system

Abstract

The system according to this embodiment aims to provide users with personalized, up-to-date news and commentary. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, a collection unit, and an update unit. The reception unit receives questions about news articles. The analysis unit analyzes news articles based on the questions entered by the reception unit. The provision unit provides details of the news articles analyzed by the analysis unit. The collection unit collects information about ongoing events. The update unit updates the information collected by the collection unit in chronological order.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the amount of news information is large and it is difficult for users to find important information.

[0005] The system according to the embodiment aims to provide personalized latest news and explanations to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a collection unit, and an update unit. The reception unit receives questions about news articles. The analysis unit analyzes news articles based on the questions entered by the reception unit. The provision unit provides details of the news articles analyzed by the analysis unit. The collection unit collects information about ongoing events. The update unit updates the information collected by the collection unit in chronological order. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with personalized, up-to-date news and commentary. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The news distribution and commentary system according to an embodiment of the present invention is a system that utilizes AI to automatically deliver personalized, up-to-date news and commentary to users, thereby promoting their understanding of the news. This news distribution and commentary system provides a function that allows users to ask questions about the news. Next, it utilizes generative AI to delve deeper into the details of the news article and provide commentary to the user. Furthermore, it provides information about ongoing events in chronological order and tracks the subsequent developments of the news. This mechanism allows users to always stay informed about the latest developments. For example, by asking questions about the news, users can understand the details of the news. For example, if a user asks, "What is the background of this news?", the generative AI analyzes the details of the news article and provides background information. This allows the user to deeply understand the content of the news. Next, it utilizes generative AI to delve deeper into the details of the news article and provide commentary to the user. For example, the generative AI provides detailed explanations of specialized terms and background information that appear in the news article. This allows the user to understand the content of the news more deeply. Furthermore, it provides information about ongoing events in chronological order and tracks the subsequent developments of the news. For example, if an incident occurs, the progress of that incident is updated chronologically and provided to the user. This allows the user to always stay informed about the latest developments. This allows users to understand news details and stay up-to-date on ongoing events. This facilitates news comprehension and streamlines user information gathering. As a result, news distribution and commentary systems can automatically deliver personalized, up-to-date news and commentary to users, further enhancing news understanding.

[0029] The news distribution and commentary system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a collection unit, and an update unit. The reception unit has a function for users to input questions about the news. The reception unit can accept questions in text format, for example. The reception unit can also accept questions in voice format. For example, users can input questions by voice using a microphone. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the acceptance of questions can be temporarily delayed to give them time to relax. The analysis unit uses generative AI to analyze the news article based on the questions entered by the reception unit. The analysis unit can use natural language processing technology, for example, to analyze the news article and provide a detailed commentary. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the news article. For example, a detailed analysis can be performed on news articles of high importance. The provision unit uses generative AI to provide details of the news article analyzed by the analysis unit. The provision unit can provide detailed commentary in text format, for example. Furthermore, the information provider can also provide detailed explanations in graph format. The information provider collects information about ongoing events. The information provider can collect information from, for example, news sites on the internet. The information provider can also collect information from social media. The information provider updates the information collected by the information provider in chronological order. The information provider can update, for example, the latest information on ongoing events in chronological order. The information provider can also update the progress of past events in chronological order. As a result, the news distribution and commentary system according to the embodiment can automatically deliver personalized, up-to-date news and commentary to users, thereby promoting their understanding of the news.

[0030] The reception desk includes a function for users to input questions about the news. For example, the reception desk can accept questions in text format. Users can input questions using a keyboard and send them to the system. The reception desk can also accept questions in voice format. For example, users can input questions by voice using a microphone. Speech recognition technology is used to convert the voice into text in a format the system can understand. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if a user is feeling stressed, the system can temporarily delay question acceptance to give them time to relax. Emotion estimation uses technology that analyzes factors such as voice tone and speed, and text content. This allows the system to grasp the user's emotional state in real time and respond appropriately. Additionally, the reception desk can learn the user's past question history and behavioral patterns to provide more personalized responses. For example, users who have previously shown interest in a particular news category can receive priority questions related to that category. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0031] The analysis unit uses generative AI to analyze news articles based on questions entered by the reception unit. For example, the analysis unit can use natural language processing technology to analyze news articles and provide detailed explanations. Specifically, the generative AI understands the content of the question, searches for relevant news articles, and extracts important information. For example, if a user asks, "Please tell me about recent economic news," the generative AI will analyze the latest economic news articles, extract key points, and generate an explanation. The analysis unit can also adjust the level of detail of the analysis based on the importance of the news article. For example, it can perform a more detailed analysis on news articles of high importance. Factors such as the number of views, social media shares, and expert ratings are considered when evaluating importance. Furthermore, the analysis unit can refer to past news articles and related data to provide deeper insights. For example, if asked about fluctuations in a specific economic indicator, it can perform trend analysis and predictions based on past data. This allows the analysis unit to provide quick and accurate explanations to users' questions, deepening their understanding of the news.

