system

Abstract

The system according to this embodiment aims to collect relevant news in a specific field and customize it based on the user's preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects news. The analysis unit analyzes the news collected by the collection unit. The provision unit provides trend information extracted by the analysis unit. The customization unit customizes the information provided by the provision unit based on the user's preferences.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that relevant news in a specific field is not sufficiently collected and customized based on user preferences.

[0005] The system according to the embodiment aims to collect relevant news in a specific field and customize it based on user preferences.

Means for Solving the Problems

[0006] [[ID = 45]] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects news. The analysis unit analyzes the news collected by the collection unit. The provision unit provides trend information extracted by the analysis unit. The customization unit customizes the information provided by the provision unit based on the user's preferences. [Effects of the Invention]

[0007] The system according to this embodiment can collect relevant news in a specific field and customize it based on the user's preferences. [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) An agent system according to an embodiment of the present invention is a system that collects relevant news from around the world in a specific field and proposes it as trend information. This agent system provides ideas based on the user's preferences. It is an AI agent that acts like an expert researcher in that field. Specifically, it consists of the following steps. First, the agent system automatically collects news from around the world related to the specific field. In this process, the agent system collects information from news sites and social media on the internet. For example, in the art field, it can collect information on the latest art events and exhibitions. Next, the agent system analyzes the collected news and extracts trend information. The agent system analyzes the content of the collected news and determines which news is trending. For example, if a particular art style is attracting attention, it extracts news related to that style as trend information. Furthermore, it provides trend information and ideas based on the user's preferences. Users can set preferences for the agent system according to their interests and concerns. For example, if a designer is interested in a particular design style, it can prioritize providing trend information related to that style. This mechanism allows users to collect a wide range of information in a short time and efficiently obtain highly relevant information. For example, if a marketer is looking for new promotional ideas, the agent system can efficiently generate ideas by providing the latest marketing trends. It can also provide the latest information in real time. The agent system constantly collects and provides the latest news to users, ensuring they are always up-to-date on current trends. For instance, if a researcher wants to stay informed about the latest research trends, the agent system can efficiently provide information by offering the latest research news. In this way, using an agent system significantly reduces the time users spend searching for ideas, providing an environment where they can focus on creative activities. It can serve as a source of inspiration, improving the quality of their creative work.This allows the agent system to provide trend information and ideas based on user preferences.

[0029] The agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects news. The collection unit can collect information from, for example, news sites and social networking services (SNS) on the internet. The collection unit can obtain the latest news, for example, by using the APIs of specific news sites or SNS. The collection unit can also collect information from news sites using web scraping technology. For example, the collection unit filters news based on specific keywords and collects highly relevant news. The collection unit can, for example, set the frequency of news collection and collect the latest news regularly. The analysis unit analyzes the news collected by the collection unit. The analysis unit analyzes the content of the news, for example, by using natural language processing technology. The analysis unit can, for example, analyze the text of the news and extract keywords and topics. The analysis unit can, for example, classify the content of the news and extract trend information. The analysis unit can, for example, summarize the content of the news and extract important information. The provision unit provides the trend information extracted by the analysis unit. The information provider can, for example, provide trend information based on user preferences. The information provider can, for example, customize trend information according to user interests and concerns. The information provider can, for example, notify users of trend information based on user settings. The information provider can, for example, provide trend information in real time. The customization unit customizes information based on user preferences. The customization unit can, for example, customize information based on the user's past browsing history and survey results. The customization unit can, for example, adjust the display format of information according to user interests and concerns. The customization unit can, for example, adjust the frequency of information provision based on user settings. As a result, the agent system according to the embodiment can efficiently collect, analyze, provide, and customize news.

[0030] The data collection unit collects news. For example, it can collect information from news sites and social media on the internet. Specifically, it can obtain the latest news articles using RSS feeds from news sites. It can also collect posts related to specific hashtags or keywords using social media APIs. Furthermore, the data collection unit can collect information from news sites using web scraping technology. Web scraping involves analyzing the HTML structure of a specific news site and extracting the necessary information. For example, the data collection unit can filter news based on specific keywords and collect highly relevant news. This allows the data collection unit to efficiently collect news that is of interest to the user. The data collection unit can, for example, set the frequency of news collection and regularly collect the latest news. The collection frequency is adjustable according to user settings, and real-time news collection is also possible. This allows the data collection unit to always provide the latest information. Furthermore, the data collection unit centrally manages the collected news data and stores it in a database. This allows the collected news data to be efficiently utilized by subsequent analysis and provision units. The data collection unit also has a function to evaluate the reliability of news, and by filtering out unreliable information, it ensures the quality of information provided to users.

[0031] The analysis department analyzes the news collected by the collection department. For example, the analysis department uses natural language processing technology to analyze the content of the news. Specifically, the analysis department performs morphological analysis on the text data of the collected news and extracts words and phrases. This allows it to grasp the basic elements that make up the content of the news. Furthermore, the analysis department classifies the news topic based on the extracted words and phrases. For example, it can use machine learning algorithms to classify news into categories such as politics, economics, sports, and entertainment. The analysis department can also summarize the content of the news and extract important information. For example, it can use natural language generation technology to automatically generate a summary of the news. This allows users to quickly grasp important information without having to read long news articles. Furthermore, the analysis department also has the function of extracting news trend information. For example, it can identify keywords and topics that are frequently mentioned within a specific period and extract them as trends. This allows users to grasp current trends and efficiently track news of interest. The analysis department can also perform sentiment analysis of the news, determining whether the content of the news is positive, negative, or neutral. This allows users to understand the emotional nuances of news and adjust how they receive information.

