data collection unit can collect news related to news that the user has shared on social media. For example, the data collection unit can collect news related to accounts that the user follows on social media. For example, the data collection unit can collect news related to news that the user has "liked" on social media. By analyzing the user's social media activity, it is possible to provide highly relevant news. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform the collection of relevant news.

[0044] The translation unit can adjust the level of detail in the translation based on the importance of the news. For example, the translation unit will translate important news in detail. For example, the translation unit will translate general news concisely. For example, the translation unit will translate light news in a summarized manner. By adjusting the level of detail in the translation based on the importance of the news, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the translation unit can input the importance of the news into a generative AI and have the generative AI perform the adjustment of the level of detail in the translation.

[0045] The translation unit can apply different translation algorithms depending on the news category during translation. For example, the translation unit might apply a translation algorithm that includes specialized terminology to political news, a casual translation algorithm to entertainment news, and a concise translation algorithm to sports news. By applying different translation algorithms depending on the news category, it can provide more appropriate translations. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input the news category into a generative AI and have the generative AI perform the application of the translation algorithm.

[0046] The translation unit can determine translation priorities based on the timing of news publication. For example, the unit might prioritize translating the latest news, or postpone translating older news, or prioritize news related to a specific event. By prioritizing translations based on the timing of news publication, the unit can provide more appropriate translations. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit can input the timing of news publication into a generative AI and have the generative AI determine the translation priorities.

[0047] The translation unit can adjust the order of translations based on the relevance of the news articles during the translation process. For example, the translation unit may prioritize translating news articles relevant to the user's interests. For example, the translation unit may determine the order of translations based on the user's past browsing history. For example, the translation unit may prioritize translating news articles relevant to the user's current location. By adjusting the order of translations based on the relevance of the news articles, more appropriate translations can be provided. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit may input the relevance of the news articles into a generative AI and have the generative AI perform the adjustment of the translation order.

[0048] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between news articles. For example, the comparison unit can compare news articles related to the same topic. For example, the comparison unit can compare news articles from different perspectives. For example, the comparison unit can compare news articles while considering the reliability of the news source. This improves the accuracy of the comparison by considering the interrelationships between news articles. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the interrelationships between news articles into AI and have AI perform the improvement of the comparison accuracy.

[0049] The comparison unit can perform comparisons while considering the attribute information of the news source. For example, the comparison unit may consider the nationality of the news source. For example, the comparison unit may consider the political stance of the news source. For example, the comparison unit may consider the reliability of the news source. By considering the attribute information of the news source, a more appropriate comparison can be provided. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the attribute information of the news source into AI and have the AI ​​perform the comparison.

[0050] The comparison unit can perform comparisons while considering the geographical distribution of news. For example, the comparison unit can compare news from the same region. For example, the comparison unit can compare news from different regions. For example, the comparison unit can compare geographically related news. By considering the geographical distribution of news, a more appropriate comparison can be provided. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the geographical distribution of news into AI and have the AI ​​perform the comparison.

[0051] The comparison unit can improve the accuracy of the comparison by referring to relevant literature related to the news during the comparison process. For example, the comparison unit may refer to academic papers related to the news for comparison. For example, the comparison unit may refer to past news reports related to the news for comparison. For example, the comparison unit may refer to statistical data related to the news for comparison. This improves the accuracy of the comparison by referring to relevant literature related to the news. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input relevant literature related to the news into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0052] The display unit can adjust the level of detail displayed based on the importance of the news. For example, it can display important news in detail, general news concisely, and minor news in a summarized format. By adjusting the level of detail based on the importance of the news, it can provide more relevant information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the importance of the news into the AI ​​and have the AI ​​perform the adjustment of the level of detail.

[0053] The display unit can apply different display algorithms depending on the news category when displaying information. For example, the display unit may apply a display algorithm that includes specialized terminology to political news, a casual display algorithm to entertainment news, and a concise display algorithm to sports news. By applying different display algorithms depending on the news category, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the news category into the AI ​​and have the AI ​​perform the application of the display algorithm.

[0054] The display unit can determine the display priority based on the timing of news publication when displaying news. For example, the display unit may prioritize displaying the latest news. For example, it may postpone displaying older news. For example, it may prioritize displaying news related to a specific event. By determining the display priority based on the timing of news publication, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the timing of news publication into the AI ​​and have the AI ​​perform the determination of the display priority.

