system

Abstract

The system according to this embodiment aims to automate the process from collecting news articles to generating them, thereby efficiently producing high-quality articles. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects news data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates articles based on the analysis results obtained by the analysis unit. The provision unit provides the articles generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process from the collection to the generation of news articles is often performed manually, and there is a problem of low efficiency.

[0005] The system according to the embodiment aims to automate the process from the collection to the generation of news articles and efficiently generate high-quality articles.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects news data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates articles based on the analysis results obtained by the analysis unit. The provision unit provides the articles generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the process from collecting news articles to generating them, enabling the efficient production of high-quality articles. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 device 32. The processor 28, the RAM 30, and the storage device 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 reception 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 reception 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 agent system according to an embodiment of the present invention is a system that utilizes the latest AI technology to help media companies and individual users quickly generate high-quality articles. This system automates a series of processes from news collection and analysis to article composition, providing a new news experience. First, the news agent system uses natural language processing technology to collect news data in real time. The collected data is analyzed by AI to understand trends and relevance. Next, the AI ​​generates personalized articles based on the user's past interests and behavior. This process improves article generation speed, enabling the generation of high-quality articles in an average of 10 minutes or less. Furthermore, the news agent system promotes user engagement through interactive content generation. For example, if a user shows interest in a particular topic, articles related to that topic are automatically generated and provided. This allows users to quickly obtain information tailored to their interests. This system is extremely useful for media companies that require rapid article generation, individual users seeking high-quality information, and small media outlets that feel constrained by time and resources. The news agent system realizes innovation in the article generation process using the latest AI technology and provides customizable content generation that meets user needs. Furthermore, the news agent system was developed to meet the demand for fast, high-quality content in today's world, characterized by the digitalization of media and the increasing speed of information consumption. This ensures both speed and accuracy, creating a society where more people can access high-quality information. In this way, the news agent system can support media companies and individual users in rapidly generating high-quality articles.

[0029] The news agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects news data. The collection unit can collect news data from, for example, news sites and social media on the internet. The collection unit can also collect news data in real time. For example, the collection unit collects news data using RSS feeds from news sites. The collection unit can also collect data from news sites using web scraping technology. The collection unit can also collect news data that users have shown interest in using social media APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes news data using natural language processing technology to grasp trends and relevance. The analysis unit can classify the content of news data using AI and extract important information. For example, the analysis unit classifies the topics of news data using machine learning algorithms. The analysis unit can also perform sentiment analysis of news data using deep learning technology. The generation unit generates articles based on the analysis results obtained by the analysis unit. For example, the generation unit generates personalized articles based on the user's past interests and behavior. The generation unit can automatically generate article content using AI. The generation unit generates articles using, for example, natural language generation technology. The generation unit can also generate articles using template-based generation technology. The delivery unit provides the articles generated by the generation unit. The delivery unit provides articles, for example, through a website or mobile app. The delivery unit can also notify users of the generated articles. The delivery unit provides articles using, for example, email or push notifications. The delivery unit can also share articles through social media. As a result, the news agent system according to this embodiment automates the entire process from news data collection and analysis to article generation and delivery, enabling the rapid generation of high-quality articles. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and delivery unit may be performed using AI or not using AI.

[0030] The data collection unit collects news data. For example, it can collect news data from news sites and social media on the internet. Specifically, it uses RSS feeds from news sites to periodically obtain the latest news articles. RSS feeds are XML-formatted data provided by news sites and contain the latest article information. The data collection unit periodically checks these RSS feeds and retrieves data when new articles are added. The data collection unit can also collect data from news sites using web scraping techniques. Web scraping is a technique that analyzes the HTML structure of web pages and extracts necessary information. The data collection unit analyzes the HTML structure of specific news sites and extracts information such as article titles, body text, and publication dates. Furthermore, the data collection unit can use social media APIs to collect news data that users are interested in. Social media APIs are interfaces for programs to access social media data and can retrieve posts related to specific keywords or hashtags. The data collection unit uses these APIs to collect news articles and trending information that users are interested in. This allows the data collection unit to collect news data in real time from diverse sources on the internet and build an information infrastructure for the entire system. The data collection unit centrally manages the collected data and stores it in a database so that the analysis and generation units can access it. The database is a mechanism for efficiently managing the collected news data and quickly searching for and retrieving necessary information. The data collection unit regularly adds data to the database, ensuring that it always maintains the latest information. This allows the data collection unit to efficiently perform the entire process from news data collection to management, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the collection department. For example, the analysis department uses natural language processing technology to analyze news data and understand trends and relationships. Specifically, it uses natural language processing technology to analyze the content of news articles and extract topics and keywords. This allows the analysis department to extract important information from the collected news data and understand trends. For example, it uses machine learning algorithms to classify the topics of news data. Machine learning algorithms are technologies that learn from large amounts of data and recognize patterns in the data. The analysis department uses machine learning algorithms to analyze the content of news articles and automatically classify the topics of the articles. This allows the analysis department to efficiently extract articles related to specific topics from the collected news data. The analysis department can also perform sentiment analysis of news data using deep learning technology. Deep learning technology is a technology that analyzes data using multi-layered neural networks and has advanced pattern recognition capabilities. The analysis department uses deep learning technology to analyze the sentiment from the content of news articles and determine whether the article has a positive, negative, or neutral sentiment. This allows the analysis department to grasp the emotional aspects of news data and provide users with more relevant information. Furthermore, the analysis department can statistically analyze the collected data to identify trends and patterns. For example, it can analyze the frequency of occurrence of specific keywords to understand when those keywords appear most frequently. This allows the analysis department to extract important trends from the news data and provide users with the latest information. The analysis department provides these analysis results to the generation department, which uses them as foundational information for generating articles. This enables the analysis department to efficiently analyze the collected news data and improve the overall information processing capabilities of the system.