[0032] The information provider uses a generative AI to provide detailed information about news articles analyzed by the analysis unit. For example, the information provider can provide detailed explanations in text format. In response to a user's question, the explanation generated by the generative AI is displayed in text format. The information provider can also provide detailed explanations in graph format. For example, in response to a question about economic news, it displays graphs and charts of relevant economic indicators, providing information in a visually easy-to-understand format. Furthermore, the information provider can also provide explanations in audio format. If a user asks a question by voice, the explanation generated by the generative AI is provided in audio format using speech synthesis technology. This allows users to understand the news using not only visual information but also auditory information. The information provider can customize the format and level of detail of the explanation according to the user's preferences. For example, it can provide deeper insights to users who prefer detailed explanations, and short, concise explanations to users who prefer concise explanations. In this way, the information provider can provide flexible information tailored to the user's needs and promote understanding of the news.

[0033] The data collection unit gathers information about ongoing events. For example, it can collect information from news sites on the internet. Using web scraping technology, the data collection unit automatically retrieves the latest articles from news sites and stores them in a database. The data collection unit can also collect information from social media. For example, it collects trending information and user posts from social media platforms, performing real-time information gathering. Furthermore, the data collection unit can obtain information directly from news providers using RSS feeds and APIs. This allows the data collection unit to gather the latest news information from diverse sources and maintain the freshness of information throughout the entire system. The data collection unit centrally manages the collected information and makes it accessible to the analysis and provisioning units. This allows the data collection unit to collect information efficiently and effectively, improving the overall performance of the system.

[0034] The update unit updates the information collected by the collection unit in chronological order. For example, the update unit can update the latest information on ongoing events in chronological order. Specifically, it organizes collected news articles and social media posts in chronological order and provides them to users. The update unit can also update the progress of past events in chronological order. For example, it can track the progress of a particular incident or project and add the latest information. This makes it easier for users to understand the flow of events from the past to the present. Furthermore, the update unit can evaluate the reliability of information and prioritize updating reliable information. For example, it can prioritize incorporating information from official news providers and experts and filter out unreliable information. In this way, the update unit can provide users with reliable and up-to-date information and facilitate their understanding of the news.

[0035] The reception desk can analyze a user's past question history and select the most suitable method for receiving questions. For example, the reception desk can prioritize receiving questions on topics that the user has frequently asked about in the past. It can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception desk can suggest the most suitable method for receiving questions at a specific time of day based on the user's past question history. This improves user convenience by selecting the most suitable method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the most suitable method for receiving questions.

[0036] The reception unit can filter questions based on the user's current areas of interest when they are received. For example, the reception unit can filter questions based on the news categories the user is currently interested in. The reception unit can also analyze the user's social media activity and prioritize receiving relevant questions. Furthermore, the reception unit can suggest questions related to the user's areas of interest by referring to the user's past search history. This allows the reception unit to prioritize receiving highly relevant questions by filtering them based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI identify areas of interest and filter the question content.

[0037] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving questions about news related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions about news related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving questions about local news. This allows the reception desk to provide users with useful information by prioritizing highly relevant questions based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's GPS data into a generating AI and have the generating AI identify highly relevant questions.

[0038] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to news that the user has shared on social media. It can also filter questions based on the content of posts from accounts that the user follows. Furthermore, the reception desk can prioritize questions related to topics in groups and communities that the user participates in. This allows the reception desk to provide information tailored to the user's interests by accepting relevant questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI identify relevant questions.

[0039] The analysis unit can adjust the level of detail of its analysis based on the importance of the news articles. For example, it can perform a detailed analysis on news articles of high importance, and a concise analysis on news articles of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the news articles. This allows for detailed analysis of important news by adjusting the level of detail according to the importance of the news articles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the news article during analysis. For example, the analysis unit can apply a specialized political analysis algorithm to political news. It can also apply a specialized economic analysis algorithm to economic news. Furthermore, it can apply a specialized sports analysis algorithm to sports news. By applying the appropriate analysis algorithm according to the category of the news article, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the publication date of news articles during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent news articles. It can also postpone the analysis of older news articles. Furthermore, the analysis unit can adjust the priority of analysis according to the publication date of the news articles. This allows for the prioritization of analysis of the most recent news by determining the priority of analysis based on the publication date of the news articles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article publication date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of news articles during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant news articles. It can also postpone the analysis of less relevant news articles. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the news articles. This allows for the prioritization of highly relevant news by adjusting the order of analysis based on the relevance of the news articles. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The service provider can adjust the level of detail in the explanations based on the importance of the news articles when providing them. For example, the service provider can provide detailed explanations for news articles of high importance. Conversely, the service provider can provide concise explanations for news articles of low importance. The service provider can also determine the priority of the explanations according to the importance of the news articles. This allows for detailed explanations to be provided for important news by adjusting the level of detail in the explanations according to the importance of the news articles. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanations.