[0055] The display unit can adjust the display order based on the relevance of the news articles when displaying them. For example, the display unit may prioritize displaying news related to the user's interests. For example, the display unit may determine the display order based on the user's past browsing history. For example, the display unit may prioritize displaying news related to the user's current location. By adjusting the display order based on the relevance of the news articles, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the relevance of the news articles into the AI ​​and have the AI ​​perform the adjustment of the display order.

[0056] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit may prioritize suggesting customization options previously selected by the user. For example, the customization unit may suggest the optimal customization method based on the user's past customization history. For example, the customization unit may suggest options based on customization options that the user has previously given high ratings to. In this way, the optimal customization method can be provided by referring to the user's past customization history. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit may input the user's past customization history into AI and have the AI ​​select the optimal customization method.

[0057] The customization unit can select the optimal customization method by considering the user's device information during the customization process. For example, if the user is using a smartphone, the customization unit provides customization options that match the screen size. For example, if the user is using a tablet, the customization unit provides customization options optimized for larger screens. For example, if the user is using a smartwatch, the customization unit provides concise and highly visible customization options. In this way, the optimal customization method can be provided by considering the user's device information. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input the user's device information into the AI ​​and have the AI ​​select the optimal customization method.

[0058] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. For example, the analysis unit selects an algorithm that improves the accuracy of the analysis from past analysis data. For example, the analysis unit analyzes past analysis data and selects the most efficient analysis algorithm. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into AI and have AI perform the optimization of the analysis algorithm.

[0059] The analysis unit can weight the analysis data based on the timing of news publication during the analysis. For example, the analysis unit may prioritize the latest news in its analysis. For example, the analysis unit may disregard past news in its analysis. For example, the analysis unit may prioritize news related to a specific event in its analysis. By weighting the analysis data based on the timing of news publication, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the timing of news publication into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The news gathering system can also include a gathering unit that analyzes the user's past news browsing history and selects the optimal news gathering method. For example, it could prioritize gathering news categories that the user has frequently viewed in the past, gather news from sources that the user has viewed for extended periods in the past, or gather news based on news that the user has previously given a high rating to. In this way, by analyzing the user's past news browsing history, the system can provide the most optimal news gathering method.

[0062] The news gathering system can also include a collection unit that prioritizes collecting highly relevant news by considering the user's geographical location. For example, it could prioritize collecting news related to the user's current location, news related to places the user has visited in the past, and news related to places the user plans to visit in the future. This allows the system to provide highly relevant news by considering the user's geographical location.

[0063] The news gathering system can further include a collection unit that analyzes users' social media activity and collects relevant news. For example, it can collect news related to news shared by users on social media, news related to accounts followed by users on social media, and news related to news liked by users on social media. This allows the system to provide highly relevant news by analyzing users' social media activity.

[0064] A news gathering system can also include a translation section that prioritizes translation based on the timing of news release. For example, it might prioritize translating the latest news, delaying older news, or prioritizing news related to specific events. By prioritizing translation based on the timing of news release, it can provide more appropriate translations.

[0065] The news collection system may also include a comparison unit that improves the accuracy of comparisons by referencing related literature for the news. For example, it may compare by referring to academic papers related to the news, past news reports related to the news, or statistical data related to the news. This improves the accuracy of comparisons by referencing related literature for the news.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects news. The collection unit can collect news from, for example, news organizations around the world. The collection unit can collect news from, for example, newspapers, television stations, and online media. The collection unit can also automate news collection using AI. Step 2: The translation unit translates and summarizes the news collected by the collection unit. The translation unit uses generative AI to automatically translate news articles and provide summaries. The generative AI uses, for example, text generation AI (e.g., LLM) to translate news articles and generate summaries. The translation unit can also use generative AI to extract and summarize the important parts of news articles. Step 3: The comparison unit compares the news translated and summarized by the translation unit. The comparison unit can, for example, compare news reports from different countries about the same news story. The comparison unit can also automate news comparison using AI. Step 4: The display unit visually displays the news compared by the comparison unit. The display unit can, for example, visually display news trends and statistics. The display unit can also automate the visual display of news using AI.

