system

Abstract

The system according to this embodiment aims to allow users to quickly catch up on topics they haven't read yet. [Solution] The system according to the embodiment comprises a collection unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects read information. The extraction unit extracts unread topics based on the read information collected by the collection unit. The summarization unit summarizes the topics extracted by the extraction unit. The provision unit provides the information summarized by the summarization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 prior art, there was a problem that it was difficult for a user to efficiently manage read information and unread information and quickly catch up with unread topics.

[0005] The system according to the embodiment aims to enable a user to quickly catch up with unread topics.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects read information. The extraction unit extracts unread topics based on the read information collected by the collection unit. The summarization unit summarizes the topics extracted by the extraction unit. The provision unit provides the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to quickly catch up on unread topics. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention utilizes the read receipt processing of messenger and news apps to extract only the topics that the user has not yet caught up on, and the AI ​​summarizes them, enabling the user to catch up on current events as quickly and efficiently as possible. This system collects read receipt information from messenger and news apps to understand which news the user has already read. For example, it collects information on news links sent by friends in the messenger app and articles already viewed in the news app. Next, based on the collected read receipt information, it extracts topics that the user has not yet read. For example, it identifies unread news articles in the news app and unread news links in the messenger app. Furthermore, the AI ​​summarizes the extracted topics. The AI ​​analyzes the content of the unread news articles and news links, extracts important points, and creates a summary. For example, by summarizing a long news article into a short summary, the user can obtain information efficiently. Finally, the summarized topics are provided to the user. By checking the summarized information, the user can catch up on the latest topics in a short time. For example, the summarized information is provided to the user using the notification function of the news app or messenger app. This system allows users to efficiently keep up with the latest topics. For example, it is useful as an efficient way to obtain information for users with limited time, such as busy business people or students. Furthermore, because AI performs the summarization, users can obtain accurate information without any excess or omission. In this way, the system enables users to efficiently keep up with the latest topics.

[0029] The information provision system according to this embodiment comprises a collection unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects news information that the user has already read. For example, the collection unit collects news links sent by friends via a messenger app and information on articles already viewed in a news app. For example, the collection unit obtains read status information for news links from a messenger app and read status information for articles from a news app. The collection unit centrally manages this information and understands which news the user has already read. The extraction unit extracts topics that the user has not yet read based on the read status information collected by the collection unit. For example, the extraction unit identifies unread news articles in a news app and unread news links in a messenger app. This allows the extraction unit to understand topics that the user has not yet caught up on. The summarization unit summarizes the topics extracted by the extraction unit. For example, the summarization unit analyzes the content of unread news articles and news links, extracts important points, and creates a summary. The summarization unit uses a generative AI to condense long news articles into short summaries. The generative AI, for example, uses a text generation AI (e.g., LLM) to extract the main points of the news article and generate the summary. The generative AI analyzes the content of the news article, extracts important information, and creates the summary. The delivery unit provides the information summarized by the summarization unit to the user. The delivery unit provides the summarized information to the user, for example, by using the notification function of a news app or messenger app. The delivery unit provides the summarized information quickly so that the user can efficiently obtain the information. As a result, the information provision system according to this embodiment allows the user to efficiently catch up on the latest topics. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can provide information using an AI model for providing summarized information to the user.

[0030] The summarization unit can analyze the content of unread news articles or news links, extract key points, and create summaries. For example, the summarization unit analyzes the content of unread news articles, extracts key points, and creates summaries. The summarization unit uses generative AI to extract the main points of news articles and generate summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze the content of news articles, extract key information, and create summaries. The generative AI analyzes the content of news articles, extracts key information, and creates summaries. For example, the summarization unit analyzes the content of news links, extracts key points, and creates summaries. The summarization unit uses generative AI to extract the main points of news links and generate summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze the content of news links, extract key information, and create summaries. The generative AI analyzes the content of news links, extracts key information, and creates summaries. This allows the summarization unit to efficiently summarize the content of unread news articles and news links.

[0031] The service provider can provide summarized information to users using the notification function of a news app or messenger app. For example, the service provider can provide summarized information to users using the notification function of a news app. The service provider can quickly provide summarized information to users using the notification function of a news app. For example, the service provider can provide summarized information to users using the notification function of a messenger app. The service provider can quickly provide summarized information to users using the notification function of a messenger app. This allows the service provider to quickly provide summarized information to users.