[0044] The service provider can apply different commentary algorithms depending on the category of the news article at the time of delivery. For example, the service provider can apply a specialized political commentary algorithm to political news. It can also apply a specialized economic commentary algorithm to economic news. Furthermore, it can apply a specialized sports commentary algorithm to sports news. By applying the appropriate commentary algorithm according to the category of the news article, it is possible to provide more accurate commentary. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input news article category data into a generating AI and have the generating AI select an appropriate commentary algorithm.

[0045] The information provider can determine the priority of commentary based on the publication date of the news articles at the time of provision. For example, the information provider can prioritize commenting on the latest news articles. It can also postpone commenting on older news articles. Furthermore, the information provider can adjust the priority of commentary according to the publication date of the news articles. This allows for prioritizing commentary on the latest news by determining the priority of commentary based on the publication date of the news articles. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input news article publication date data into a generating AI and have the generating AI determine the priority of commentary.

[0046] The content provider can adjust the order of explanations based on the relevance of the news articles at the time of delivery. For example, the content provider can prioritize explaining highly relevant news articles. It can also postpone explaining less relevant news articles. Furthermore, the content provider can adjust the order of explanations according to the relevance of the news articles. This allows for prioritizing the explanation of highly relevant news by adjusting the order of explanations based on the relevance of the news articles. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the content provider can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the explanation order.

[0047] The data collection unit can adjust the level of detail collected based on the importance of ongoing events. For example, the data collection unit can collect detailed information for high-importance events. It can also collect concise information for low-importance events. Furthermore, the data collection unit can determine the priority of collection according to the importance of events. This allows for the collection of detailed information for important events by adjusting the level of detail according to the importance of ongoing events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of collection.

[0048] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of ongoing events during data collection. For example, if an ongoing event is close to the user's current location, the data collection unit can prioritize the collection of that information. The data collection unit can also prioritize the collection of information if an ongoing event is related to the user's area of ​​interest. Furthermore, the data collection unit can prioritize the collection of information if an ongoing event is related to the user's past travel history. This allows the data collection unit to provide useful information to the user by prioritizing the collection of highly relevant information based on the geographical location information of ongoing events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data of ongoing events into a generating AI and have the generating AI identify highly relevant information.

[0049] The update unit can adjust the level of detail of the update based on the importance of the collected information during the update process. For example, the update unit can perform detailed updates on information of high importance. Conversely, it can perform concise updates on information of low importance. The update unit can also determine the priority of updates according to the importance of the information. This allows for detailed updates to be performed on important information by adjusting the level of detail of the update according to the importance of the collected information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the update.

[0050] The update unit can prioritize updating highly relevant information by considering the geographical location information of the collected information during the update process. For example, if the collected information is related to the user's current location, the update unit can prioritize updating that information. It can also prioritize updating information related to the user's areas of interest. Furthermore, if the collected information is related to the user's past travel history, the update unit can prioritize updating that information. This allows the update unit to provide users with useful information by prioritizing the updating of highly relevant information based on the geographical location information of the collected information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the geographical location data of the collected information into a generating AI and have the generating AI identify highly relevant information.

[0051] The update unit can update collected information in chronological order. For example, the update unit can update the latest information on ongoing events in chronological order. It can also update the progress of past events in chronological order. Furthermore, the update unit can update predicted events in chronological order based on future plans. In this way, by updating collected information in chronological order, it is possible to provide the latest information on ongoing events. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input collected information into a generating AI and have the generating AI perform chronological updates.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The news distribution and commentary system can further adjust how news is delivered by taking into account the user's learning history. For example, if a user has previously viewed a lot of news on a particular topic, the system can prioritize providing news related to that topic. It can also avoid news formats that the user previously found difficult to understand and instead provide news in a more easily understandable format. Furthermore, it can refer to news formats that the user has previously rated highly and provide news in a similar format. By adjusting the news delivery method based on the user's learning history, the system can promote user comprehension and improve news acceptance.

[0054] The news distribution and commentary system can further adjust how news is delivered based on the user's geographical location. For example, if a user is in a specific region, news related to that region can be prioritized. Similarly, if a user is traveling, news related to their destination can be provided. Furthermore, if a user is at home, local news can be prioritized. By tailoring news delivery based on the user's geographical location, the system can provide users with more relevant information.

[0055] The news distribution and commentary system can further analyze users' social media activity and provide relevant news. For example, it can prioritize news related to news that users have shared on social media. It can also filter news based on the content of posts from accounts that users follow. Furthermore, it can prioritize news related to topics in groups and communities that users participate in. In this way, by providing relevant news based on users' social media activity, it can provide information tailored to users' interests.