[0068] (Example of form 2) The news gathering system according to an embodiment of the present invention is a system for capturing news from a global perspective. This news gathering system aggregates news from news organizations around the world and displays and compares perspectives from different countries. Furthermore, it utilizes generative AI to automatically translate news articles and provide summaries. It also provides an interactive dashboard that visually displays news trends and statistics. This allows users to understand news from multiple perspectives and access multinational news beyond language barriers. For example, if a user wants to know the perspectives of different countries on a particular news story, the news gathering system aggregates and displays multiple reports on that news story for comparison. The generative AI automatically translates each report and provides a summary. Users can visually check news trends and statistics through the interactive dashboard. This mechanism allows users to grasp news from multiple perspectives and broaden their horizons. In addition, because it allows access to multinational news beyond language barriers, it prevents information bias. Furthermore, by visually displaying news trends and statistics, users can easily grasp the overall picture of the news. In this way, the news gathering system enables users to understand news from multiple perspectives and access multinational news beyond language barriers.

[0069] The news gathering system according to this embodiment comprises a gathering unit, a translation unit, a comparison unit, and a display unit. The gathering unit gathers news. The gathering unit can gather news from, for example, news organizations around the world. The gathering unit can gather news from, for example, newspapers, television stations, online media, etc. The gathering unit can also automate news gathering using AI. The translation unit translates and summarizes the news gathered by the gathering unit. The translation unit automatically translates news articles using a generation AI and provides a summary. The generation AI translates news articles and generates summaries using, for example, a text generation AI (e.g., LLM). The translation unit can also extract and summarize important parts of news articles using the generation AI. The comparison unit compares the news translated and summarized by the translation unit. The comparison unit can, for example, compare news coverage from different countries on the same news. The comparison unit can also automate news comparison using AI. The display unit visually displays the news compared by the comparison unit. The display unit can, for example, visually display news trends and statistical information. The display unit can also automate the visual display of news using AI. This enables the news gathering system according to the embodiment to allow users to understand news from multiple perspectives and access multinational news across language barriers.

[0070] The collection unit collects news. For example, it can collect news from news organizations around the world. Specifically, it can collect news from newspapers, television stations, and online media. This includes methods such as using RSS feeds and APIs to automatically retrieve the latest news articles from each news organization. Furthermore, the collection unit can automate news collection using AI. AI uses natural language processing technology to analyze the content of news articles and select highly relevant articles. For example, it can filter news based on specific keywords or topics to collect only important news. The collection unit can also collect news from social media. This allows users to obtain news not only from official news organizations but also from the perspective of ordinary citizens. The collection unit centrally manages the news collected from these diverse sources and stores it in a database. This allows the collection unit to efficiently collect news from a wide range of news sources, improving the richness and reliability of information throughout the system.

[0071] The translation department translates and summarizes news collected by the data collection department. The translation department uses generative AI to automatically translate news articles and provide summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to translate news articles and generate summaries. Specifically, the generative AI analyzes the entire news article, understands the context, and then provides an appropriate translation. Furthermore, the generative AI can also extract and summarize the most important parts of the news article. For example, the generative AI can concisely summarize the main points of an article based on its headline, lead paragraph, and key keywords. This allows users to quickly grasp important information without having to read long articles. The translation department supports multiple languages ​​and can translate into any language according to user settings. Additionally, the translation department can utilize dictionaries of specialized terminology and proper nouns to improve translation accuracy. This enables the translation department to accurately and quickly translate and summarize news articles and provide them to users.

[0072] The comparison unit compares news articles translated and summarized by the translation unit. For example, the comparison unit can compare news coverage from different countries about the same news story. Specifically, the comparison unit can automate news comparisons using AI. The AI ​​uses natural language processing technology to analyze the content of news articles and identify similarities and differences. For instance, it can compare how different countries' news organizations report on the same event, revealing differences in reporting perspectives and emphasis. Furthermore, the comparison unit can evaluate the reliability and bias of news articles. Based on historical data and statistics, the AI ​​evaluates the reliability of each news organization and provides this evaluation to the user. This allows the user to understand the news from different perspectives and obtain more objective information. The comparison unit stores these comparison results in a database for later reference. This enables the comparison unit to provide a multifaceted analysis of the news and support the user's information gathering.

[0073] The display unit visually displays news compared by the comparison unit. The display unit can, for example, visually display news trends and statistics. Specifically, it uses graphs, charts, heatmaps, etc., to visually represent news trends and statistics. This allows users to intuitively understand the overall picture of the news. Furthermore, the display unit can automate the visual display of news using AI. The AI ​​analyzes the collected data and selects the optimal visualization method. For example, it can display the frequency and regional distribution of news on a specific topic using a heatmap, providing users with visual insights. The display unit can also provide customized displays according to user settings. For example, it can provide a filtering function that displays only news on specific topics or regions. This helps the display unit help users quickly find information of interest. The display unit can update these visual displays in real time, providing the latest information. This allows the display unit to enable users to understand news from multiple perspectives and access multinational news across language barriers.