[0032] The generation unit generates articles based on the analysis results obtained by the analysis unit. For example, the generation unit generates personalized articles based on the user's past interests and behavior. Specifically, it analyzes the user's past browsing and click history to identify topics and keywords that the user is interested in. This allows the generation unit to generate articles tailored to the user's interests. The generation unit can also automatically generate article content using AI. For example, it can generate articles using natural language generation technology. Natural language generation technology is a technology that generates natural-sounding sentences that resemble human writing based on text data. The generation unit uses natural language generation technology to generate articles based on the analysis results provided by the analysis unit. This allows the generation unit to quickly generate high-quality articles. The generation unit can also generate articles using template-based generation technology. Template-based generation technology is a technology that generates articles according to a predefined template, and can efficiently generate articles that conform to a specific format. The generation unit selects an appropriate template based on the user's interests and behavior and generates articles according to that template. This allows the generation unit to provide users with consistent articles. Furthermore, the generation unit can evaluate the quality of the generated articles and make corrections as needed. For example, the generated articles are checked for typos and grammatical errors. The generation unit also evaluates whether the article content matches user interests and modifies it as needed. This allows the generation unit to establish a process for providing high-quality articles and improve the overall reliability of the system.

[0033] The Provider unit provides articles generated by the Generation unit. The Provider unit provides articles, for example, through websites and mobile apps. Specifically, generated articles are posted on websites, making them accessible to users. Websites are designed to allow users to efficiently search and view articles of interest, and generated articles are organized and displayed by category and topic. The Provider unit can also provide articles through mobile apps. Mobile apps are designed to allow users to view articles using smartphones and tablets, and generated articles are delivered to users via push notifications and in-app notifications. This allows the Provider unit to provide users with an environment where they can view the latest news articles anytime, anywhere. Furthermore, the Provider unit can also notify users of generated articles. For example, articles can be delivered via email or push notifications. Email notifications involve sending a link to the generated article to the user's registered email address, allowing the user to view the article by opening the email and clicking the link. Push notifications send real-time notifications to smartphones and tablets, allowing users to view the article by tapping the notification. The Provider unit can also share articles through social media. Social media is a platform for users to share articles with other users, and by posting generated articles on social media, the service provider can reach a wider audience. This allows the service provider to deliver generated articles to users in diverse ways, improving the overall usability of the system.

[0034] The interactive unit can generate interactive content. For example, it can generate quizzes and surveys that involve the user. The interactive unit can use AI to generate interactive content based on the user's interests. For example, it can analyze the user's past behavior data and generate quizzes based on topics of interest. The interactive unit can also analyze the user's real-time responses and dynamically adjust the interactive content. For example, it can analyze how the user answered a quiz and adjust the next question. This allows the interactive unit to generate interactive content that promotes user engagement. Some or all of the above processes in the interactive unit may be performed using AI or not.

[0035] The data collection unit can collect news data in real time. For example, the data collection unit collects news data in real time from news sites and social media on the internet. By collecting news data in real time, the data collection unit can quickly obtain the latest information. For example, the data collection unit can collect news data in real time using RSS feeds from news sites. The data collection unit can also collect data in real time from news sites using web scraping technology. The data collection unit can also collect news data that users have shown interest in using social media APIs. As a result, the data collection unit can quickly obtain the latest information by collecting news data in real time. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0036] The analysis department can analyze collected data using AI to understand trends and relationships. For example, the analysis department can analyze news data using natural language processing technology to understand trends and relationships. The analysis department can classify the content of news data using AI and extract important information. For example, the analysis department can classify the topics of news data using machine learning algorithms. The analysis department can also perform sentiment analysis of news data using deep learning technology. This allows the analysis department to quickly grasp trends and relationships through AI analysis. Some or all of the above processes in the analysis department may be performed using AI or not.

[0037] The generation unit can generate personalized articles based on the user's past interests and behavior. For example, the generation unit analyzes the user's past interests and behavior data and generates personalized articles. The generation unit can automatically generate article content using AI. For example, the generation unit generates personalized articles using natural language generation technology. The generation unit can also generate personalized articles using template-based generation technology. This allows the generation unit to generate personalized articles based on the user's interests and behavior. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0038] The distribution unit can provide the generated articles to users. The distribution unit can provide articles, for example, through a website or mobile app. The distribution unit can also notify users of the generated articles. The distribution unit can provide articles, for example, using email or push notifications. The distribution unit can also share articles through social media. This allows the distribution unit to quickly provide the generated articles to users. Some or all of the above processes in the distribution unit may be performed using AI or not.

[0039] The data collection unit can analyze a user's past news browsing history and select the optimal collection method. For example, the data collection unit can prioritize collecting news categories that the user has frequently viewed in the past. If a user tends to browse during specific time periods, the data collection unit can also collect news data according to those times. The data collection unit can also prioritize collecting data from news sources that the user has previously rated highly. This enables efficient news data collection by allowing the data collection unit to select the optimal collection method based on the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0040] The data collection unit can filter news data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting news data related to topics the user is currently interested in. The data collection unit can also filter relevant news data based on the user's search history for specific keywords. The data collection unit can also collect relevant news data based on the content of posts from social media accounts the user follows. This allows the data collection unit to collect highly relevant data by filtering news data 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.

[0041] The data collection unit can prioritize collecting highly relevant data by considering the user's geographical location when collecting news data. For example, the data collection unit can prioritize collecting local news related to the area where the user is currently located. If the user is traveling, the data collection unit can also prioritize collecting news related to their travel destination. If the user has shown interest in a particular region, the data collection unit can also prioritize collecting news related to that region. In this way, the data collection unit can efficiently collect region-related news by collecting highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0042] The data collection unit can analyze users' social media activity and collect relevant data when collecting news data. For example, the data collection unit can collect data related to news that users have shared on social media. The data collection unit can also collect relevant news data based on the content of posts from accounts that users follow. The data collection unit can also collect data related to news that users have "liked" on social media. In this way, the data collection unit can collect news data that matches the user's interests by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the news data. For example, the analysis unit can perform a detailed analysis on important news data, a concise analysis on general news data, and a rapid analysis on urgent news data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the news data. Some or all of the above processes in the analysis unit may be performed using AI, or they may not.

[0044] The analysis unit can apply different analysis algorithms depending on the category of news data during analysis. For example, for economic news, the analysis unit can apply an analysis algorithm that uses economic indicators. For sports news, the analysis unit can also apply an algorithm that analyzes match results and player performance. For political news, the analysis unit can also apply an algorithm that analyzes policies and election results. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of news data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0045] The analysis department can determine the priority of analysis based on when the news data was collected. For example, the analysis department will prioritize the analysis of the most recent news data. The analysis department can also analyze past news data as needed. The analysis department can also prioritize the analysis of news data with the highest urgency. This allows the analysis department to perform efficient analysis by determining the priority of analysis based on when the news data was collected. Some or all of the above processes in the analysis department may be performed using AI, or they may not.