[0056] The news distribution and commentary system can further analyze a user's past news browsing history and prioritize providing relevant news. For example, it can prioritize news related to news categories that the user has frequently viewed in the past. It can also provide news related to news that the user has previously rated highly. Furthermore, it can suggest relevant news based on the topics of news the user has previously viewed. In this way, by providing relevant news based on the user's past news browsing history, it can provide information tailored to the user's interests.

[0057] The news distribution and commentary system can further adjust how news is delivered based on the user's learning style. For example, if the user is a visual learner, the system can provide news that makes extensive use of graphs and charts. If the user is an auditory learner, the system can provide news with audio commentary. Furthermore, if the user is an experiential learner, the system can provide interactive news content. By adjusting the news delivery method based on the user's learning style, the system can provide news that is easy for the user to understand.

[0058] The news distribution and commentary system can further analyze a user's past news rating history and prioritize providing relevant news. For example, it can prioritize news related to news categories that the user has previously rated highly. It can also avoid news categories that the user has previously rated poorly. Furthermore, it can suggest relevant news based on the topics of news the user has previously rated. In this way, by providing relevant news based on the user's past news rating history, it can provide information tailored to the user's interests.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk will have a function for users to enter questions about the news. The reception desk can accept questions in text or voice format. For example, users can enter questions by voice using a microphone. The reception desk can also estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions of the user. For example, if the user is feeling stressed, the acceptance of questions can be temporarily delayed to give them time to relax. Step 2: The analysis unit uses a generation AI to analyze news articles based on the questions entered by the reception unit. The analysis unit can analyze news articles using natural language processing technology and provide detailed explanations. It can also adjust the level of detail of the analysis based on the importance of the news article. For example, a detailed analysis can be performed on news articles of high importance. Step 3: The provisioning unit uses the generation AI to provide details of the news article analyzed by the analysis unit. The provisioning unit can provide detailed explanations in text or graph format. Step 4: The information gathering team collects information about ongoing events. The information gathering team can collect information from news sites and social media on the internet. Step 5: The update unit updates the information collected by the collection unit in chronological order. The update unit can update the latest information on ongoing events and the progress of past events in chronological order.

[0061] (Example of form 2) The news distribution and commentary system according to an embodiment of the present invention is a system that utilizes AI to automatically deliver personalized, up-to-date news and commentary to users, thereby promoting their understanding of the news. This news distribution and commentary system provides a function that allows users to ask questions about the news. Next, it utilizes generative AI to delve deeper into the details of the news article and provide commentary to the user. Furthermore, it provides information about ongoing events in chronological order and tracks the subsequent developments of the news. This mechanism allows users to always stay informed about the latest developments. For example, by asking questions about the news, users can understand the details of the news. For example, if a user asks, "What is the background of this news?", the generative AI analyzes the details of the news article and provides background information. This allows the user to deeply understand the content of the news. Next, it utilizes generative AI to delve deeper into the details of the news article and provide commentary to the user. For example, the generative AI provides detailed explanations of specialized terms and background information that appear in the news article. This allows the user to understand the content of the news more deeply. Furthermore, it provides information about ongoing events in chronological order and tracks the subsequent developments of the news. For example, if an incident occurs, the progress of that incident is updated chronologically and provided to the user. This allows the user to always stay informed about the latest developments. This allows users to understand news details and stay up-to-date on ongoing events. This facilitates news comprehension and streamlines user information gathering. As a result, news distribution and commentary systems can automatically deliver personalized, up-to-date news and commentary to users, further enhancing news understanding.

[0062] The news distribution and commentary system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a collection unit, and an update unit. The reception unit has a function for users to input questions about the news. The reception unit can accept questions in text format, for example. The reception unit can also accept questions in voice format. For example, users can input questions by voice using a microphone. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the acceptance of questions can be temporarily delayed to give them time to relax. The analysis unit uses generative AI to analyze the news article based on the questions entered by the reception unit. The analysis unit can use natural language processing technology, for example, to analyze the news article and provide a detailed commentary. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the news article. For example, a detailed analysis can be performed on news articles of high importance. The provision unit uses generative AI to provide details of the news article analyzed by the analysis unit. The provision unit can provide detailed commentary in text format, for example. Furthermore, the information provider can also provide detailed explanations in graph format. The information provider collects information about ongoing events. The information provider can collect information from, for example, news sites on the internet. The information provider can also collect information from social media. The information provider updates the information collected by the information provider in chronological order. The information provider can update, for example, the latest information on ongoing events in chronological order. The information provider can also update the progress of past events in chronological order. As a result, the news distribution and commentary system according to the embodiment can automatically deliver personalized, up-to-date news and commentary to users, thereby promoting their understanding of the news.