[0074] The data collection unit can gather news from news organizations around the world. For example, it can collect news from newspapers, television stations, and online media. The data collection unit can also automate news collection using AI. For instance, it can use AI to automatically crawl news websites on the internet and collect the latest news. Furthermore, the data collection unit can use AI to collect news from social media. For example, it can use AI to analyze trends on social media and collect relevant news. This allows for the collection of news from around the world, providing a global perspective.

[0075] The translation department can automatically translate news articles using generative AI and provide summaries. For example, the translation department can automatically translate news articles using generative AI. The generative AI can translate news articles and generate summaries using, for example, text generation AI (e.g., LLM). The translation department can also use generative AI to extract and summarize the important parts of news articles. For example, the translation department can input a prompt to the generative AI such as "Summarize the main points of this news article," and the generative AI will extract the main points and generate a summary. In this way, the translation and summarization of news articles are automated by using generative AI.

[0076] The comparison unit can compare news coverage from different countries about the same news story. For example, it can collect news coverage from different countries about the same news story and compare them. The comparison unit can also automate news comparison using AI. For example, it can use AI to analyze the content of news articles and extract differences in coverage from different countries. Furthermore, the comparison unit can use AI to perform sentiment analysis on news articles and compare the differences in sentiment in coverage from different countries. This allows for a multifaceted understanding of the news by comparing coverage from different countries.

[0077] The display unit can visually display news trends and statistics. For example, it can visually display news trends and statistics using graphs, charts, infographics, etc. The display unit can also automate the visual display of news using AI. For example, the display unit can use AI to analyze news trends and visually display the results. The display unit can also use AI to analyze news statistics and visually display the results. This makes it easier for users to grasp the overall picture of the news by visually displaying news trends and statistics.

[0078] It includes a customization section. This customization section provides users with the ability to customize news. For example, the customization section allows users to filter news based on their interests. The customization section can also automate news customization using AI. For instance, the customization section can use AI to analyze a user's past news browsing history and customize news based on their interests. Furthermore, the customization section can use AI to filter news based on the user's current areas of interest. This allows users to customize news to their own interests.

[0079] The system includes an analysis unit. The analysis unit analyzes news trends using a generative AI. For example, the analysis unit uses a generative AI to analyze news trends. The generative AI analyzes news articles using a text generation AI (e.g., LLM) and extracts trends. The analysis unit can also use the generative AI to extract important parts of news articles and analyze their trends. For example, the analysis unit inputs a prompt to the generative AI, "Please analyze the trends of this news article," and the generative AI extracts and analyzes the trends. In this way, news trend analysis is automated by using a generative AI.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of news collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect news during times when the user is relaxed. For example, if the user is excited, the data collection unit will immediately collect the latest news. For example, if the user is relaxed, the data collection unit will collect news periodically. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the delivery of news at a more appropriate time by adjusting the timing of news collection according to the user's emotions.

[0081] The data collection unit can analyze the user's past news browsing history and select the optimal data collection method. For example, the data collection unit may prioritize collecting news categories that the user has frequently viewed in the past. For example, the data collection unit may collect from news sources that the user has viewed for extended periods in the past. For example, the data collection unit may collect based on news that the user has previously given a high rating to. In this way, by analyzing the user's past news browsing history, the optimal news collection method can be provided. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past news browsing history into AI and have the AI ​​select the optimal data collection method.

[0082] The data collection unit can filter news based on the user's current areas of interest. For example, the data collection unit may prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit may filter news based on keywords the user has recently searched for. For example, the data collection unit may collect news related to topics the user follows on social media. This allows for the provision of more relevant news by filtering news based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into the AI ​​and have the AI ​​perform the news filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of news to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting relaxing news. For example, if the user is excited, the data collection unit will prioritize collecting the latest news. For example, if the user is relaxed, the data collection unit will prioritize collecting interesting news. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more relevant news by prioritizing news according to the user's emotions.