[0032] The service provider provides trend information extracted by the analysis department. For example, the service provider can provide trend information based on user preferences. Specifically, it selects and provides highly relevant trend information based on the user's past browsing history and interests. The service provider can also notify users of trend information based on their settings. For example, if a user shows interest in a particular topic, it can notify them in real time of the latest trend information related to that topic. The service provider also has a function to display trend information in an easy-to-understand visual format. For example, it can visually represent trend fluctuations using graphs and charts. This allows users to intuitively understand trend fluctuations. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and relevance of the information it provides. For example, by having users evaluate the trend information provided, the service provider can optimize the information delivery method based on that evaluation. The service provider supports multiple devices and platforms, allowing users to access trend information from any device. This ensures that users can always stay informed about the latest trend information, regardless of location or time.

[0033] The customization function customizes information based on user preferences. For example, it can customize information based on a user's past browsing history or survey results. Specifically, the customization function analyzes the user's browsing history to identify topics and keywords of interest to the user. This allows it to provide information tailored to the user's interests. The customization function can adjust the frequency of information delivery based on user settings. For example, if a user wants to receive information frequently, the frequency can be set high; conversely, if they want to receive information less frequently, the frequency can be set low. The customization function also has a function to adjust the information display format. For example, if a user prefers visual information, a display format that makes extensive use of graphs and charts can be selected; if they prefer text-based information, detailed text information can be provided. Furthermore, the customization function can collect user feedback to improve the accuracy of information customization. For example, by having users evaluate the information provided, the customization function can optimize the information delivery method based on that evaluation. This allows the customization function to provide information that meets user needs and improve user satisfaction.

[0034] The data collection unit can collect information from news sites and social networking services (SNS) on the internet. For example, it can collect information from major news sites and news sites in specific categories. For example, it can use news site APIs to obtain the latest news. For example, it can use web scraping techniques to collect information from news sites. For example, it can filter news based on specific keywords to collect highly relevant news. For example, it can use SNS APIs to obtain the latest posts. For example, it can analyze the content of SNS posts to collect highly relevant information. For example, it can filter posts based on specific hashtags to collect highly relevant information. This allows the data collection unit to obtain a wide range of information from news sites and SNS on the internet. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data obtained using news site and SNS APIs into a generating AI, which can then filter and analyze the data.

[0035] The analysis unit can analyze the content of collected news and extract trend information. The analysis unit can analyze the content of news using, for example, natural language processing technology. The analysis unit can analyze the text of news and extract keywords and topics. The analysis unit can classify the content of news and extract trend information. The analysis unit can summarize the content of news and extract important information. The analysis unit can analyze the content of news and determine which news is trending. The analysis unit can filter news based on specific keywords or topics and extract trend information. The analysis unit can analyze the content of news and visualize trend information. In this way, the analysis unit can provide useful information to users by analyzing the content of collected news and extracting trend information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input collected news data into a generating AI, which can perform data analysis and extract trend information.

[0036] The service provider can provide trend information and ideas based on user preferences. For example, the service provider can customize trend information according to the user's interests. For example, the service provider can notify users of trend information based on their settings. For example, the service provider can provide trend information in real time. For example, the service provider can customize trend information based on the user's past browsing history and survey results. For example, the service provider can adjust the display format of information according to the user's interests. For example, the service provider can adjust the frequency of information provision based on the user's settings. This allows the service provider to provide useful information to users by providing trend information and ideas based on their preferences. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user settings and past browsing history into a generating AI, which can then customize and notify users of trend information.

[0037] The customization unit can customize information according to the user's interests and preferences. For example, the customization unit can customize information based on the user's past browsing history or survey results. For example, the customization unit can adjust the display format of information according to the user's interests and preferences. For example, the customization unit can adjust the frequency of information provision based on the user's settings. For example, the customization unit can customize information based on the user's current areas of interest. For example, the customization unit can customize information based on keywords related to the user's areas of interest. For example, the customization unit can provide highly relevant information based on the user's areas of interest. In this way, the customization unit can provide highly relevant information to the user by customizing information according to the user's interests and preferences. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past browsing history or survey results into a generating AI, which can then customize the information.

[0038] The service provider can provide the latest information in real time. The service provider can obtain the latest information, for example, by using APIs from news sites and social media. The service provider can also collect the latest information from news sites using web scraping technology, for example. The service provider can filter news based on specific keywords and provide the latest information that is highly relevant. The service provider can set the frequency of news collection and provide the latest information regularly. The service provider can notify users of the latest information based on their settings, for example. The service provider can collect news in real time and provide it to users immediately. This allows users to always stay informed of the latest trends by providing the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data obtained using APIs from news sites and social media into a generating AI, which can then filter and analyze the data.

[0039] The data collection unit can analyze the user's past news browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information from news sites that the user has frequently visited in the past. For example, the data collection unit can analyze the categories of news that the user has viewed in the past and collect relevant news. For example, the data collection unit can adjust the types of news collected at specific time periods based on the user's past browsing history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past news browsing history. 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 past news browsing history into a generating AI, which can then select the optimal data collection method.

[0040] The collection unit can filter news based on the user's current areas of interest when collecting it. For example, the collection unit can collect only news related to the user's current areas of interest. For example, the collection unit can filter news based on keywords related to the user's areas of interest. For example, the collection unit can prioritize collecting highly relevant news based on the user's areas of interest. In this way, the collection unit can collect highly relevant news by filtering news based on the user's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest into a generating AI, which can then filter the news.

[0041] The news collection unit can prioritize collecting highly relevant news by considering the user's geographical location when collecting news. For example, the collection unit can prioritize collecting local news related to the user's current location. For example, the collection unit can collect regional trending news based on the user's geographical location. For example, if the user is traveling, the collection unit can prioritize collecting news from their destination. In this way, the collection unit can provide highly relevant news by collecting news while considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant news.