[0032] The data collection unit can collect information on news links sent by friends via messenger apps or articles already viewed in news apps. For example, the data collection unit collects information on news links sent by friends via messenger apps. The data collection unit obtains read status information for news links from messenger apps to understand which news links the user has already read. For example, the data collection unit collects information on articles already viewed in news apps. The data collection unit obtains read status information for articles from news apps to understand which news articles the user has already read. This allows the data collection unit to efficiently collect news information that the user has already viewed.

[0033] The extraction unit can identify unread news articles in news apps or unread news links in messenger apps. For example, the extraction unit identifies unread news articles in news apps. The extraction unit retrieves information on unread news articles from news apps and identifies news articles that the user has not yet read. For example, the extraction unit identifies unread news links in messenger apps. The extraction unit retrieves information on unread news links from messenger apps and identifies news links that the user has not yet read. In this way, the extraction unit can efficiently identify news information that the user has not yet read.

[0034] The data collection unit can analyze the user's past browsing history and select a collection method. For example, the data collection unit prioritizes collecting news categories that the user frequently views. By analyzing the user's past browsing history and prioritizing the collection of news categories that the user frequently views, the data collection unit efficiently collects information that the user is interested in. For example, the data collection unit analyzes the trends of articles that the user has viewed for extended periods in the past and prioritizes collecting similar content. By analyzing the user's past browsing history and analyzing the trends of articles that the user has viewed for extended periods in the past, the data collection unit efficiently collects information that the user is interested in. For example, if the user tends to browse at a specific time of day, the data collection unit will adjust the collection to match that time of day. By analyzing the user's past browsing history and adjusting the collection to match that time of day, the user can efficiently obtain information. This allows the data collection unit to collect read information in the most optimal way based on the user's past browsing history.

[0035] The data collection unit can filter the collected read information based on the user's current areas of interest. For example, the data collection unit prioritizes collecting read information related to topics the user is currently interested in. By understanding the user's current areas of interest and prioritizing the collection of relevant read information, the data collection unit efficiently collects information that the user is interested in. For example, if the user has shown interest in a particular news category, the data collection unit prioritizes collecting read information in that category. By understanding the user's current areas of interest and prioritizing the collection of read information in that category, the data collection unit efficiently collects information that the user is interested in. For example, the data collection unit collects relevant read information based on keywords the user has recently searched for. By understanding the user's current areas of interest and collecting relevant read information based on recently searched keywords, the data collection unit efficiently collects information that the user is interested in. This allows the data collection unit to collect the most relevant read information based on the user's current areas of interest.

[0036] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location when collecting read information. For example, the data collection unit prioritizes collecting news related to the user's current location. By considering the user's geographical location and prioritizing the collection of news related to the user's current location, the data collection unit efficiently collects information of interest to the user. For example, if the user is traveling, the data collection unit prioritizes collecting news related to the travel destination. By considering the user's geographical location and prioritizing the collection of news related to the travel destination, the data collection unit efficiently collects information of interest to the user. For example, if the user is interested in a particular region, the data collection unit prioritizes collecting news related to that region. By considering the user's geographical location and prioritizing the collection of news related to a particular region, the data collection unit efficiently collects information of interest to the user. This allows the data collection unit to collect optimal read information based on the user's geographical location.

[0037] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting read information. For example, the data collection unit can collect news articles that the user has shared on social media. By analyzing the user's social media activity and collecting news articles that the user has shared on social media, the data collection unit can efficiently collect information that the user is interested in. For example, the data collection unit can collect posts from accounts that the user follows on social media. By analyzing the user's social media activity and collecting posts from accounts that the user follows on social media, the data collection unit can efficiently collect information that the user is interested in. For example, the data collection unit can collect news articles that the user has "liked" on social media. By analyzing the user's social media activity and collecting news articles that the user has "liked" on social media, the data collection unit can efficiently collect information that the user is interested in. This allows the data collection unit to collect the most relevant read information based on the user's social media activity.

[0038] The extraction unit can improve the accuracy of its extraction based on the interrelationships of news articles. For example, the extraction unit groups related news articles and extracts unread topics. The extraction unit efficiently extracts unread topics by considering the interrelationships of news articles and grouping related news articles. For example, the extraction unit analyzes the sources and links of news articles and extracts highly relevant unread topics. The extraction unit efficiently extracts highly relevant unread topics by considering the interrelationships of news articles and analyzing the sources and links of news articles. For example, the extraction unit extracts related unread topics based on common keywords in news articles. The extraction unit efficiently extracts related unread topics based on common keywords in news articles and considers the interrelationships of news articles. In this way, the extraction unit can improve the accuracy of its extraction by considering the interrelationships of news articles.