[0084] The data collection unit can prioritize collecting highly relevant news by considering the user's geographical location information when collecting news. For example, the data collection unit may prioritize collecting news related to the area where the user is currently located. For example, the data collection unit may collect news related to places the user has visited in the past. For example, the data collection unit may collect news related to places the user plans to visit in the future. This allows the system to provide highly relevant news by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​perform the task of collecting highly relevant news.

[0085] The data collection unit can analyze a user's social media activity and collect relevant news when gathering news. For example, the data collection unit can collect news related to news that the user has shared on social media. For example, the data collection unit can collect news related to accounts that the user follows on social media. For example, the data collection unit can collect news related to news that the user has "liked" on social media. By analyzing the user's social media activity, it is possible to provide highly relevant news. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform the collection of relevant news.

[0086] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is relaxed, the translation unit will use softer language. If the user is in a hurry, the translation unit will use concise language. If the user is excited, the translation unit will use more emphasized language. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate translations by adjusting the translation's expression according to the user's emotions.

[0087] The translation unit can adjust the level of detail in the translation based on the importance of the news. For example, the translation unit will translate important news in detail. For example, the translation unit will translate general news concisely. For example, the translation unit will translate light news in a summarized manner. By adjusting the level of detail in the translation based on the importance of the news, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the translation unit can input the importance of the news into a generative AI and have the generative AI perform the adjustment of the level of detail in the translation.

[0088] The translation unit can apply different translation algorithms depending on the news category during translation. For example, the translation unit might apply a translation algorithm that includes specialized terminology to political news, a casual translation algorithm to entertainment news, and a concise translation algorithm to sports news. By applying different translation algorithms depending on the news category, it can provide more appropriate translations. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input the news category into a generative AI and have the generative AI perform the application of the translation algorithm.

[0089] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit will provide a short, concise translation. If the user is relaxed, the translation unit will provide a detailed translation. If the user is excited, the translation unit will provide a translation with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate translations by adjusting the translation length according to the user's emotions.

[0090] The translation unit can determine translation priorities based on the timing of news publication. For example, the unit might prioritize translating the latest news, or postpone translating older news, or prioritize news related to a specific event. By prioritizing translations based on the timing of news publication, the unit can provide more appropriate translations. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit can input the timing of news publication into a generative AI and have the generative AI determine the translation priorities.

[0091] The translation unit can adjust the order of translations based on the relevance of the news articles during the translation process. For example, the translation unit may prioritize translating news articles relevant to the user's interests. For example, the translation unit may determine the order of translations based on the user's past browsing history. For example, the translation unit may prioritize translating news articles relevant to the user's current location. By adjusting the order of translations based on the relevance of the news articles, more appropriate translations can be provided. Some or all of the above processes in the translation unit may be performed using or without a generative AI. For example, the translation unit may input the relevance of the news articles into a generative AI and have the generative AI perform the adjustment of the translation order.

[0092] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated emotions. For example, if the user is relaxed, the comparison unit provides detailed comparison criteria. For example, if the user is in a hurry, the comparison unit provides concise comparison criteria. For example, if the user is excited, the comparison unit provides visually stimulating comparison criteria. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate comparisons by adjusting the comparison criteria according to the user's emotions.

[0093] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between news articles. For example, the comparison unit can compare news articles related to the same topic. For example, the comparison unit can compare news articles from different perspectives. For example, the comparison unit can compare news articles while considering the reliability of the news source. This improves the accuracy of the comparison by considering the interrelationships between news articles. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the interrelationships between news articles into AI and have AI perform the improvement of the comparison accuracy.

[0094] The comparison unit can perform comparisons while considering the attribute information of the news source. For example, the comparison unit may consider the nationality of the news source. For example, the comparison unit may consider the political stance of the news source. For example, the comparison unit may consider the reliability of the news source. By considering the attribute information of the news source, a more appropriate comparison can be provided. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the attribute information of the news source into AI and have the AI ​​perform the comparison.

[0095] The comparison unit can estimate the user's emotions and adjust the order in which the comparison results are displayed based on the estimated emotions. For example, if the user is relaxed, the comparison unit will prioritize displaying detailed comparison results. For example, if the user is in a hurry, the comparison unit will prioritize displaying concise comparison results. For example, if the user is excited, the comparison unit will prioritize displaying visually stimulating comparison results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by adjusting the display order of comparison results according to the user's emotions.