[0042] The collection unit can analyze a user's social media activity and collect relevant news when collecting news. For example, the collection unit can collect news based on the content of posts from accounts that the user follows on social media. For example, the collection unit can collect information related to news that the user has shared on social media. For example, the collection unit can analyze a user's social media activity history and collect news based on their interests. In this way, the collection unit can collect relevant news by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI, which can then collect relevant news.

[0043] The analysis unit can adjust the level of detail in its news analysis based on the importance of the news. For example, the analysis unit can perform a detailed analysis on high-importance news, and a concise analysis on low-importance news. The analysis unit can also adjust the depth of the analysis and the method of visualization according to the importance of the news. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the news. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news importance data into a generating AI, which can then adjust the level of detail in the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the news category when analyzing news. For example, the analysis unit can apply an analysis algorithm using economic indicators to economic news. For example, the analysis unit can apply an algorithm that analyzes match results and player performance to sports news. For example, the analysis unit can apply an algorithm that analyzes research results and the number of citations of papers to science news. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the news category. 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 category data into a generating AI, which can then select and apply an appropriate analysis algorithm.

[0045] The analysis department can evaluate the reliability of news based on its source when analyzing news. For example, the analysis department may prioritize the analysis of information from highly reliable news websites. For example, the analysis department may treat social media posts as unreliable information. For example, the analysis department may reflect past reliability evaluations of news sources in its analysis results. In this way, the analysis department can provide reliable information by evaluating reliability based on the news source. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department may input news source data into a generating AI, which can then perform reliability evaluations.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant past news data when analyzing news. For example, the analysis unit can refer to similar past news data to analyze changes in trends. For example, the analysis unit can understand the background of current news based on past news data. For example, the analysis unit can predict future trends using past news data. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant past news data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past news data into a generating AI, which can then refer to the data and improve the accuracy of the analysis.

[0047] The service provider can analyze users' past reactions to select the optimal delivery method when providing trend information. For example, the service provider may prioritize the format of trend information that users have received favorably in the past. For example, the service provider may adjust the frequency of trend information delivery based on users' past reactions. For example, the service provider may analyze users' past reactions and provide trend information at the optimal timing. In this way, the service provider can select the optimal delivery method by analyzing users' past reactions. 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 may input users' past reaction data into a generating AI, which can then select the optimal delivery method.

[0048] The information provider can customize the information based on the user's current areas of interest when providing trend information. For example, the provider can prioritize providing trend information in areas that the user is currently interested in. For example, the provider can customize the trend information based on keywords related to the user's areas of interest. For example, the provider can provide highly relevant trend information based on the user's areas of interest. In this way, the provider can provide highly relevant information by customizing the information based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current areas of interest data into a generating AI, and the generating AI can customize the information.

[0049] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing trend information. For example, the information provider can prioritize providing local trend information related to the user's current location. For example, the information provider can provide regional trend information based on the user's geographical location. For example, if the user is traveling, the information provider can prioritize providing trend information for their destination. In this way, the information provider can provide highly relevant information by considering the user's geographical location. 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 the user's geographical location information into a generating AI, which can then provide highly relevant information.

[0050] The service provider can provide relevant information by analyzing the user's social media activity when providing trend information. For example, the service provider can provide trend information based on the content of posts from accounts that the user follows on social media. For example, the service provider can provide information related to trend information that the user has shared on social media. For example, the service provider can analyze the user's social media activity history and provide trend information based on their interests. In this way, the service provider can provide relevant information by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, and the generating AI can provide relevant information.

[0051] The customization unit can analyze the user's past interests and preferences to select the optimal customization method when customizing information. For example, the customization unit can customize information based on topics the user has shown interest in in the past. For example, the customization unit can analyze the user's past areas of interest and provide relevant information. For example, the customization unit can adjust the display format of information based on the user's past interests and preferences. In this way, the customization unit can select the optimal customization method by analyzing the user's past interests and preferences. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past interest and preference data into a generating AI, which can then select the optimal customization method.

[0052] The customization unit can customize information based on the user's current areas of interest when customizing information. For example, the customization unit can prioritize providing information in areas the user is currently interested in. For example, the customization unit can customize information based on keywords related to the user's areas of interest. For example, the customization unit can provide highly relevant information based on the user's areas of interest. In this way, the customization unit can provide highly relevant information by customizing information based on the user's current areas of interest. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's current areas of interest data into a generating AI, and the generating AI can customize the information.

[0053] The customization unit can prioritize highly relevant information when customizing information, taking into account the user's geographical location. For example, the customization unit can prioritize providing local information related to the user's current location. For example, the customization unit can provide regional trend information based on the user's geographical location. For example, if the user is traveling, the customization unit can prioritize providing information about their destination. In this way, the customization unit can provide highly relevant information by customizing information while taking the user's geographical location into account. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information into a generating AI, which can then customize the information to be highly relevant.

[0054] The customization unit can analyze the user's social media activity and customize relevant information when customizing information. For example, the customization unit can customize information based on the content of posts from accounts the user follows on social media. For example, the customization unit can provide content related to information the user has shared on social media. For example, the customization unit can analyze the user's social media activity history and customize information based on their interests. In this way, the customization unit can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media activity data into a generating AI, which can then customize the relevant information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's past news browsing history and select the optimal collection method. For example, it can prioritize collecting information from news sites that the user has frequently visited in the past. It can also analyze the categories of news the user has viewed in the past and collect related news. Furthermore, it can adjust the types of news collected at specific time periods based on the user's past browsing history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past news browsing history.