[0039] The extraction unit can apply different extraction algorithms to each category of news articles during the extraction process. For example, the extraction unit applies an extraction algorithm using sentiment analysis to news articles in the entertainment category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm using sentiment analysis to news articles in the entertainment category, the extraction unit efficiently extracts information that users are interested in. For example, the extraction unit applies an extraction algorithm based on economic indicators to news articles in the business category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm based on economic indicators to news articles in the business category, the extraction unit efficiently extracts information that users are interested in. For example, the extraction unit applies an extraction algorithm based on match results and athlete performance to news articles in the sports category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm based on match results and athlete performance to news articles in the sports category, the extraction unit efficiently extracts information that users are interested in. This allows the extraction unit to apply the optimal extraction algorithm for each category of news articles.

[0040] The extraction unit can perform extraction based on the geographical distribution of news articles. For example, the extraction unit prioritizes extracting news articles related to the user's current location. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to the user's current location, the extraction unit efficiently extracts information of interest to the user. For example, if the user is traveling, the extraction unit prioritizes extracting news articles related to the travel destination. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to the travel destination, the extraction unit efficiently extracts information of interest to the user. For example, if the user is interested in a specific region, the extraction unit prioritizes extracting news articles related to that region. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to that region, the extraction unit efficiently extracts information of interest to the user. In this way, the extraction unit can improve the accuracy of extraction by considering the geographical distribution of news articles.

[0041] The extraction unit can improve the accuracy of its extraction based on related literature of news articles. For example, the extraction unit analyzes the sources and links of news articles to extract highly relevant unread topics. The extraction unit efficiently extracts highly relevant unread topics by referring to related literature of news articles and analyzing the sources and links of news articles. For example, the extraction unit extracts relevant unread topics based on common keywords in news articles. The extraction unit efficiently extracts relevant unread topics by referring to related literature of news articles and analyzing the common keywords of news articles. For example, the extraction unit improves the accuracy of its extraction by referring to related literature of news articles. The extraction unit efficiently extracts highly relevant unread topics by referring to related literature of news articles and analyzing the sources and links of news articles. As a result, the extraction unit can improve the accuracy of its extraction by referring to related literature of news articles.

[0042] The summarization function can adjust the level of detail in a summary based on the importance of the news article during summary generation. For example, the summarization function provides a detailed summary for high-importance news articles. By evaluating the importance of news articles and providing detailed summaries for high-importance articles, the summarization function can efficiently obtain information of interest to the user. For example, the summarization function provides a concise summary for low-importance news articles. By evaluating the importance of news articles and providing concise summaries for low-importance articles, the summarization function can efficiently obtain information of interest to the user. For example, the summarization function adjusts the length of the summary according to the importance of the news article. By evaluating the importance of news articles and adjusting the length of the summary according to the importance of the news article, the summarization function can efficiently obtain information of interest to the user. This allows the summarization function to provide summaries with the optimal level of detail according to the importance of the news article.

[0043] The summarization unit can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization unit applies a summarization algorithm that uses sentiment analysis to news articles in the entertainment category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm that uses sentiment analysis to news articles in the entertainment category, the summarization unit efficiently summarizes information that users are interested in. For example, the summarization unit applies a summarization algorithm based on economic indicators to news articles in the business category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm based on economic indicators to news articles in the business category, the summarization unit efficiently summarizes information that users are interested in. For example, the summarization unit applies a summarization algorithm based on match results and athlete performance to news articles in the sports category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm based on match results and athlete performance to news articles in the sports category, the summarization unit efficiently summarizes information that users are interested in. This allows the summarization unit to apply the optimal summarization algorithm for each category of news articles.

[0044] The summarization function can determine the priority of summaries based on when the news articles were submitted. For example, the summarization function prioritizes summarizing the most recent news articles. By considering the submission date of news articles and prioritizing summarizing the most recent news articles, the summarization function can efficiently provide users with information of interest. For example, the summarization function provides a concise summary for older news articles. By considering the submission date of news articles and providing a concise summary for older articles, the summarization function can efficiently provide users with information of interest. For example, the summarization function adjusts the level of detail in the summary according to the submission date. By considering the submission date of news articles and adjusting the level of detail in the summary according to the submission date, the summarization function can efficiently provide users with information of interest. This allows the summarization function to provide summaries with the optimal priority according to the submission date of news articles.