[0046] The analysis unit can adjust the order of analysis based on the relevance of news data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant news data. The analysis unit may also postpone analyzing less relevant news data. The analysis unit may also prioritize analyzing highly relevant news data based on user interests. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of news data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0047] The generation unit can adjust the level of detail in an article based on the importance of the news data during article generation. For example, the generation unit can generate a detailed article for important news data. The generation unit can also generate a concise article for general news data. The generation unit can also generate an article quickly for urgent news data. This allows the generation unit to efficiently generate articles by adjusting the level of detail based on the importance of the news data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0048] The generation unit can apply different generation algorithms depending on the category of news data when generating articles. For example, for economic news, the generation unit can apply a generation algorithm that uses economic indicators. For sports news, the generation unit can also apply an algorithm that generates match results and player performance. For political news, the generation unit can also apply an algorithm that generates policies and election results. By applying different generation algorithms depending on the category of news data, the generation unit can generate more accurate articles. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI.

[0049] The generation unit can determine the priority of articles based on when the news data was collected during article generation. For example, the generation unit will prioritize generating articles for the latest news data. The generation unit can also generate articles for past news data as needed. The generation unit can also prioritize generating articles for news data of high urgency. This allows the generation unit to efficiently generate articles by determining the priority of articles based on when the news data was collected. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI.

[0050] The generation unit can adjust the order of articles based on the relevance of the news data when generating articles. For example, the generation unit can prioritize generating articles based on highly relevant news data. The generation unit can also postpone generating articles based on less relevant news data. The generation unit can also prioritize generating articles based on highly relevant news data based on user interests. This enables efficient article generation by adjusting the order of articles based on the relevance of the news data. Some or all of the above processing in the generation unit may be performed using AI or not.

[0051] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when delivering articles. For example, the content delivery unit can prioritize delivering news categories that the user has frequently viewed in the past. If the content delivery unit has a tendency to browse at a specific time of day, it can also deliver articles according to that time. The content delivery unit can also prioritize delivering articles from news sources that the user has previously given high ratings to. In this way, the content delivery unit can efficiently deliver articles by selecting the optimal delivery method based on the user's past browsing history. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0052] The content provider can customize the content offered based on the user's current areas of interest when providing articles. For example, the provider may prioritize providing articles related to topics the user is currently interested in. The provider may also provide relevant articles based on the user's search history for specific keywords. The provider may also provide relevant articles based on the content of social media accounts the user follows. This allows the provider to provide more relevant articles by customizing the content based on the user's current areas of interest. Some or all of the above processing in the content provider may be performed using AI or not.

[0053] The news delivery system can select the optimal delivery method when providing articles, taking into account the user's geographical location. For example, the system can prioritize providing local news relevant to the user's current location. If the user is traveling, the system can also prioritize providing news relevant to their travel destination. If the user has expressed interest in a particular region, the system can also prioritize providing news relevant to that region. This allows the system to efficiently provide region-related news by selecting the optimal delivery method, taking into account the user's geographical location. Some or all of the above processing in the news delivery system may be performed using AI, or it may not be performed using AI.

[0054] The content provider can analyze the user's social media activity and customize the content provided when delivering articles. For example, the content provider can provide articles related to news that the user has shared on social media. The content provider can also provide relevant articles based on the content of posts from accounts that the user follows. The content provider can also provide articles related to news that the user has "liked" on social media. In this way, the content provider can provide more appropriate articles by analyzing the user's social media activity and customizing the content provided. Some or all of the above processing in the content provider may be performed using AI or not.

[0055] The interactive unit can select the optimal generation method when generating interactive content by referring to the user's past operation history. For example, the interactive unit can prioritize generating interactive content in formats that the user has frequently interacted with in the past. The interactive unit can also generate relevant interactive content based on the user's history of performing specific operations. The interactive unit can also prioritize generating interactive content in formats that the user has previously given high ratings to. This enables efficient content generation by allowing the interactive unit to select the optimal generation method based on the user's past operation history. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0056] The interactive unit can customize the generated content based on the user's current areas of interest when generating interactive content. For example, the interactive unit can generate interactive content related to topics the user is currently interested in. The interactive unit can also generate relevant interactive content based on the user's search history for specific keywords. The interactive unit can also generate relevant interactive content based on the content of social media accounts the user follows. This allows the interactive unit to provide more relevant content by customizing the generated content based on the user's current areas of interest. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0057] The interactive unit can select the optimal generation method when generating interactive content, taking into account the user's geographical location. For example, the interactive unit can prioritize generating local interactive content related to the user's current location. If the user is traveling, the interactive unit can also prioritize generating interactive content related to their travel destination. If the user has shown interest in a particular region, the interactive unit can also prioritize generating interactive content related to that region. In this way, the interactive unit can efficiently generate region-related content by selecting the optimal generation method, taking into account the user's geographical location. Some or all of the above processing in the interactive unit may be performed using AI, or it may be performed without using AI.

[0058] The interactive unit can analyze the user's social media activity and customize the generated content when creating interactive content. For example, the interactive unit can generate interactive content related to news that the user has shared on social media. The interactive unit can also generate relevant interactive content based on posts from accounts that the user follows. The interactive unit can also generate interactive content related to news that the user has "liked" on social media. This allows the interactive unit to provide more appropriate content by analyzing the user's social media activity and customizing the generated content. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] A news agent system can analyze a user's past news browsing history and determine the optimal timing for delivering articles. For example, if a user frequently browsed the news during a specific time period in the past, articles can be delivered accordingly. Similarly, if a user browsed the news frequently on a particular day of the week, articles can be delivered accordingly. Furthermore, if a user browsed the news frequently before and after a specific event, articles can be delivered to coincide with that event. In this way, the news agent system can deliver articles at the optimal time based on the user's past behavior.