[0057] The analysis department can adjust the level of detail in news analysis based on its importance. For example, highly important news can be analyzed in detail, while less important news can be analyzed concisely. Furthermore, the depth of the analysis and the method of visualization can be adjusted according to the importance of the news. This allows the analysis department to perform efficient analysis by adjusting the level of detail based on the importance of the news.

[0058] The news collection unit can prioritize collecting highly relevant news by considering the user's geographical location. For example, it can prioritize collecting local news related to the user's current location. It can also collect regional trending news based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize collecting news from their destination. In this way, the news collection unit can provide highly relevant news by considering the user's geographical location when collecting news.

[0059] The analysis department can apply different analysis algorithms depending on the news category when analyzing news. For example, economic news can be analyzed using an analysis algorithm that utilizes economic indicators. Sports news can be analyzed using an algorithm that analyzes match results and athlete performance. Furthermore, science news can be analyzed using an algorithm that analyzes research results and the number of citations of papers. In this way, the analysis department can perform more accurate analyses by applying different analysis algorithms depending on the news category.

[0060] The service provider can analyze users' past reactions to select the optimal delivery method when providing trend information. For example, it can prioritize the format of trend information that users have received favorably in the past. It can also adjust the frequency of trend information delivery based on users' past reactions. Furthermore, it can analyze users' past reactions to provide trend information at the optimal timing. In this way, the service provider can select the optimal delivery method by analyzing users' past reactions.

[0061] The customization function can customize information based on the user's current areas of interest. For example, it can prioritize providing information related to the user's current areas of interest. It can also customize information based on keywords related to the user's areas of interest. Furthermore, it can provide highly relevant information based on the user's areas of interest. In this way, the customization function can provide highly relevant information by customizing it based on the user's current areas of interest.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects news. The collection unit can collect information from news sites and social media on the internet. For example, it can use the APIs of specific news sites or social media to obtain the latest news. It can also collect information from news sites using web scraping techniques. The collection unit filters news based on specific keywords and collects highly relevant news. Furthermore, it can set the frequency of news collection and collect the latest news regularly. Step 2: The analysis unit analyzes the news collected by the collection unit. The analysis unit uses natural language processing technology to analyze the content of the news. For example, it can analyze the text of the news and extract keywords and topics. It can also classify the content of the news and extract trend information. Furthermore, it can summarize the content of the news and extract important information. Step 3: The service provider provides the trend information extracted by the analysis service provider. The service provider can provide trend information based on user preferences. For example, it can customize trend information according to the user's interests and concerns. It can also notify users of trend information based on their settings. Furthermore, it can provide trend information in real time. Step 4: The customization section customizes information based on user preferences. The customization section can customize information based on the user's past browsing history and survey results. For example, it can adjust the display format of information according to the user's interests and preferences. It can also adjust the frequency of information provision based on the user's settings.

[0064] (Example of form 2) An agent system according to an embodiment of the present invention is a system that collects relevant news from around the world in a specific field and proposes it as trend information. This agent system provides ideas based on the user's preferences. It is an AI agent that acts like an expert researcher in that field. Specifically, it consists of the following steps. First, the agent system automatically collects news from around the world related to the specific field. In this process, the agent system collects information from news sites and social media on the internet. For example, in the art field, it can collect information on the latest art events and exhibitions. Next, the agent system analyzes the collected news and extracts trend information. The agent system analyzes the content of the collected news and determines which news is trending. For example, if a particular art style is attracting attention, it extracts news related to that style as trend information. Furthermore, it provides trend information and ideas based on the user's preferences. Users can set preferences for the agent system according to their interests and concerns. For example, if a designer is interested in a particular design style, it can prioritize providing trend information related to that style. This mechanism allows users to collect a wide range of information in a short time and efficiently obtain highly relevant information. For example, if a marketer is looking for new promotional ideas, the agent system can efficiently generate ideas by providing the latest marketing trends. It can also provide the latest information in real time. The agent system constantly collects and provides the latest news to users, ensuring they are always up-to-date on current trends. For instance, if a researcher wants to stay informed about the latest research trends, the agent system can efficiently provide information by offering the latest research news. In this way, using an agent system significantly reduces the time users spend searching for ideas, providing an environment where they can focus on creative activities. It can serve as a source of inspiration, improving the quality of their creative work.This allows the agent system to provide trend information and ideas based on user preferences.

[0065] The agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a customization unit. The collection unit collects news. The collection unit can collect information from, for example, news sites and social networking services (SNS) on the internet. The collection unit can obtain the latest news, for example, by using the APIs of specific news sites or SNS. The collection unit can also collect information from news sites using web scraping technology. For example, the collection unit filters news based on specific keywords and collects highly relevant news. The collection unit can, for example, set the frequency of news collection and collect the latest news regularly. The analysis unit analyzes the news collected by the collection unit. The analysis unit analyzes the content of the news, for example, by using natural language processing technology. The analysis unit can, for example, analyze the text of the news and extract keywords and topics. The analysis unit can, for example, classify the content of the news and extract trend information. The analysis unit can, for example, summarize the content of the news and extract important information. The provision unit provides the trend information extracted by the analysis unit. The information provider can, for example, provide trend information based on user preferences. The information provider can, for example, customize trend information according to user interests and concerns. The information provider can, for example, notify users of trend information based on user settings. The information provider can, for example, provide trend information in real time. The customization unit customizes information based on user preferences. The customization unit can, for example, customize information based on the user's past browsing history and survey results. The customization unit can, for example, adjust the display format of information according to user interests and concerns. The customization unit can, for example, adjust the frequency of information provision based on user settings. As a result, the agent system according to the embodiment can efficiently collect, analyze, provide, and customize news.