[0045] The summarization unit can adjust the order of summaries based on the relevance of the news articles during summary generation. For example, the summarization unit prioritizes summarizing highly relevant news articles. By evaluating the relevance of news articles and prioritizing their summarization, the summarization unit can efficiently obtain information of interest to the user. For example, the summarization unit provides a concise summary for less relevant news articles. By evaluating the relevance of news articles and providing a concise summary for less relevant news articles, the summarization unit can efficiently obtain information of interest to the user. For example, the summarization unit adjusts the order of summaries according to the relevance of the news articles. By evaluating the relevance of news articles and adjusting the order of summaries according to the relevance of the news articles, the summarization unit can efficiently obtain information of interest to the user. This allows the summarization unit to provide summaries in the optimal order according to the relevance of the news articles.

[0046] The information provider can select the method of providing information based on the user's past browsing history. For example, the provider can prioritize providing relevant information based on the news categories the user has frequently viewed in the past. By referring to the user's past browsing history and prioritizing the provision of relevant information based on the news categories the user has frequently viewed in the past, the provider can efficiently provide information that interests the user. For example, the provider can analyze the trends of articles the user has viewed for extended periods in the past and prioritize providing similar content. By referring to the user's past browsing history and analyzing the trends of articles the user has viewed for extended periods in the past, the provider can efficiently provide information that interests the user. For example, if the user tends to browse at a specific time of day, the provider can provide information tailored to that time of day. By referring to the user's past browsing history and providing information tailored to that time of day, the provider can efficiently provide information that interests the user. This allows the provider to provide information in the most optimal way based on the user's past browsing history.

[0047] The information provider can customize the method of delivery based on the user's current living situation. For example, if the user is commuting, the information provider can provide information via audio. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it via audio when the user is commuting. For example, if the user is relaxing at home, the information provider can provide information visually. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it visually when the user is relaxing at home. For example, if the user is exercising, the information provider can provide information in concise text. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it in concise text when the user is exercising. This allows the information provider to deliver information using the most appropriate method according to the user's current living situation.

[0048] The information provider can select the method of delivery based on the user's geographical location when providing information. For example, the provider can prioritize providing news related to the user's current location. By considering the user's geographical location and prioritizing news related to the user's current location, the provider can efficiently provide information of interest to the user. For example, if the user is traveling, the provider can prioritize providing news related to the travel destination. By considering the user's geographical location and prioritizing news related to the travel destination, the provider can efficiently provide information of interest to the user. For example, if the user is interested in a particular region, the provider can prioritize providing news related to that region. By considering the user's geographical location and prioritizing news related to a particular region, the provider can efficiently provide information of interest to the user. This allows the provider to deliver information in the most optimal way based on the user's geographical location.

[0049] The information provider can analyze the user's social media activity and propose a method of delivery when providing information. For example, the information provider can provide relevant information based on news articles shared by the user on social media. By analyzing the user's social media activity and providing relevant information based on news articles shared by the user on social media, the information provider can efficiently provide information that the user is interested in. For example, the information provider can provide relevant information based on the content of posts from accounts that the user follows on social media. By analyzing the user's social media activity and providing relevant information based on the content of posts from accounts that the user follows on social media, the information provider can efficiently provide information that the user is interested in. For example, the information provider can provide relevant information based on news articles that the user "liked" on social media. By analyzing the user's social media activity and providing relevant information based on news articles that the user "liked" on social media, the information provider can efficiently provide information that the user is interested in. This allows the information provider to provide information using the most appropriate method based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze a user's past subscription history and prioritize collecting information from specific news sources. For example, if a user frequently visits a particular news site, it will prioritize collecting news from that site. By analyzing a user's subscription history and prioritizing information from news sources the user trusts, the data collection unit efficiently collects information that interests the user. For example, if a user prefers to read articles by a particular journalist, it will prioritize collecting articles by that journalist. This allows the data collection unit to collect the most relevant information based on the user's subscription history.