[0063] The reception desk includes a function for users to input questions about the news. For example, the reception desk can accept questions in text format. Users can input questions using a keyboard and send them to the system. The reception desk can also accept questions in voice format. For example, users can input questions by voice using a microphone. Speech recognition technology is used to convert the voice into text in a format the system can understand. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if a user is feeling stressed, the system can temporarily delay question acceptance to give them time to relax. Emotion estimation uses technology that analyzes factors such as voice tone and speed, and text content. This allows the system to grasp the user's emotional state in real time and respond appropriately. Additionally, the reception desk can learn the user's past question history and behavioral patterns to provide more personalized responses. For example, users who have previously shown interest in a particular news category can receive priority questions related to that category. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0064] The analysis unit uses generative AI to analyze news articles based on questions entered by the reception unit. For example, the analysis unit can use natural language processing technology to analyze news articles and provide detailed explanations. Specifically, the generative AI understands the content of the question, searches for relevant news articles, and extracts important information. For example, if a user asks, "Please tell me about recent economic news," the generative AI will analyze the latest economic news articles, extract key points, and generate an explanation. The analysis unit can also adjust the level of detail of the analysis based on the importance of the news article. For example, it can perform a more detailed analysis on news articles of high importance. Factors considered in evaluating importance include the number of views, social media shares, and expert ratings of the news article. Furthermore, the analysis unit can refer to past news articles and related data to provide deeper insights. For example, if asked about fluctuations in a specific economic indicator, it can perform trend analysis and predictions based on past data. This allows the analysis unit to provide quick and accurate explanations to users' questions, deepening their understanding of the news.

[0065] The information provider uses a generative AI to provide detailed information about news articles analyzed by the analysis unit. For example, the information provider can provide detailed explanations in text format. In response to a user's question, the explanation generated by the generative AI is displayed in text format. The information provider can also provide detailed explanations in graph format. For example, in response to a question about economic news, it displays graphs and charts of relevant economic indicators, providing information in a visually easy-to-understand format. Furthermore, the information provider can also provide explanations in audio format. If a user asks a question by voice, the explanation generated by the generative AI is provided in audio format using speech synthesis technology. This allows users to understand the news using not only visual information but also auditory information. The information provider can customize the format and level of detail of the explanation according to the user's preferences. For example, it can provide deeper insights to users who prefer detailed explanations, and short, concise explanations to users who prefer concise explanations. In this way, the information provider can provide flexible information tailored to the user's needs and promote understanding of the news.

[0066] The data collection unit gathers information about ongoing events. For example, it can collect information from news sites on the internet. Using web scraping technology, the data collection unit automatically retrieves the latest articles from news sites and stores them in a database. The data collection unit can also collect information from social media. For example, it collects trending information and user posts from social media platforms, performing real-time information gathering. Furthermore, the data collection unit can obtain information directly from news providers using RSS feeds and APIs. This allows the data collection unit to gather the latest news information from diverse sources and maintain the freshness of information throughout the entire system. The data collection unit centrally manages the collected information and makes it accessible to the analysis and provisioning units. This allows the data collection unit to collect information efficiently and effectively, improving the overall performance of the system.

[0067] The update unit updates the information collected by the collection unit in chronological order. For example, the update unit can update the latest information on ongoing events in chronological order. Specifically, it organizes collected news articles and social media posts in chronological order and provides them to users. The update unit can also update the progress of past events in chronological order. For example, it can track the progress of a particular incident or project and add the latest information. This makes it easier for users to understand the flow of events from the past to the present. Furthermore, the update unit can evaluate the reliability of information and prioritize updating reliable information. For example, it can prioritize incorporating information from official news providers and experts and filter out unreliable information. In this way, the update unit can provide users with reliable and up-to-date information and facilitate their understanding of the news.

[0068] The reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk can temporarily delay question acceptance to give the user time to relax. Conversely, if the user is agitated, the reception desk can accept questions immediately and respond quickly. Furthermore, if the user is tired, the reception desk can simplify the question acceptance process to complete it in a short time. This allows for question acceptance at a more appropriate time by adjusting the timing of question acceptance 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0069] The reception desk can analyze a user's past question history and select the most suitable method for receiving questions. For example, the reception desk can prioritize receiving questions on topics that the user has frequently asked about in the past. It can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception desk can suggest the most suitable method for receiving questions at a specific time of day based on the user's past question history. This improves user convenience by selecting the most suitable method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the most suitable method for receiving questions.

[0070] The reception unit can filter questions based on the user's current areas of interest when they are received. For example, the reception unit can filter questions based on the news categories the user is currently interested in. The reception unit can also analyze the user's social media activity and prioritize receiving relevant questions. Furthermore, the reception unit can suggest questions related to the user's areas of interest by referring to the user's past search history. This allows the reception unit to prioritize receiving highly relevant questions by filtering them based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI identify areas of interest and filter the question content.