[0066] The data collection unit collects news. For example, it can collect information from news sites and social media on the internet. Specifically, it can obtain the latest news articles using RSS feeds from news sites. It can also collect posts related to specific hashtags or keywords using social media APIs. Furthermore, the data collection unit can collect information from news sites using web scraping technology. Web scraping involves analyzing the HTML structure of a specific news site and extracting the necessary information. For example, the data collection unit can filter news based on specific keywords and collect highly relevant news. This allows the data collection unit to efficiently collect news that is of interest to the user. The data collection unit can, for example, set the frequency of news collection and regularly collect the latest news. The collection frequency is adjustable according to user settings, and real-time news collection is also possible. This allows the data collection unit to always provide the latest information. Furthermore, the data collection unit centrally manages the collected news data and stores it in a database. This allows the collected news data to be efficiently utilized by subsequent analysis and provision units. The data collection unit also has a function to evaluate the reliability of news, and by filtering out unreliable information, it ensures the quality of information provided to users.

[0067] The analysis department analyzes the news collected by the collection department. For example, the analysis department uses natural language processing technology to analyze the content of the news. Specifically, the analysis department performs morphological analysis on the text data of the collected news and extracts words and phrases. This allows it to grasp the basic elements that make up the content of the news. Furthermore, the analysis department classifies the news topic based on the extracted words and phrases. For example, it can use machine learning algorithms to classify news into categories such as politics, economics, sports, and entertainment. The analysis department can also summarize the content of the news and extract important information. For example, it can use natural language generation technology to automatically generate a summary of the news. This allows users to quickly grasp important information without having to read long news articles. Furthermore, the analysis department also has the function of extracting news trend information. For example, it can identify keywords and topics that are frequently mentioned within a specific period and extract them as trends. This allows users to grasp current trends and efficiently track news of interest. The analysis department can also perform sentiment analysis of the news, determining whether the content of the news is positive, negative, or neutral. This allows users to understand the emotional nuances of news and adjust how they receive information.

[0068] The service provider provides trend information extracted by the analysis department. For example, the service provider can provide trend information based on user preferences. Specifically, it selects and provides highly relevant trend information based on the user's past browsing history and interests. The service provider can also notify users of trend information based on their settings. For example, if a user shows interest in a particular topic, it can notify them in real time of the latest trend information related to that topic. The service provider also has a function to display trend information in an easy-to-understand visual format. For example, it can visually represent trend fluctuations using graphs and charts. This allows users to intuitively understand trend fluctuations. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and relevance of the information it provides. For example, by having users evaluate the trend information provided, the service provider can optimize the information delivery method based on that evaluation. The service provider supports multiple devices and platforms, allowing users to access trend information from any device. This ensures that users can always stay informed about the latest trend information, regardless of location or time.

[0069] The customization function customizes information based on user preferences. For example, it can customize information based on a user's past browsing history or survey results. Specifically, the customization function analyzes the user's browsing history to identify topics and keywords of interest to the user. This allows it to provide information tailored to the user's interests. The customization function can adjust the frequency of information delivery based on user settings. For example, if a user wants to receive information frequently, the frequency can be set high; conversely, if they want to receive information less frequently, the frequency can be set low. The customization function also has a function to adjust the information display format. For example, if a user prefers visual information, a display format that makes extensive use of graphs and charts can be selected; if they prefer text-based information, detailed text information can be provided. Furthermore, the customization function can collect user feedback to improve the accuracy of information customization. For example, by having users evaluate the information provided, the customization function can optimize the information delivery method based on that evaluation. This allows the customization function to provide information that meets user needs and improve user satisfaction.

[0070] The data collection unit can collect information from news sites and social networking services (SNS) on the internet. For example, it can collect information from major news sites and news sites in specific categories. For example, it can use news site APIs to obtain the latest news. For example, it can use web scraping techniques to collect information from news sites. For example, it can filter news based on specific keywords to collect highly relevant news. For example, it can use SNS APIs to obtain the latest posts. For example, it can analyze the content of SNS posts to collect highly relevant information. For example, it can filter posts based on specific hashtags to collect highly relevant information. This allows the data collection unit to obtain a wide range of information from news sites and SNS on the internet. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data obtained using news site and SNS APIs into a generating AI, which can then filter and analyze the data.

[0071] The analysis unit can analyze the content of collected news and extract trend information. The analysis unit can analyze the content of news using, for example, natural language processing technology. The analysis unit can analyze the text of news and extract keywords and topics. The analysis unit can classify the content of news and extract trend information. The analysis unit can summarize the content of news and extract important information. The analysis unit can analyze the content of news and determine which news is trending. The analysis unit can filter news based on specific keywords or topics and extract trend information. The analysis unit can analyze the content of news and visualize trend information. In this way, the analysis unit can provide useful information to users by analyzing the content of collected news and extracting trend information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input collected news data into a generating AI, which can perform data analysis and extract trend information.

[0072] The service provider can provide trend information and ideas based on user preferences. For example, the service provider can customize trend information according to the user's interests. For example, the service provider can notify users of trend information based on their settings. For example, the service provider can provide trend information in real time. For example, the service provider can customize trend information based on the user's past browsing history and survey results. For example, the service provider can adjust the display format of information according to the user's interests. For example, the service provider can adjust the frequency of information provision based on the user's settings. This allows the service provider to provide useful information to users by providing trend information and ideas based on their preferences. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user settings and past browsing history into a generating AI, which can then customize and notify users of trend information.