[0061] A news agent system can prioritize local news by taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize news related to that region. Similarly, if a user is traveling, it can provide news related to their destination. Furthermore, if a user has expressed interest in a particular region, it can provide news related to that region. This allows the news agent system to provide more relevant news based on the user's geographical location.

[0062] A news agent system can analyze a user's social media activity and provide relevant news. For example, it can provide articles related to news that a user has shared on social media. It can also provide relevant news based on the content of posts from accounts that a user follows. Furthermore, it can provide articles related to news that a user has "liked" on social media. In this way, the news agent system can provide more relevant news based on the user's social media activity.

[0063] A news agent system can analyze a user's past news rating history and select the most suitable news sources. For example, it can prioritize providing articles from news sources that the user has previously given high ratings to. It can also avoid providing articles from news sources that the user has previously given low ratings to. Furthermore, if a user frequently views a particular news source, it can prioritize providing articles from that source. In this way, the news agent system can provide more satisfying news based on the user's past rating history.

[0064] A news agent system can prioritize providing relevant news based on a user's current areas of interest. For example, it can prioritize news related to topics the user is currently interested in. It can also provide relevant news based on the user's search history for specific keywords. Furthermore, it can provide relevant news based on the content of posts from social media accounts the user follows. This allows the news agent system to provide more relevant news based on the user's current areas of interest.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects news data. The collection unit can collect news data from news sites and social media on the internet. The collection unit can also collect news data in real time, for example, by using RSS feeds from news sites or by using web scraping techniques to collect data from news sites. It can also use social media APIs to collect news data that users have shown interest in. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze news data and understand trends and relationships. AI can be used to classify the content of news data and extract important information. For example, machine learning algorithms can be used to classify topics in news data, or deep learning technology can be used to perform sentiment analysis on news data. Step 3: The generation unit generates articles based on the analysis results obtained by the analysis unit. The generation unit generates personalized articles based on the user's past interests and behavior. Article content can be automatically generated using AI, for example, by using natural language generation technology or template-based generation technology. Step 4: The provider delivers the articles generated by the generator. The provider delivers the articles via a website or mobile app. It can also notify users of the generated articles, for example, by using email or push notifications. It can also share the articles through social media.

[0067] (Example of form 2) The news agent system according to an embodiment of the present invention is a system that utilizes the latest AI technology to help media companies and individual users quickly generate high-quality articles. This system automates a series of processes from news collection and analysis to article composition, providing a new news experience. First, the news agent system uses natural language processing technology to collect news data in real time. The collected data is analyzed by AI to understand trends and relevance. Next, the AI ​​generates personalized articles based on the user's past interests and behavior. This process improves article generation speed, enabling the generation of high-quality articles in an average of 10 minutes or less. Furthermore, the news agent system promotes user engagement through interactive content generation. For example, if a user shows interest in a particular topic, articles related to that topic are automatically generated and provided. This allows users to quickly obtain information tailored to their interests. This system is extremely useful for media companies that require rapid article generation, individual users seeking high-quality information, and small media outlets that feel constrained by time and resources. The news agent system realizes innovation in the article generation process using the latest AI technology and provides customizable content generation that meets user needs. Furthermore, the news agent system was developed to meet the demand for fast, high-quality content in today's world, characterized by the digitalization of media and the increasing speed of information consumption. This ensures both speed and accuracy, creating a society where more people can access high-quality information. In this way, the news agent system can support media companies and individual users in rapidly generating high-quality articles.

[0068] The news agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects news data. The collection unit can collect news data from, for example, news sites and social media on the internet. The collection unit can also collect news data in real time. For example, the collection unit collects news data using RSS feeds from news sites. The collection unit can also collect data from news sites using web scraping technology. The collection unit can also collect news data that users have shown interest in using social media APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes news data using natural language processing technology to grasp trends and relevance. The analysis unit can classify the content of news data using AI and extract important information. For example, the analysis unit classifies the topics of news data using machine learning algorithms. The analysis unit can also perform sentiment analysis of news data using deep learning technology. The generation unit generates articles based on the analysis results obtained by the analysis unit. For example, the generation unit generates personalized articles based on the user's past interests and behavior. The generation unit can automatically generate article content using AI. The generation unit generates articles using, for example, natural language generation technology. The generation unit can also generate articles using template-based generation technology. The delivery unit provides the articles generated by the generation unit. The delivery unit provides articles, for example, through a website or mobile app. The delivery unit can also notify users of the generated articles. The delivery unit provides articles using, for example, email or push notifications. The delivery unit can also share articles through social media. As a result, the news agent system according to this embodiment automates the entire process from news data collection and analysis to article generation and delivery, enabling the rapid generation of high-quality articles. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and delivery unit may be performed using AI or not using AI.

[0069] The data collection unit collects news data. For example, it can collect news data from news sites and social media on the internet. Specifically, it uses RSS feeds from news sites to periodically obtain the latest news articles. RSS feeds are XML-formatted data provided by news sites and contain the latest article information. The data collection unit periodically checks these RSS feeds and retrieves data when new articles are added. The data collection unit can also collect data from news sites using web scraping techniques. Web scraping is a technique that analyzes the HTML structure of web pages and extracts necessary information. The data collection unit analyzes the HTML structure of specific news sites and extracts information such as article titles, body text, and publication dates. Furthermore, the data collection unit can use social media APIs to collect news data that users are interested in. Social media APIs are interfaces for programs to access social media data and can retrieve posts related to specific keywords or hashtags. The data collection unit uses these APIs to collect news articles and trending information that users are interested in. This allows the data collection unit to collect news data in real time from diverse sources on the internet and build an information infrastructure for the entire system. The data collection unit centrally manages the collected data and stores it in a database so that the analysis and generation units can access it. The database is a mechanism for efficiently managing the collected news data and quickly searching for and retrieving necessary information. The data collection unit regularly adds data to the database, ensuring that it always maintains the latest information. This allows the data collection unit to efficiently perform the entire process from news data collection to management, improving the overall system performance.