[0071] The reception desk can estimate the user's emotions and determine the priority of questions to answer based on the estimated emotions. For example, if the user is nervous, the reception desk can prioritize high-priority questions. If the user is relaxed, the reception desk can prioritize detailed questions. If the user is in a hurry, the reception desk can prioritize concise questions. In this way, by prioritizing questions according to the user's emotions, important questions can be answered preferentially. 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 reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving questions about news related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions about news related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving questions about local news. This allows the reception desk to provide users with useful information by prioritizing highly relevant questions based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's GPS data into a generating AI and have the generating AI identify highly relevant questions.

[0073] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to news that the user has shared on social media. It can also filter questions based on the content of posts from accounts that the user follows. Furthermore, the reception desk can prioritize questions related to topics in groups and communities that the user participates in. This allows the reception desk to provide information tailored to the user's interests by accepting relevant questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI identify relevant questions.

[0074] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The analysis unit can adjust the level of detail of its analysis based on the importance of the news articles. For example, it can perform a detailed analysis on news articles of high importance, and a concise analysis on news articles of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the news articles. This allows for detailed analysis of important news by adjusting the level of detail according to the importance of the news articles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the news article during analysis. For example, the analysis unit can apply a specialized political analysis algorithm to political news. It can also apply a specialized economic analysis algorithm to economic news. Furthermore, it can apply a specialized sports analysis algorithm to sports news. By applying the appropriate analysis algorithm according to the category of the news article, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0077] 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 provide a detailed analysis. If the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's voice data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The analysis unit can determine the priority of analysis based on the publication date of news articles during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent news articles. It can also postpone the analysis of older news articles. Furthermore, the analysis unit can adjust the priority of analysis according to the publication date of the news articles. This allows for the prioritization of analysis of the most recent news by determining the priority of analysis based on the publication date of the news articles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article publication date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of news articles during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant news articles. It can also postpone the analysis of less relevant news articles. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the news articles. This allows for the prioritization of highly relevant news by adjusting the order of analysis based on the relevance of the news articles. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The service provider can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed explanation. If the user is in a hurry, the service provider can provide a concise explanation. If the user is excited, the service provider can provide a visually stimulating explanation. By adjusting the way the explanation is presented according to the user's emotions, the service provider can provide explanations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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, for example, or not using AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The service provider can adjust the level of detail in the explanations based on the importance of the news articles when providing them. For example, the service provider can provide detailed explanations for news articles of high importance. Conversely, the service provider can provide concise explanations for news articles of low importance. The service provider can also determine the priority of the explanations according to the importance of the news articles. This allows for detailed explanations to be provided for important news by adjusting the level of detail in the explanations according to the importance of the news articles. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input news article importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanations.

[0082] The service provider can apply different commentary algorithms depending on the category of the news article at the time of delivery. For example, the service provider can apply a specialized political commentary algorithm to political news. It can also apply a specialized economic commentary algorithm to economic news. Furthermore, it can apply a specialized sports commentary algorithm to sports news. By applying the appropriate commentary algorithm according to the category of the news article, it is possible to provide more accurate commentary. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input news article category data into a generating AI and have the generating AI select an appropriate commentary algorithm.

[0083] The service provider can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide a short, concise explanation. If the user is relaxed, the service provider can provide a detailed explanation. If the user is excited, the service provider can provide a visually stimulating explanation. By adjusting the length of the explanation according to the user's emotions, the service provider can provide an explanation of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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, for example, or not using AI. For example, the service provider can input the user's voice data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The information provider can determine the priority of commentary based on the publication date of the news articles at the time of provision. For example, the information provider can prioritize commenting on the latest news articles. It can also postpone commenting on older news articles. Furthermore, the information provider can adjust the priority of commentary according to the publication date of the news articles. This allows for prioritizing commentary on the latest news by determining the priority of commentary based on the publication date of the news articles. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input news article publication date data into a generating AI and have the generating AI determine the priority of commentary.

[0085] The content provider can adjust the order of explanations based on the relevance of the news articles at the time of delivery. For example, the content provider can prioritize explaining highly relevant news articles. It can also postpone explaining less relevant news articles. Furthermore, the content provider can adjust the order of explanations according to the relevance of the news articles. This allows for prioritizing the explanation of highly relevant news by adjusting the order of explanations based on the relevance of the news articles. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the content provider can input news article relevance data into a generating AI and have the generating AI perform the adjustment of the explanation order.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect information periodically. If the user is in a hurry, the data collection unit can collect information quickly. If the user is excited, the data collection unit can collect information in real time. By adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time. 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, for example, or without AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The data collection unit can adjust the level of detail collected based on the importance of ongoing events. For example, the data collection unit can collect detailed information for high-importance events. It can also collect concise information for low-importance events. Furthermore, the data collection unit can determine the priority of collection according to the importance of events. This allows for the collection of detailed information for important events by adjusting the level of detail according to the importance of ongoing events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of collection.