[0073] The customization unit can customize information according to the user's interests and preferences. For example, the customization unit can customize information based on the user's past browsing history or survey results. For example, the customization unit can adjust the display format of information according to the user's interests and preferences. For example, the customization unit can adjust the frequency of information provision based on the user's settings. For example, the customization unit can customize information based on the user's current areas of interest. For example, the customization unit can customize information based on keywords related to the user's areas of interest. For example, the customization unit can provide highly relevant information based on the user's areas of interest. In this way, the customization unit can provide highly relevant information to the user by customizing information according to the user's interests and preferences. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past browsing history or survey results into a generating AI, which can then customize the information.

[0074] The service provider can provide the latest information in real time. The service provider can obtain the latest information, for example, by using APIs from news sites and social media. The service provider can also collect the latest information from news sites using web scraping technology, for example. The service provider can filter news based on specific keywords and provide the latest information that is highly relevant. The service provider can set the frequency of news collection and provide the latest information regularly. The service provider can notify users of the latest information based on their settings, for example. The service provider can collect news in real time and provide it to users immediately. This allows users to always stay informed of the latest trends by providing the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data obtained using APIs from news sites and social media into a generating AI, which can then filter and analyze the data.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of news collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect only important news. For example, if the user is relaxed, the data collection unit can increase the collection frequency and provide more news. For example, if the user is excited, the data collection unit can collect news in real time and provide it immediately. In this way, the data collection unit can collect news at a more appropriate time by adjusting the timing of news collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the timing of collection.

[0076] The data collection unit can analyze the user's past news browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information from news sites that the user has frequently visited in the past. For example, the data collection unit can analyze the categories of news that the user has viewed in the past and collect relevant news. For example, the data collection unit can adjust the types of news collected at specific time periods based on the user's past browsing history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past news browsing history. 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 past news browsing history into a generating AI, which can then select the optimal data collection method.

[0077] The collection unit can filter news based on the user's current areas of interest when collecting it. For example, the collection unit can collect only news related to the user's current areas of interest. For example, the collection unit can filter news based on keywords related to the user's areas of interest. For example, the collection unit can prioritize collecting highly relevant news based on the user's areas of interest. In this way, the collection unit can collect highly relevant news by filtering news based on the user's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest into a generating AI, which can then filter the news.

[0078] The data collection unit can estimate the user's emotions and determine the priority of news to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting positive news. If the user is relaxed, the data collection unit can collect news from a wide range of genres. If the user is excited, the data collection unit can prioritize collecting the latest breaking news. In this way, the data collection unit can provide more appropriate news by determining the priority of news to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate emotions and determine news priorities.

[0079] The news collection unit can prioritize collecting highly relevant news by considering the user's geographical location when collecting news. For example, the collection unit can prioritize collecting local news related to the user's current location. For example, the collection unit can collect regional trending news based on the user's geographical location. For example, if the user is traveling, the collection unit can prioritize collecting news from their destination. In this way, the collection unit can provide highly relevant news by collecting news while considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant news.

[0080] The collection unit can analyze a user's social media activity and collect relevant news when collecting news. For example, the collection unit can collect news based on the content of posts from accounts that the user follows on social media. For example, the collection unit can collect information related to news that the user has shared on social media. For example, the collection unit can analyze a user's social media activity history and collect news based on their interests. In this way, the collection unit can collect relevant news by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI, which can then collect relevant news.

[0081] The analysis unit can estimate the user's emotions and adjust the news analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a concise and to-the-point analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform an analysis using visually easy-to-understand graphs and charts. In this way, the analysis unit can provide more appropriate analysis results by adjusting the news analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the analysis method.

[0082] The analysis unit can adjust the level of detail in its news analysis based on the importance of the news. For example, the analysis unit can perform a detailed analysis on high-importance news, and a concise analysis on low-importance news. The analysis unit can also adjust the depth of the analysis and the method of visualization according to the importance of the news. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the news. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news importance data into a generating AI, which can then adjust the level of detail in the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the news category when analyzing news. For example, the analysis unit can apply an analysis algorithm using economic indicators to economic news. For example, the analysis unit can apply an algorithm that analyzes match results and player performance to sports news. For example, the analysis unit can apply an algorithm that analyzes research results and the number of citations of papers to science news. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the news category. 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 category data into a generating AI, which can then select and apply an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. 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 user emotion data into a generative AI, which can then estimate emotions and adjust the display method.

[0085] The analysis department can evaluate the reliability of news based on its source when analyzing news. For example, the analysis department may prioritize the analysis of information from highly reliable news websites. For example, the analysis department may treat social media posts as unreliable information. For example, the analysis department may reflect past reliability evaluations of news sources in its analysis results. In this way, the analysis department can provide reliable information by evaluating reliability based on the news source. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department may input news source data into a generating AI, which can then perform reliability evaluations.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant past news data when analyzing news. For example, the analysis unit can refer to similar past news data to analyze changes in trends. For example, the analysis unit can understand the background of current news based on past news data. For example, the analysis unit can predict future trends using past news data. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant past news data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past news data into a generating AI, which can then refer to the data and improve the accuracy of the analysis.

[0087] The service provider can estimate the user's emotions and adjust the way trend information is provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide concise and to-the-point trend information. For example, if the user is relaxed, the service provider can provide detailed trend information. For example, if the user is excited, the service provider can provide trend information using visually easy-to-understand graphs and charts. In this way, the service provider can provide more appropriate information by adjusting the way trend information is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate emotions and adjust the method of provision.

[0088] The service provider can analyze users' past reactions to select the optimal delivery method when providing trend information. For example, the service provider may prioritize the format of trend information that users have received favorably in the past. For example, the service provider may adjust the frequency of trend information delivery based on users' past reactions. For example, the service provider may analyze users' past reactions and provide trend information at the optimal timing. In this way, the service provider can select the optimal delivery method by analyzing users' past reactions. 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 may input users' past reaction data into a generating AI, which can then select the optimal delivery method.