[0070] The analysis department analyzes the data collected by the collection department. For example, the analysis department uses natural language processing technology to analyze news data and understand trends and relationships. Specifically, it uses natural language processing technology to analyze the content of news articles and extract topics and keywords. This allows the analysis department to extract important information from the collected news data and understand trends. For example, it uses machine learning algorithms to classify the topics of news data. Machine learning algorithms are technologies that learn from large amounts of data and recognize patterns in the data. The analysis department uses machine learning algorithms to analyze the content of news articles and automatically classify the topics of the articles. This allows the analysis department to efficiently extract articles related to specific topics from the collected news data. The analysis department can also perform sentiment analysis of news data using deep learning technology. Deep learning technology is a technology that analyzes data using multi-layered neural networks and has advanced pattern recognition capabilities. The analysis department uses deep learning technology to analyze the sentiment from the content of news articles and determine whether the article has a positive, negative, or neutral sentiment. This allows the analysis department to grasp the emotional aspects of news data and provide users with more relevant information. Furthermore, the analysis department can statistically analyze the collected data to identify trends and patterns. For example, it can analyze the frequency of occurrence of specific keywords to understand when those keywords appear most frequently. This allows the analysis department to extract important trends from the news data and provide users with the latest information. The analysis department provides these analysis results to the generation department, which uses them as foundational information for generating articles. This enables the analysis department to efficiently analyze the collected news data and improve the overall information processing capabilities of the system.

[0071] The generation unit generates articles based on the analysis results obtained by the analysis unit. For example, the generation unit generates personalized articles based on the user's past interests and behavior. Specifically, it analyzes the user's past browsing and click history to identify topics and keywords that the user is interested in. This allows the generation unit to generate articles tailored to the user's interests. The generation unit can also automatically generate article content using AI. For example, it can generate articles using natural language generation technology. Natural language generation technology is a technology that generates natural-sounding sentences that resemble human writing based on text data. The generation unit uses natural language generation technology to generate articles based on the analysis results provided by the analysis unit. This allows the generation unit to quickly generate high-quality articles. The generation unit can also generate articles using template-based generation technology. Template-based generation technology is a technology that generates articles according to a predefined template, and can efficiently generate articles that conform to a specific format. The generation unit selects an appropriate template based on the user's interests and behavior and generates articles according to that template. This allows the generation unit to provide users with consistent articles. Furthermore, the generation unit can evaluate the quality of the generated articles and make corrections as needed. For example, the generated articles are checked for typos and grammatical errors. The generation unit also evaluates whether the article content matches user interests and modifies it as needed. This allows the generation unit to establish a process for providing high-quality articles and improve the overall reliability of the system.

[0072] The Provider unit provides articles generated by the Generation unit. The Provider unit provides articles, for example, through websites and mobile apps. Specifically, generated articles are posted on websites, making them accessible to users. Websites are designed to allow users to efficiently search and view articles of interest, and generated articles are organized and displayed by category and topic. The Provider unit can also provide articles through mobile apps. Mobile apps are designed to allow users to view articles using smartphones and tablets, and generated articles are delivered to users via push notifications and in-app notifications. This allows the Provider unit to provide users with an environment where they can view the latest news articles anytime, anywhere. Furthermore, the Provider unit can also notify users of generated articles. For example, articles can be delivered via email or push notifications. Email notifications involve sending a link to the generated article to the user's registered email address, allowing the user to view the article by opening the email and clicking the link. Push notifications send real-time notifications to smartphones and tablets, allowing users to view the article by tapping the notification. The Provider unit can also share articles through social media. Social media is a platform for users to share articles with other users, and by posting generated articles on social media, the service provider can reach a wider audience. This allows the service provider to deliver generated articles to users in diverse ways, improving the overall usability of the system.

[0073] The interactive unit can generate interactive content. For example, it can generate quizzes and surveys that involve the user. The interactive unit can use AI to generate interactive content based on the user's interests. For example, it can analyze the user's past behavior data and generate quizzes based on topics of interest. The interactive unit can also analyze the user's real-time responses and dynamically adjust the interactive content. For example, it can analyze how the user answered a quiz and adjust the next question. This allows the interactive unit to generate interactive content that promotes user engagement. Some or all of the above processes in the interactive unit may be performed using AI or not.

[0074] The data collection unit can collect news data in real time. For example, the data collection unit collects news data in real time from news sites and social media on the internet. By collecting news data in real time, the data collection unit can quickly obtain the latest information. For example, the data collection unit can collect news data in real time using RSS feeds from news sites. The data collection unit can also collect data in real time from news sites using web scraping technology. The data collection unit can also collect news data that users have shown interest in using social media APIs. As a result, the data collection unit can quickly obtain the latest information by collecting news data in real time. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0075] The analysis department can analyze collected data using AI to understand trends and relationships. For example, the analysis department can analyze news data using natural language processing technology to understand trends and relationships. The analysis department can classify the content of news data using AI and extract important information. For example, the analysis department can classify the topics of news data using machine learning algorithms. The analysis department can also perform sentiment analysis of news data using deep learning technology. This allows the analysis department to quickly grasp trends and relationships through AI analysis. Some or all of the above processes in the analysis department may be performed using AI or not.

[0076] The generation unit can generate personalized articles based on the user's past interests and behavior. For example, the generation unit analyzes the user's past interests and behavior data and generates personalized articles. The generation unit can automatically generate article content using AI. For example, the generation unit generates personalized articles using natural language generation technology. The generation unit can also generate personalized articles using template-based generation technology. This allows the generation unit to generate personalized articles based on the user's interests and behavior. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0077] The distribution unit can provide the generated articles to users. The distribution unit can provide articles, for example, through a website or mobile app. The distribution unit can also notify users of the generated articles. The distribution unit can provide articles, for example, using email or push notifications. The distribution unit can also share articles through social media. This allows the distribution unit to quickly provide the generated articles to users. Some or all of the above processes in the distribution unit may be performed using AI or not.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of news data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect news data when the user is relaxed. If the user is excited, the data collection unit can also advance the collection timing to collect news data in real time. If the user is tired, the data collection unit can adjust the collection timing to collect news data while the user is resting. In this way, the data collection unit can collect news data at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0079] The data collection unit can analyze a user's past news browsing history and select the optimal collection method. For example, the data collection unit can prioritize collecting news categories that the user has frequently viewed in the past. If a user tends to browse during specific time periods, the data collection unit can also collect news data according to those times. The data collection unit can also prioritize collecting data from news sources that the user has previously rated highly. This enables efficient news data collection by allowing the data collection unit to select the optimal collection method based on the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0080] The data collection unit can filter news data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting news data related to topics the user is currently interested in. The data collection unit can also filter relevant news data based on the user's search history for specific keywords. The data collection unit can also collect relevant news data based on the content of posts from social media accounts the user follows. This allows the data collection unit to collect highly relevant data by filtering news data 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.