[0088] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is nervous, the data collection unit can prioritize collecting information of high importance. If the user is relaxed, the data collection unit can prioritize collecting detailed information. If the user is in a hurry, the data collection unit can prioritize collecting concise information. In this way, by prioritizing the information to be collected according to the user's emotions, important information can be collected preferentially. 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, for example, or without AI. For example, the data collection unit can input the user's voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of ongoing events during data collection. For example, if an ongoing event is close to the user's current location, the data collection unit can prioritize the collection of that information. The data collection unit can also prioritize the collection of information if an ongoing event is related to the user's area of ​​interest. Furthermore, the data collection unit can prioritize the collection of information if an ongoing event is related to the user's past travel history. This allows the data collection unit to provide useful information to the user by prioritizing the collection of highly relevant information based on the geographical location information of ongoing events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data of ongoing events into a generating AI and have the generating AI identify highly relevant information.

[0090] The update unit can estimate the user's emotions and adjust the timing of updates based on the estimated emotions. For example, if the user is relaxed, the update unit can update information periodically. If the user is in a hurry, the update unit can also update information quickly. If the user is excited, the update unit can also update information in real time. By adjusting the timing of updates according to the user's emotions, information can be updated at a more appropriate time. 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 update unit may be performed using AI, or not using AI. For example, the update unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The update unit can adjust the level of detail of the update based on the importance of the collected information during the update process. For example, the update unit can perform detailed updates on information of high importance. Conversely, it can perform concise updates on information of low importance. The update unit can also determine the priority of updates according to the importance of the information. This allows for detailed updates to be performed on important information by adjusting the level of detail of the update according to the importance of the collected information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the update.

[0092] The update unit can estimate the user's emotions and determine the priority of information to update based on the estimated emotions. For example, if the user is stressed, the update unit can prioritize updating information of high importance. If the user is relaxed, the update unit can prioritize updating detailed information. If the user is in a hurry, the update unit can prioritize updating concise information. In this way, by determining the priority of information to update according to the user's emotions, important information can be updated preferentially. 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 update unit may be performed using AI, or not using AI. For example, the update unit can input user voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The update unit can prioritize updating highly relevant information by considering the geographical location information of the collected information during the update process. For example, if the collected information is related to the user's current location, the update unit can prioritize updating that information. It can also prioritize updating information related to the user's areas of interest. Furthermore, if the collected information is related to the user's past travel history, the update unit can prioritize updating that information. This allows the update unit to provide users with useful information by prioritizing the updating of highly relevant information based on the geographical location information of the collected information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the geographical location data of the collected information into a generating AI and have the generating AI identify highly relevant information.

[0094] The update unit can update collected information in chronological order. For example, the update unit can update the latest information on ongoing events in chronological order. It can also update the progress of past events in chronological order. Furthermore, the update unit can update predicted events in chronological order based on future plans. In this way, by updating collected information in chronological order, it is possible to provide the latest information on ongoing events. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input collected information into a generating AI and have the generating AI perform chronological updates.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The news distribution and commentary system can further adjust how news is delivered by taking into account the user's learning history. For example, if a user has previously viewed a lot of news on a particular topic, the system can prioritize providing news related to that topic. It can also avoid news formats that the user previously found difficult to understand and instead provide news in a more easily understandable format. Furthermore, it can refer to news formats that the user has previously rated highly and provide news in a similar format. By adjusting the news delivery method based on the user's learning history, the system can promote user comprehension and improve news acceptance.

[0097] The news delivery and commentary system can further estimate the user's emotions and adjust the frequency of news delivery based on those emotions. For example, if a user is stressed, the frequency of news delivery can be reduced to provide time for relaxation. Conversely, if a user is excited, the frequency of news delivery can be increased to provide interesting news. Furthermore, if a user is tired, news that can be read quickly can be prioritized. By adjusting the frequency of news delivery according to the user's emotions, the system can reduce the user's burden and improve news receptivity.

[0098] The news distribution and commentary system can further adjust how news is delivered based on the user's geographical location. For example, if a user is in a specific region, news related to that region can be prioritized. Similarly, if a user is traveling, news related to their destination can be provided. Furthermore, if a user is at home, local news can be prioritized. By tailoring news delivery based on the user's geographical location, the system can provide users with more relevant information.

[0099] The news distribution and commentary system can further analyze users' social media activity and provide relevant news. For example, it can prioritize news related to news that users have shared on social media. It can also filter news based on the content of posts from accounts that users follow. Furthermore, it can prioritize news related to topics in groups and communities that users participate in. In this way, by providing relevant news based on users' social media activity, it can provide information tailored to users' interests.

[0100] The news distribution and commentary system can further estimate the user's emotions and adjust the news display format based on those emotions. For example, if the user is relaxed, a detailed news article can be displayed. If the user is in a hurry, a concise news summary can be displayed. Furthermore, if the user is excited, a visually stimulating news display format can be provided. In this way, by adjusting the news display format according to the user's emotions, news can be provided that is easy for the user to understand.