[0089] The information provider can customize the information based on the user's current areas of interest when providing trend information. For example, the provider can prioritize providing trend information in areas that the user is currently interested in. For example, the provider can customize the trend information based on keywords related to the user's areas of interest. For example, the provider can provide highly relevant trend information based on the user's areas of interest. In this way, the provider can provide highly relevant information by customizing the information based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current areas of interest data into a generating AI, and the generating AI can customize the information.

[0090] The service provider can estimate the user's emotions and prioritize trend information based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing positive trend information. For example, if the user is relaxed, the service provider can provide trend information across a wide range of genres. For example, if the user is excited, the service provider can prioritize providing the latest breaking trend information. In this way, the service provider can provide more appropriate information by prioritizing trend information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate emotions and determine the priority of trend information.

[0091] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing trend information. For example, the information provider can prioritize providing local trend information related to the user's current location. For example, the information provider can provide regional trend information based on the user's geographical location. For example, if the user is traveling, the information provider can prioritize providing trend information for their destination. In this way, the information provider can provide highly relevant information by considering the user's geographical location. 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 the user's geographical location information into a generating AI, which can then provide highly relevant information.

[0092] The service provider can provide relevant information by analyzing the user's social media activity when providing trend information. For example, the service provider can provide trend information based on the content of posts from accounts that the user follows on social media. For example, the service provider can provide information related to trend information that the user has shared on social media. For example, the service provider can analyze the user's social media activity history and provide trend information based on their interests. In this way, the service provider can provide relevant information by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, and the generating AI can provide relevant information.

[0093] The customization unit can estimate the user's emotions and adjust how information is customized based on the estimated emotions. For example, if the user is stressed, the customization unit can provide concise and to-the-point information. For example, if the user is relaxed, the customization unit can provide detailed information. For example, if the user is excited, the customization unit can provide information using visually easy-to-understand graphs and charts. In this way, the customization unit can provide more appropriate information by adjusting how information is customized 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 customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the customization method.

[0094] The customization unit can analyze the user's past interests and preferences to select the optimal customization method when customizing information. For example, the customization unit can customize information based on topics the user has shown interest in in the past. For example, the customization unit can analyze the user's past areas of interest and provide relevant information. For example, the customization unit can adjust the display format of information based on the user's past interests and preferences. In this way, the customization unit can select the optimal customization method by analyzing the user's past interests and preferences. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past interest and preference data into a generating AI, which can then select the optimal customization method.

[0095] The customization unit can customize information based on the user's current areas of interest when customizing information. For example, the customization unit can prioritize providing information in areas the user is currently interested in. For example, the customization unit can customize information based on keywords related to the user's areas of interest. For example, the customization unit can provide highly relevant information based on the user's areas of interest. In this way, the customization unit can provide highly relevant information by customizing information based on the user's current areas of interest. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's current areas of interest data into a generating AI, and the generating AI can customize the information.

[0096] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated emotions. For example, if the user is stressed, the customization unit can prioritize providing positive information. For example, if the user is relaxed, the customization unit can provide information across a wide range of genres. For example, if the user is excited, the customization unit can prioritize providing the latest breaking news. In this way, the customization unit can provide more appropriate information by determining customization priorities 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 customization unit may be performed using AI or not using AI. For example, the customization unit can input user emotion data into a generative AI, which can then estimate emotions and determine customization priorities.

[0097] The customization unit can prioritize highly relevant information when customizing information, taking into account the user's geographical location. For example, the customization unit can prioritize providing local information related to the user's current location. For example, the customization unit can provide regional trend information based on the user's geographical location. For example, if the user is traveling, the customization unit can prioritize providing information about their destination. In this way, the customization unit can provide highly relevant information by customizing information while taking the user's geographical location into account. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information into a generating AI, which can then customize the information to be highly relevant.

[0098] The customization unit can analyze the user's social media activity and customize relevant information when customizing information. For example, the customization unit can customize information based on the content of posts from accounts the user follows on social media. For example, the customization unit can provide content related to information the user has shared on social media. For example, the customization unit can analyze the user's social media activity history and customize information based on their interests. In this way, the customization unit can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media activity data into a generating AI, which can then customize the relevant information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can analyze a user's past news browsing history and select the optimal collection method. For example, it can prioritize collecting information from news sites that the user has frequently visited in the past. It can also analyze the categories of news the user has viewed in the past and collect related news. Furthermore, it can adjust the types of news collected at specific time periods based on the user's past browsing history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past news browsing history.

[0101] The analysis department can adjust the level of detail in news analysis based on its importance. For example, highly important news can be analyzed in detail, while less important news can be analyzed concisely. Furthermore, the depth of the analysis and the method of visualization can be adjusted according to the importance of the news. This allows the analysis department to perform efficient analysis by adjusting the level of detail based on the importance of the news.

[0102] The service provider can estimate the user's emotions and adjust the way trend information is delivered based on those emotions. For example, if the user is stressed, concise and to-the-point trend information can be provided. If the user is relaxed, detailed trend information can be provided. Furthermore, if the user is excited, trend information using visually easy-to-understand graphs and charts can be provided. In this way, the service provider can provide more appropriate information by adjusting the way trend information is delivered according to the user's emotions.

[0103] The customization unit can estimate the user's emotions and adjust how information is customized based on those emotions. For example, if the user is stressed, it can provide concise and to-the-point information. If the user is relaxed, it can provide detailed information. Furthermore, if the user is excited, it can provide information using visually easy-to-understand graphs and charts. In this way, the customization unit can provide more appropriate information by adjusting how information is customized according to the user's emotions.