[0081] The data collection unit can estimate the user's emotions and determine the priority of news data to collect based on the estimated emotions. For example, if the user is sad, the data collection unit will prioritize collecting positive news. If the user is excited, the data collection unit may also prioritize collecting the latest breaking news. If the user is relaxed, the data collection unit may also prioritize collecting interesting feature articles. In this way, the data collection unit can collect more relevant data by prioritizing news data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0082] The data collection unit can prioritize collecting highly relevant data by considering the user's geographical location when collecting news data. For example, the data collection unit can prioritize collecting local news related to the area where the user is currently located. If the user is traveling, the data collection unit can also prioritize collecting news related to their travel destination. If the user has shown interest in a particular region, the data collection unit can also prioritize collecting news related to that region. In this way, the data collection unit can efficiently collect region-related news by collecting highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0083] The data collection unit can analyze users' social media activity and collect relevant data when collecting news data. For example, the data collection unit can collect data related to news that users have shared on social media. The data collection unit can also collect relevant news data based on the content of posts from accounts that users follow. The data collection unit can also collect data related to news that users have "liked" on social media. In this way, the data collection unit can collect news data that matches the user's interests by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is sad, the analysis unit will present the analysis results using a lot of positive language. If the user is excited, the analysis unit can also present the analysis results using detailed data and graphs. If the user is relaxed, the analysis unit can also present the analysis results using concise and easy-to-understand language. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0085] The analysis unit can adjust the level of detail in its analysis based on the importance of the news data. For example, the analysis unit can perform a detailed analysis on important news data, a concise analysis on general news data, and a rapid analysis on urgent news data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the news data. Some or all of the above processes in the analysis unit may be performed using AI, or they may not.

[0086] The analysis unit can apply different analysis algorithms depending on the category of news data during analysis. For example, for economic news, the analysis unit can apply an analysis algorithm that uses economic indicators. For sports news, the analysis unit can also apply an algorithm that analyzes match results and player performance. For political news, the analysis unit can also apply an algorithm that analyzes policies and election results. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of news data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0088] The analysis department can determine the priority of analysis based on when the news data was collected. For example, the analysis department will prioritize the analysis of the most recent news data. The analysis department can also analyze past news data as needed. The analysis department can also prioritize the analysis of news data with the highest urgency. This allows the analysis department to perform efficient analysis by determining the priority of analysis based on when the news data was collected. Some or all of the above processes in the analysis department may be performed using AI, or they may not.

[0089] The analysis unit can adjust the order of analysis based on the relevance of news data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant news data. The analysis unit may also postpone analyzing less relevant news data. The analysis unit may also prioritize analyzing highly relevant news data based on user interests. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of news data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0090] The generation unit can estimate the user's emotions and adjust the article generation method based on the estimated user emotions. For example, if the user is sad, the generation unit will generate an article with a lot of positive content. If the user is excited, the generation unit can also generate an article with detailed data and graphs. If the user is relaxed, the generation unit can also generate a concise and easy-to-understand article. In this way, the generation unit can generate more appropriate articles by adjusting the article generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0091] The generation unit can adjust the level of detail in an article based on the importance of the news data during article generation. For example, the generation unit can generate a detailed article for important news data. The generation unit can also generate a concise article for general news data. The generation unit can also generate an article quickly for urgent news data. This allows the generation unit to efficiently generate articles by adjusting the level of detail based on the importance of the news data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0092] The generation unit can apply different generation algorithms depending on the category of news data when generating articles. For example, for economic news, the generation unit can apply a generation algorithm that uses economic indicators. For sports news, the generation unit can also apply an algorithm that generates match results and player performance. For political news, the generation unit can also apply an algorithm that generates policies and election results. By applying different generation algorithms depending on the category of news data, the generation unit can generate more accurate articles. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI.

[0093] The generation unit can estimate the user's emotions and adjust the length of the article based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise article. If the user is relaxed, the generation unit can also generate a detailed article. If the user is excited, the generation unit can also generate an article with visually stimulating effects. In this way, the generation unit can provide more appropriate articles by adjusting the length of the article according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0094] The generation unit can determine the priority of articles based on when the news data was collected during article generation. For example, the generation unit will prioritize generating articles for the latest news data. The generation unit can also generate articles for past news data as needed. The generation unit can also prioritize generating articles for news data of high urgency. This allows the generation unit to efficiently generate articles by determining the priority of articles based on when the news data was collected. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI.

[0095] The generation unit can adjust the order of articles based on the relevance of the news data when generating articles. For example, the generation unit can prioritize generating articles based on highly relevant news data. The generation unit can also postpone generating articles based on less relevant news data. The generation unit can also prioritize generating articles based on highly relevant news data based on user interests. This enables efficient article generation by adjusting the order of articles based on the relevance of the news data. Some or all of the above processing in the generation unit may be performed using AI or not.

[0096] The service provider can estimate the user's emotions and adjust how articles are delivered based on those estimated emotions. For example, if the user is sad, the service provider will provide articles that contain a lot of positive content. If the user is excited, the service provider may also provide articles with detailed data and graphs. If the user is relaxed, the service provider may also provide concise and easy-to-understand articles. In this way, the service provider can provide more appropriate articles by adjusting how articles are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI.

[0097] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when delivering articles. For example, the content delivery unit can prioritize delivering news categories that the user has frequently viewed in the past. If the content delivery unit has a tendency to browse at a specific time of day, it can also deliver articles according to that time. The content delivery unit can also prioritize delivering articles from news sources that the user has previously given high ratings to. In this way, the content delivery unit can efficiently deliver articles by selecting the optimal delivery method based on the user's past browsing history. Some or all of the above processing in the content delivery unit may be performed using AI or not.