[0096] The comparison unit can perform comparisons while considering the geographical distribution of news. For example, the comparison unit can compare news from the same region. For example, the comparison unit can compare news from different regions. For example, the comparison unit can compare geographically related news. By considering the geographical distribution of news, a more appropriate comparison can be provided. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input the geographical distribution of news into AI and have the AI ​​perform the comparison.

[0097] The comparison unit can improve the accuracy of the comparison by referring to relevant literature related to the news during the comparison process. For example, the comparison unit may refer to academic papers related to the news for comparison. For example, the comparison unit may refer to past news reports related to the news for comparison. For example, the comparison unit may refer to statistical data related to the news for comparison. This improves the accuracy of the comparison by referring to relevant literature related to the news. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input relevant literature related to the news into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0098] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by adjusting the display method according to the user's emotions.

[0099] The display unit can adjust the level of detail displayed based on the importance of the news. For example, it can display important news in detail, general news concisely, and minor news in a summarized format. By adjusting the level of detail based on the importance of the news, it can provide more relevant information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the importance of the news into the AI ​​and have the AI ​​perform the adjustment of the level of detail.

[0100] The display unit can apply different display algorithms depending on the news category when displaying information. For example, the display unit may apply a display algorithm that includes specialized terminology to political news, a casual display algorithm to entertainment news, and a concise display algorithm to sports news. By applying different display algorithms depending on the news category, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the news category into the AI ​​and have the AI ​​perform the application of the display algorithm.

[0101] The display unit can estimate the user's emotions and adjust the display order based on the estimated emotions. For example, if the user is relaxed, the display unit will prioritize displaying detailed news. If the user is in a hurry, the display unit will prioritize displaying concise news. If the user is excited, the display unit will prioritize displaying visually stimulating news. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by adjusting the display order according to the user's emotions.

[0102] The display unit can determine the display priority based on the timing of news publication when displaying news. For example, the display unit may prioritize displaying the latest news. For example, it may postpone displaying older news. For example, it may prioritize displaying news related to a specific event. By determining the display priority based on the timing of news publication, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the timing of news publication into the AI ​​and have the AI ​​perform the determination of the display priority.

[0103] The display unit can adjust the display order based on the relevance of the news articles when displaying them. For example, the display unit may prioritize displaying news related to the user's interests. For example, the display unit may determine the display order based on the user's past browsing history. For example, the display unit may prioritize displaying news related to the user's current location. By adjusting the display order based on the relevance of the news articles, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the relevance of the news articles into the AI ​​and have the AI ​​perform the adjustment of the display order.

[0104] The customization section can estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is relaxed, the customization section will provide detailed customization options. For example, if the user is in a hurry, the customization section will provide concise customization options. For example, if the user is excited, the customization section will provide visually stimulating customization options. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by adjusting the customization content according to the user's emotions.

[0105] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit may prioritize suggesting customization options previously selected by the user. For example, the customization unit may suggest the optimal customization method based on the user's past customization history. For example, the customization unit may suggest options based on customization options that the user has previously given high ratings to. In this way, the optimal customization method can be provided by referring to the user's past customization history. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit may input the user's past customization history into AI and have the AI ​​select the optimal customization method.

[0106] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is relaxed, the customization unit will prioritize detailed customization options. If the user is in a hurry, the customization unit will prioritize concise customization options. If the user is excited, the customization unit will prioritize visually stimulating customization options. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by prioritizing customization according to the user's emotions.

[0107] The customization unit can select the optimal customization method by considering the user's device information during the customization process. For example, if the user is using a smartphone, the customization unit provides customization options that match the screen size. For example, if the user is using a tablet, the customization unit provides customization options optimized for larger screens. For example, if the user is using a smartwatch, the customization unit provides concise and highly visible customization options. In this way, the optimal customization method can be provided by considering the user's device information. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input the user's device information into the AI ​​and have the AI ​​select the optimal customization method.

[0108] The analysis unit can estimate the user's emotions and select analysis data based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis data. For example, if the user is in a hurry, the analysis unit provides concise analysis data. For example, if the user is excited, the analysis unit provides visually stimulating analysis data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the selection of analysis data according to the user's emotions, thereby providing more appropriate information.

[0109] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. For example, the analysis unit selects an algorithm that improves the accuracy of the analysis from past analysis data. For example, the analysis unit analyzes past analysis data and selects the most efficient analysis algorithm. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into AI and have AI perform the optimization of the analysis algorithm.