[0104] The news collection unit can prioritize collecting highly relevant news by considering the user's geographical location. For example, it can prioritize collecting local news related to the user's current location. It can also collect regional trending news based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize collecting news from their destination. In this way, the news collection unit can provide highly relevant news by considering the user's geographical location when collecting news.

[0105] The analysis department can apply different analysis algorithms depending on the news category when analyzing news. For example, economic news can be analyzed using an analysis algorithm that utilizes economic indicators. Sports news can be analyzed using an algorithm that analyzes match results and athlete performance. Furthermore, science news can be analyzed using an algorithm that analyzes research results and the number of citations of papers. In this way, the analysis department can perform more accurate analyses by applying different analysis algorithms depending on the news category.

[0106] The service provider can analyze users' past reactions to select the optimal delivery method when providing trend information. For example, it can prioritize the format of trend information that users have received favorably in the past. It can also adjust the frequency of trend information delivery based on users' past reactions. Furthermore, it can analyze users' past reactions to provide trend information at the optimal timing. In this way, the service provider can select the optimal delivery method by analyzing users' past reactions.

[0107] The customization function can customize information based on the user's current areas of interest. For example, it can prioritize providing information related to the user's current areas of interest. It can also customize information based on keywords related to the user's areas of interest. Furthermore, it can provide highly relevant information based on the user's areas of interest. In this way, the customization function can provide highly relevant information by customizing it based on the user's current areas of interest.

[0108] The news collection unit can estimate the user's emotions and determine the priority of news to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting positive news. If the user is relaxed, it can collect news from a wide range of genres. Furthermore, if the user is excited, it can prioritize collecting the latest breaking news. In this way, the news collection unit can provide more relevant news by prioritizing news collection according to the user's emotions.

[0109] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects news. The collection unit can collect information from news sites and social media on the internet. For example, it can use the APIs of specific news sites or social media to obtain the latest news. It can also collect information from news sites using web scraping techniques. The collection unit filters news based on specific keywords and collects highly relevant news. Furthermore, it can set the frequency of news collection and collect the latest news regularly. Step 2: The analysis unit analyzes the news collected by the collection unit. The analysis unit uses natural language processing technology to analyze the content of the news. For example, it can analyze the text of the news and extract keywords and topics. It can also classify the content of the news and extract trend information. Furthermore, it can summarize the content of the news and extract important information. Step 3: The service provider provides the trend information extracted by the analysis service provider. The service provider can provide trend information based on user preferences. For example, it can customize trend information according to the user's interests and concerns. It can also notify users of trend information based on their settings. Furthermore, it can provide trend information in real time. Step 4: The customization section customizes information based on user preferences. The customization section can customize information based on the user's past browsing history and survey results. For example, it can adjust the display format of information according to the user's interests and preferences. It can also adjust the frequency of information provision based on the user's settings.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information from news sites and social networking services on the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected news using natural language processing technology. The provision unit is implemented by the control unit 46A of the smart device 14 and provides trend information based on the user's preferences. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the information based on the user's past browsing history and survey results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from news sites and social networking services on the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected news using natural language processing technology. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides trend information based on the user's preferences. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the information based on the user's past browsing history and survey results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from news sites and social networking services on the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected news using natural language processing technology. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides trend information based on the user's preferences. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the information based on the user's past browsing history and survey results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from news sites and social networking services on the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected news using natural language processing technology. The provision unit is implemented by the control unit 46A of the robot 414 and provides trend information based on the user's preferences. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and customizes the information based on the user's past browsing history and survey results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The news collection department, An analysis unit analyzes the news collected by the aforementioned collection unit, A providing unit that provides trend information extracted by the aforementioned analysis unit, The system includes a customization unit that customizes the information provided by the aforementioned provisioning unit based on the user's preferences. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from news sites and social media on the internet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected news content is analyzed to extract trending information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides trend information and ideas based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Customize information according to the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing the latest information in real time The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past news browsing history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting news, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of news to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting news, the system prioritizes collecting highly relevant news by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting news, the system analyzes users' social media activity and gathers relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust news analysis methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing news, adjust the level of detail in the analysis based on the importance of the news. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing news, different analysis algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing news, assess its reliability based on its source. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing news, referencing relevant historical news data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate user sentiment and adjust how trend information is delivered based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing trend information, we analyze users' past reactions to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing trend information, customize the information based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates user sentiment and prioritizes trend information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing trend information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing trend information, we analyze users' social media activity to provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is It estimates the user's emotions and adjusts how information is customized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is When customizing information, the system analyzes the user's past interests and preferences to select the optimal customization method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is When customizing information, the information is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is When customizing information, the system prioritizes highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is When customizing information, analyze the user's social media activity to tailor relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The news collection department, An analysis unit analyzes the news collected by the aforementioned collection unit, A providing unit that provides trend information extracted by the aforementioned analysis unit, The system includes a customization unit that customizes the information provided by the aforementioned provisioning unit based on the user's preferences. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from news sites and social media on the internet. The system according to feature 1.

3. The aforementioned analysis unit is The collected news content is analyzed to extract trending information. The system according to feature 1.

4. The aforementioned supply unit is, Provides trend information and ideas based on user preferences. The system according to feature 1.

5. The aforementioned customization unit is Customize information according to the user's interests and preferences. The system according to feature 1.

6. The aforementioned supply unit is, Providing the latest information in real time The system according to feature 1.

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

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

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

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of news to collect based on the estimated user sentiment. The system according to feature 1.

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