[0098] The content provider can customize the content offered based on the user's current areas of interest when providing articles. For example, the provider may prioritize providing articles related to topics the user is currently interested in. The provider may also provide relevant articles based on the user's search history for specific keywords. The provider may also provide relevant articles based on the content of social media accounts the user follows. This allows the provider to provide more relevant articles by customizing the content based on the user's current areas of interest. Some or all of the above processing in the content provider may be performed using AI or not.

[0099] The content delivery unit can estimate the user's emotions and adjust the order in which articles are delivered based on the estimated emotions. For example, if the user is sad, the content delivery unit will prioritize delivering articles with positive content. If the user is excited, the content delivery unit may also prioritize delivering the latest breaking news articles. If the user is relaxed, the content delivery unit may also prioritize delivering interesting feature articles. In this way, the content delivery unit can provide more appropriate articles by adjusting the order in which articles are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the content delivery unit may be performed using AI or not using AI.

[0100] The news delivery system can select the optimal delivery method when providing articles, taking into account the user's geographical location. For example, the system can prioritize providing local news relevant to the user's current location. If the user is traveling, the system can also prioritize providing news relevant to their travel destination. If the user has expressed interest in a particular region, the system can also prioritize providing news relevant to that region. This allows the system to efficiently provide region-related news by selecting the optimal delivery method, taking into account the user's geographical location. Some or all of the above processing in the news delivery system may be performed using AI, or it may not be performed using AI.

[0101] The content provider can analyze the user's social media activity and customize the content provided when delivering articles. For example, the content provider can provide articles related to news that the user has shared on social media. The content provider can also provide relevant articles based on the content of posts from accounts that the user follows. The content provider can also provide articles related to news that the user has "liked" on social media. In this way, the content provider can provide more appropriate articles by analyzing the user's social media activity and customizing the content provided. Some or all of the above processing in the content provider may be performed using AI or not.

[0102] The interactive unit can estimate the user's emotions and adjust how interactive content is generated based on the estimated emotions. For example, if the user is sad, the interactive unit will generate interactive content that contains a lot of positive content. If the user is excited, the interactive unit can also generate interactive content using detailed data and graphs. If the user is relaxed, the interactive unit can also generate concise and easy-to-understand interactive content. In this way, the interactive unit can provide more appropriate content by adjusting how interactive content is generated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI or not using AI.

[0103] The interactive unit can select the optimal generation method when generating interactive content by referring to the user's past operation history. For example, the interactive unit can prioritize generating interactive content in formats that the user has frequently interacted with in the past. The interactive unit can also generate relevant interactive content based on the user's history of performing specific operations. The interactive unit can also prioritize generating interactive content in formats that the user has previously given high ratings to. This enables efficient content generation by allowing the interactive unit to select the optimal generation method based on the user's past operation history. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0104] The interactive unit can customize the generated content based on the user's current areas of interest when generating interactive content. For example, the interactive unit can generate interactive content related to topics the user is currently interested in. The interactive unit can also generate relevant interactive content based on the user's search history for specific keywords. The interactive unit can also generate relevant interactive content based on the content of social media accounts the user follows. This allows the interactive unit to provide more relevant content by customizing the generated content based on the user's current areas of interest. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0105] The interactive section can estimate the user's emotions and adjust the order in which interactive content is provided based on the estimated emotions. For example, if the user is sad, the interactive section will prioritize providing positive interactive content. If the user is excited, the interactive section may also prioritize providing the latest breaking news interactive content. If the user is relaxed, the interactive section may also prioritize providing interesting featured interactive content. In this way, the interactive section can provide more appropriate content by adjusting the order in which interactive content is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive section may be performed using AI or not using AI.

[0106] The interactive unit can select the optimal generation method when generating interactive content, taking into account the user's geographical location. For example, the interactive unit can prioritize generating local interactive content related to the user's current location. If the user is traveling, the interactive unit can also prioritize generating interactive content related to their travel destination. If the user has shown interest in a particular region, the interactive unit can also prioritize generating interactive content related to that region. In this way, the interactive unit can efficiently generate region-related content by selecting the optimal generation method, taking into account the user's geographical location. Some or all of the above processing in the interactive unit may be performed using AI, or it may be performed without using AI.

[0107] The interactive unit can analyze the user's social media activity and customize the generated content when creating interactive content. For example, the interactive unit can generate interactive content related to news that the user has shared on social media. The interactive unit can also generate relevant interactive content based on posts from accounts that the user follows. The interactive unit can also generate interactive content related to news that the user has "liked" on social media. This allows the interactive unit to provide more appropriate content by analyzing the user's social media activity and customizing the generated content. Some or all of the above processing in the interactive unit may be performed using AI or not.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] A news agent system can estimate a user's emotions and adjust the tone of the article based on those emotions. For example, if a user is sad, the article's tone can be made positive to improve their mood. If a user is excited, the article's tone can be made calm to allow them to receive the information calmly. Furthermore, if a user is relaxed, the article's tone can be made lighthearted to make it easier to read. In this way, the news agent system can provide the most suitable article for the user's emotions.

[0110] A news agent system can analyze a user's past news browsing history and determine the optimal timing for delivering articles. For example, if a user frequently browsed the news during a specific time period in the past, articles can be delivered accordingly. Similarly, if a user browsed the news frequently on a particular day of the week, articles can be delivered accordingly. Furthermore, if a user browsed the news frequently before and after a specific event, articles can be delivered to coincide with that event. In this way, the news agent system can deliver articles at the optimal time based on the user's past behavior.

[0111] A news agent system can estimate a user's emotions and adjust the content of articles based on those emotions. For example, if a user is stressed, it can provide articles that promote relaxation. If a user is excited, it can provide articles that calm their excitement. Furthermore, if a user is sad, it can provide articles that cheer them up. In this way, the news agent system can provide articles that are optimally suited to the user's emotions.

[0112] A news agent system can prioritize local news by taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize news related to that region. Similarly, if a user is traveling, it can provide news related to their destination. Furthermore, if a user has expressed interest in a particular region, it can provide news related to that region. This allows the news agent system to provide more relevant news based on the user's geographical location.

[0113] The news agent system can estimate the user's emotions and adjust how articles are displayed based on those emotions. For example, if the user is sad, positive news can be displayed more prominently. If the user is excited, calm news can be displayed more prominently. Furthermore, if the user is relaxed, lighthearted news can be displayed more prominently. In this way, the news agent system can deliver news in the most appropriate way according to the user's emotions.