[0101] The news distribution and commentary system can further analyze a user's past news browsing history and prioritize providing relevant news. For example, it can prioritize news related to news categories that the user has frequently viewed in the past. It can also provide news related to news that the user has previously rated highly. Furthermore, it can suggest relevant news based on the topics of news the user has previously viewed. In this way, by providing relevant news based on the user's past news browsing history, it can provide information tailored to the user's interests.

[0102] The news delivery and commentary system can further estimate the user's emotions and adjust the way news is notified based on those emotions. For example, if the user is relaxed, notifications can be kept to a minimum. If the user is in a hurry, only important news can be notified. Furthermore, if the user is excited, news can be notified in real time. By adjusting the way news is notified according to the user's emotions, news can be delivered to the user at the appropriate time.

[0103] The news distribution and commentary system can further adjust how news is delivered based on the user's learning style. For example, if the user is a visual learner, the system can provide news that makes extensive use of graphs and charts. If the user is an auditory learner, the system can provide news with audio commentary. Furthermore, if the user is an experiential learner, the system can provide interactive news content. By adjusting the news delivery method based on the user's learning style, the system can provide news that is easy for the user to understand.

[0104] The news distribution and commentary system can further estimate the user's emotions and adjust the way news is presented based on those emotions. For example, if the user is relaxed, detailed feedback can be requested. If the user is in a hurry, concise feedback can be requested. Furthermore, if the user is excited, visually stimulating feedback can be provided. By adjusting the news feedback method according to the user's emotions, the system can provide users with appropriate feedback.

[0105] The news distribution and commentary system can further analyze a user's past news rating history and prioritize providing relevant news. For example, it can prioritize news related to news categories that the user has previously rated highly. It can also avoid news categories that the user has previously rated poorly. Furthermore, it can suggest relevant news based on the topics of news the user has previously rated. In this way, by providing relevant news based on the user's past news rating history, it can provide information tailored to the user's interests.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The reception desk will have a function for users to enter questions about the news. The reception desk can accept questions in text or voice format. For example, users can enter questions by voice using a microphone. The reception desk can also estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions of the user. For example, if the user is feeling stressed, the acceptance of questions can be temporarily delayed to give them time to relax. Step 2: The analysis unit uses a generation AI to analyze news articles based on the questions entered by the reception unit. The analysis unit can analyze news articles using natural language processing technology and provide detailed explanations. It can also adjust the level of detail of the analysis based on the importance of the news article. For example, a detailed analysis can be performed on news articles of high importance. Step 3: The provisioning unit uses the generation AI to provide details of the news article analyzed by the analysis unit. The provisioning unit can provide detailed explanations in text or graph format. Step 4: The information gathering team collects information about ongoing events. The information gathering team can collect information from news sites and social media on the internet. Step 5: The update unit updates the information collected by the collection unit in chronological order. The update unit can update the latest information on ongoing events and the progress of past events in chronological order.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, collection unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text or voice questions from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes news articles using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the analysis results to the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from news sites and social media on the internet. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the collected information in chronological order. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, collection unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice questions from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes news articles using generating AI. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the analysis results to the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from news sites and social media on the internet. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the collected information in chronological order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, collection unit, and update unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice questions from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes news articles using generation AI. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the analysis results to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information from news sites and social media on the internet. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the collected information in chronological order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In 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.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 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.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, collection unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice questions from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes news articles using generating AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the analysis results to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information from news sites and social media on the internet. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the collected information in chronological order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A reception desk where you can enter questions about the news, An analysis unit analyzes news articles based on questions entered by the reception unit, A provisioning unit that provides details of the news article analyzed by the aforementioned analysis unit, A collection unit that gathers information about ongoing events, The system includes an update unit that updates the information collected by the collection unit in chronological order. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When a question is submitted, the content of the question is filtered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and accepts relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, 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 12) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the publication date of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing the content, the level of detail in the explanation will be adjusted based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing news articles, different commentary algorithms are applied depending on the article's category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the information, we will prioritize the explanations based on the publication date of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the content, we adjust the order of explanations based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is During data collection, adjust the level of detail based on the importance of ongoing events. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the geographical location of ongoing events. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is It estimates user sentiment and adjusts the timing of updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is During updates, adjust the level of detail of the update based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is It estimates the user's emotions and determines the priority of information to update based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is During updates, the system prioritizes updating highly relevant information, taking into account the geographical location of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is The collected information is updated chronologically. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 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. A reception desk where you can enter questions about the news, An analysis unit analyzes news articles based on questions entered by the reception unit, A provisioning unit that provides details of the news article analyzed by the aforementioned analysis unit, A collection unit that gathers information about ongoing events, The system includes an update unit that updates the information collected by the collection unit in chronological order. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system according to feature 1.

4. The aforementioned reception unit is When a question is submitted, the content of the question is filtered based on the user's current areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and accepts relevant questions. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the news article. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the news article. The system according to feature 1.

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