[0110] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will perform detailed analysis frequently. If the user is in a hurry, the analysis unit will perform a concise analysis. If the user is excited, the analysis unit will perform a visually stimulating analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate information by adjusting the frequency of analysis according to the user's emotions.

[0111] The analysis unit can weight the analysis data based on the timing of news publication during the analysis. For example, the analysis unit may prioritize the latest news in its analysis. For example, the analysis unit may disregard past news in its analysis. For example, the analysis unit may prioritize news related to a specific event in its analysis. By weighting the analysis data based on the timing of news publication, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the timing of news publication into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The news gathering system can also include a display unit that estimates the user's emotions and adjusts how news is displayed based on those emotions. For example, if the user is relaxed, detailed news articles may be displayed. If the user is in a hurry, concise news articles focusing on the main points may be displayed. If the user is excited, news articles with visually stimulating effects may be displayed. Emotion estimation is achieved using an emotion engine or generative AI. This enables optimal news display tailored to the user's emotions, improving the user experience.

[0114] The news gathering system can also include a gathering unit that analyzes the user's past news browsing history and selects the optimal news gathering method. For example, it could prioritize gathering news categories that the user has frequently viewed in the past, gather news from sources that the user has viewed for extended periods in the past, or gather news based on news that the user has previously given a high rating to. In this way, by analyzing the user's past news browsing history, the system can provide the most optimal news gathering method.

[0115] The news gathering system can also include a gathering unit that estimates the user's emotions and adjusts the timing of news collection based on those emotions. For example, if the user is stressed, news can be collected during times when they are relaxed. If the user is excited, the latest news can be collected immediately. If the user is relaxed, news can be collected periodically. Emotion estimation can be achieved using an emotion engine or generative AI. This allows news to be delivered at a more appropriate time by adjusting the timing of news collection according to the user's emotions.

[0116] The news gathering system can also include a collection unit that prioritizes collecting highly relevant news by considering the user's geographical location. For example, it could prioritize collecting news related to the user's current location, news related to places the user has visited in the past, and news related to places the user plans to visit in the future. This allows the system to provide highly relevant news by considering the user's geographical location.

[0117] The news gathering system can further include a gathering unit that estimates the user's emotions and determines the priority of news to collect based on those emotions. For example, if the user is stressed, it would prioritize collecting relaxing news. If the user is excited, it would prioritize collecting the latest news. If the user is relaxed, it would prioritize collecting interesting news. Emotion estimation is achieved using an emotion engine or generative AI. This allows for the provision of more relevant news by prioritizing news according to the user's emotions.

[0118] The news gathering system can further include a collection unit that analyzes users' social media activity and collects relevant news. For example, it can collect news related to news shared by users on social media, news related to accounts followed by users on social media, and news related to news liked by users on social media. This allows the system to provide highly relevant news by analyzing users' social media activity.

[0119] The news gathering system may also include a translation unit that estimates the user's emotions and adjusts the translation's expression based on those emotions. For example, if the user is relaxed, the translation might use softer language. If the user is in a hurry, it might use concise language. If the user is excited, it might use more emphasized language. Emotion estimation is achieved using an emotion engine or generative AI. This allows for more appropriate translations by adjusting the translation's expression according to the user's emotions.

[0120] A news gathering system can also include a translation section that prioritizes translation based on the timing of news release. For example, it might prioritize translating the latest news, delaying older news, or prioritizing news related to specific events. By prioritizing translation based on the timing of news release, it can provide more appropriate translations.

[0121] The news gathering system may also include a comparison unit that estimates the user's emotions and adjusts the comparison criteria based on those emotions. For example, if the user is relaxed, it might provide detailed comparison criteria; if the user is in a hurry, it might provide concise criteria; and if the user is excited, it might provide visually stimulating criteria. Emotion estimation is achieved using an emotion engine or generative AI, among other things. This allows for more appropriate comparisons by adjusting the comparison criteria according to the user's emotions.

[0122] The news collection system may also include a comparison unit that improves the accuracy of comparisons by referencing related literature for the news. For example, it may compare by referring to academic papers related to the news, past news reports related to the news, or statistical data related to the news. This improves the accuracy of comparisons by referencing related literature for the news.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects news. The collection unit can collect news from, for example, news organizations around the world. The collection unit can collect news from, for example, newspapers, television stations, and online media. The collection unit can also automate news collection using AI. Step 2: The translation unit translates and summarizes the news collected by the collection unit. The translation unit uses generative AI to automatically translate news articles and provide summaries. The generative AI uses, for example, text generation AI (e.g., LLM) to translate news articles and generate summaries. The translation unit can also use generative AI to extract and summarize the important parts of news articles. Step 3: The comparison unit compares the news translated and summarized by the translation unit. The comparison unit can, for example, compare news reports from different countries about the same news story. The comparison unit can also automate news comparison using AI. Step 4: The display unit visually displays the news compared by the comparison unit. The display unit can, for example, visually display news trends and statistics. The display unit can also automate the visual display of news using AI.