[0114] A news agent system can analyze a user's social media activity and provide relevant news. For example, it can provide articles related to news that a user has shared on social media. It can also provide relevant news based on the content of posts from accounts that a user follows. Furthermore, it can provide articles related to news that a user has "liked" on social media. In this way, the news agent system can provide more relevant news based on the user's social media activity.

[0115] A news agent system can estimate a user's emotions and adjust how articles are delivered based on those emotions. For example, if a user is sad, positive news can be delivered to improve their mood. If a user is excited, calming news can be delivered to soothe their excitement. Furthermore, if a user is relaxed, upbeat news can be delivered to maintain their relaxation. In this way, the news agent system can deliver news in the most appropriate way according to the user's emotions.

[0116] A news agent system can analyze a user's past news rating history and select the most suitable news sources. For example, it can prioritize providing articles from news sources that the user has previously given high ratings to. It can also avoid providing articles from news sources that the user has previously given low ratings to. Furthermore, if a user frequently views a particular news source, it can prioritize providing articles from that source. In this way, the news agent system can provide more satisfying news based on the user's past rating history.

[0117] A news agent system can estimate a user's emotions and provide feedback on articles based on those emotions. For example, if a user is sad, it can provide a function that encourages positive feedback. If a user is excited, it can provide a function that encourages calm feedback. Furthermore, if a user is relaxed, it can provide a function that encourages lighthearted feedback. In this way, the news agent system can provide optimal feedback tailored to the user's emotions.

[0118] A news agent system can prioritize providing relevant news based on a user's current areas of interest. For example, it can prioritize news related to topics the user is currently interested in. It can also provide relevant news based on the user's search history for specific keywords. Furthermore, it can provide relevant news based on the content of posts from social media accounts the user follows. This allows the news agent system to provide more relevant news based on the user's current areas of interest.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects news data. The collection unit can collect news data from news sites and social media on the internet. The collection unit can also collect news data in real time, for example, by using RSS feeds from news sites or by using web scraping techniques to collect data from news sites. It can also use social media APIs to collect news data that users have shown interest in. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze news data and understand trends and relationships. AI can be used to classify the content of news data and extract important information. For example, machine learning algorithms can be used to classify topics in news data, or deep learning technology can be used to perform sentiment analysis on news data. Step 3: The generation unit generates articles based on the analysis results obtained by the analysis unit. The generation unit generates personalized articles based on the user's past interests and behavior. Article content can be automatically generated using AI, for example, by using natural language generation technology or template-based generation technology. Step 4: The provider delivers the articles generated by the generator. The provider delivers the articles via a website or mobile app. It can also notify users of the generated articles, for example, by using email or push notifications. It can also share the articles through social media.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and interactive unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects news data from news sites and social media on the internet via the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected news data to understand trends and relevances. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates personalized articles based on the user's interests. The provision unit is implemented by the control unit 46A of the smart device 14, which notifies the user of the generated articles. The interactive unit is implemented by the control unit 46A of the smart device 14, which generates interactive content that promotes user engagement. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and interactive unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects news data from news sites and social media on the internet via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected news data to understand trends and relevance. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates personalized articles based on the user's interests. The provision unit is implemented by the control unit 46A of the smart glasses 214, which notifies the user of the generated articles. The interactive unit is implemented by the control unit 46A of the smart glasses 214, which generates interactive content that promotes user engagement. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and interactive unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects news data from news sites and social media on the internet via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected news data to understand trends and relevances. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates personalized articles based on the user's interests. The provision unit is implemented by the control unit 46A of the headset terminal 314, which notifies the user of the generated articles. The interactive unit is implemented by the control unit 46A of the headset terminal 314, which generates interactive content that promotes user engagement. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and interactive unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects news data from news sites and social media on the internet via the communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected news data to understand trends and relevances. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates personalized articles based on the user's interests. The provision unit is implemented by the control unit 46A of the robot 414, which notifies the user of the generated articles. The interactive unit is implemented by the control unit 46A of the robot 414, which generates interactive content that promotes user engagement. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The data collection department collects news data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an article based on the analysis results obtained by the analysis unit, A providing unit that provides articles generated by the generation unit, A system equipped with these features. (Note 2) It features an interactive section for generating interactive content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect news data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected data is analyzed using AI to identify trends and correlations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate personalized articles based on the user's past interests and behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated articles to the users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of news data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past news browsing history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting news data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of news data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting news data, the system prioritizes collecting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting news data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the news data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user sentiment and adjust the article generation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating an article, adjust the level of detail based on the importance of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating articles, different generation algorithms are applied depending on the category of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's sentiment and adjusts the article length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating articles, the priority of articles is determined based on when the news data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating articles, the order of articles is adjusted based on the relevance of the news data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate user sentiment and adjust how articles are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing articles, the system selects the most suitable delivery method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing articles, the content will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates user sentiment and adjusts the order in which articles are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing articles, the optimal delivery method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing articles, we analyze users' social media activity to customize the content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned interactive unit is It estimates the user's emotions and adjusts how interactive content is generated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned interactive unit is When generating interactive content, the system selects the optimal generation method by referring to the user's past interaction history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned interactive unit is When generating interactive content, customize the generated content based on the user's current areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned interactive unit is It estimates the user's emotions and adjusts the order in which interactive content is delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned interactive unit is When generating interactive content, the optimal generation method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned interactive unit is When generating interactive content, analyze users' social media activity to customize the generated content. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0193] 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 data collection department collects news data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an article based on the analysis results obtained by the analysis unit, A providing unit that provides articles generated by the generation unit, A system equipped with these features.

2. It features an interactive section for generating interactive content. The system according to feature 1.

3. The aforementioned collection unit is Collect news data in real time. The system according to feature 1.

4. The aforementioned analysis unit is The collected data is analyzed using AI to identify trends and correlations. The system according to feature 1.

5. The generating unit is Generate personalized articles based on the user's past interests and behavior. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated articles to the users. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of news data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past news browsing history and select the appropriate data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting news data, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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