[0125] 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.

[0126] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, translation unit, comparison unit, display unit, customization unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects news from news organizations around the world. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically translates news articles using generation AI and provides summaries. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares news from different countries. The display unit is implemented by the control unit 46A of the smart device 14 and visually displays news trends and statistical information. The customization unit is implemented by the control unit 46A of the smart device 14 and filters news based on user interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes news trends using generation AI. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, translation unit, comparison unit, display unit, customization unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects news from news organizations around the world. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically translates news articles using generation AI and provides summaries. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares news from different countries. The display unit is implemented by the control unit 46A of the smart glasses 214 and visually displays news trends and statistical information. The customization unit is implemented by the control unit 46A of the smart glasses 214 and filters news based on user interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes news trends using generation AI. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, translation unit, comparison unit, display unit, customization unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects news from news organizations around the world. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically translates news articles using generation AI and provides summaries. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares news from different countries. The display unit is implemented by the control unit 46A of the headset terminal 314 and visually displays news trends and statistical information. The customization unit is implemented by the control unit 46A of the headset terminal 314 and filters news based on user interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes news trends using generation AI. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, translation unit, comparison unit, display unit, customization unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects news from news organizations around the world. The translation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically translates news articles using generation AI and provides summaries. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares news from different countries. The display unit is implemented by, for example, the control unit 46A of the robot 414 and visually displays news trends and statistical information. The customization unit is implemented by, for example, the control unit 46A of the robot 414 and filters news based on user interests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes news trends using generation AI. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The news collection department, The translation department translates and summarizes the news collected by the aforementioned collection department, A comparison section compares the news translated and summarized by the aforementioned translation section, A display unit visually displays the news compared by the comparison unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect news from news organizations around the world. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned translation department, This system uses AI to automatically translate and summarize news articles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The comparison unit is, Compare news coverage from different countries about the same news story. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Visually display news trends and statistics. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features a customization section that allows users to customize their news. The system described in Appendix 1, characterized by the features described herein. (Note 7) It features an analysis unit that analyzes news trends using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past news browsing history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting news, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and determines the priority of news to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting news, the system prioritizes collecting highly relevant news by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting news, the system analyzes users' social media activity and gathers relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, During translation, adjust the level of detail based on the importance of the news item. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, When translating, different translation algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned translation department, When translating, prioritize translations based on when the news was published. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, When making comparisons, consider the interrelationships between news articles to improve the accuracy of the comparisons. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, When making comparisons, the attribute information of the news source is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, It estimates the user's sentiment and adjusts the order in which comparison results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The comparison unit is, When making comparisons, the geographical distribution of news should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The comparison unit is, When making comparisons, refer to related literature for news articles to improve the accuracy of the comparisons. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying news, adjust the level of detail based on the importance of the news item. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying news, different display algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and adjusts the display order based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying news, the display priority is determined based on when the news was published. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying news, adjust the display order based on the relevance of the news items. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, The system estimates the user's emotions and selects analysis data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit, It estimates the user's emotions and adjusts the frequency of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned analysis unit, During analysis, the analysis data is weighted based on the timing of news release. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 news collection department, The translation department translates and summarizes the news collected by the aforementioned collection department, A comparison section compares the news translated and summarized by the aforementioned translation section, A display unit visually displays the news compared by the comparison unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect news from news organizations around the world. The system according to feature 1.

3. The aforementioned translation department, This system uses AI to automatically translate news articles and provide summaries. The system according to feature 1.

4. The comparison unit is, Compare news coverage from different countries about the same news story. The system according to feature 1.

5. The aforementioned display unit is Visually display news trends and statistics. The system according to feature 1.

6. It features a customization section that allows users to customize their news. The system according to feature 1.

7. It features an analysis unit that analyzes news trends using generation AI. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news collection based on those estimated emotions. The system according to feature 1.

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