[0052] The extraction unit can analyze a user's past search history and prioritize extracting relevant unread topics. For example, it can extract relevant news articles based on keywords the user has previously searched for. By analyzing the user's search history and prioritizing topics that the user is likely to be interested in, the extraction unit enables users to obtain information efficiently. For example, if a user has shown interest in a particular topic, the extraction unit will prioritize extracting unread news articles related to that topic. In this way, the extraction unit can extract the most relevant topics based on the user's search history.

[0053] The summarization function can evaluate the reliability of news articles and prioritize summarizing the most reliable information. For example, it can prioritize summarizing information from reliable news sources. By evaluating the reliability of news articles and prioritizing reliable information, the summarization function enables users to obtain accurate information. For example, the summarization function can evaluate the reliability of the sources and authors cited in news articles and prioritize summarizing the most reliable information. This allows the summarization function to provide the most appropriate summary based on the reliability of the news articles.

[0054] The service provider can adjust the method of information delivery according to the type of device the user is using. For example, if a user is using a smartphone, the service provider will primarily provide information through short text and images. By considering the type of device the user is using and providing information in the most appropriate format, the service provider makes it easier for users to receive the information. For example, if a user is using a tablet, the service provider will provide detailed information and interactive content. This allows the service provider to deliver information in the most optimal way for each user's device.

[0055] The data collection unit can adjust the frequency of information collection based on the user's internet connection status. For example, if the user is using a high-speed internet connection, it will collect information more frequently. By considering the user's internet connection status and collecting information at the optimal frequency, the data collection unit enables the user to obtain information efficiently. For example, if the user is using a slow internet connection, the data collection unit will reduce the frequency of information collection. This allows the data collection unit to collect information at the optimal frequency according to the user's internet connection status.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects news information that the user has already read. For example, the data collection unit collects news links sent by friends via messenger apps and information on articles already viewed in news apps. The data collection unit centrally manages this information and understands which news articles the user has already read. Step 2: The extraction unit extracts topics that the user has not yet read, based on the read information collected by the collection unit. For example, the extraction unit identifies unread news articles in news apps or unread news links in messenger apps. Step 3: The summarization unit summarizes the topics extracted by the extraction unit. For example, the summarization unit analyzes the content of unread news articles or news links, extracts key points, and creates a summary. It uses a generation AI to condense long news articles into short summaries. Step 4: The provider unit provides the user with the information summarized by the summary unit. The provider unit provides the summarized information to the user, for example, by using the notification function of a news app or messenger app. This ensures that the user can obtain the information efficiently by providing the summarized information quickly.

[0058] (Example of form 2) The system according to an embodiment of the present invention utilizes the read receipt processing of messenger and news apps to extract only the topics that the user has not yet caught up on, and the AI ​​summarizes them, enabling the user to catch up on current events as quickly and efficiently as possible. This system collects read receipt information from messenger and news apps to understand which news the user has already read. For example, it collects information on news links sent by friends in the messenger app and articles already viewed in the news app. Next, based on the collected read receipt information, it extracts topics that the user has not yet read. For example, it identifies unread news articles in the news app and unread news links in the messenger app. Furthermore, the AI ​​summarizes the extracted topics. The AI ​​analyzes the content of the unread news articles and news links, extracts important points, and creates a summary. For example, by summarizing a long news article into a short summary, the user can obtain information efficiently. Finally, the summarized topics are provided to the user. By checking the summarized information, the user can catch up on the latest topics in a short time. For example, the summarized information is provided to the user using the notification function of the news app or messenger app. This system allows users to efficiently keep up with the latest topics. For example, it is useful as an efficient way to obtain information for users with limited time, such as busy business people or students. Furthermore, because AI performs the summarization, users can obtain accurate information without any excess or omission. In this way, the system enables users to efficiently keep up with the latest topics.

[0059] The information provision system according to this embodiment comprises a collection unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects news information that the user has already read. For example, the collection unit collects news links sent by friends via a messenger app and information on articles already viewed in a news app. For example, the collection unit obtains read status information for news links from a messenger app and read status information for articles from a news app. The collection unit centrally manages this information and understands which news the user has already read. The extraction unit extracts topics that the user has not yet read based on the read status information collected by the collection unit. For example, the extraction unit identifies unread news articles in a news app and unread news links in a messenger app. This allows the extraction unit to understand topics that the user has not yet caught up on. The summarization unit summarizes the topics extracted by the extraction unit. For example, the summarization unit analyzes the content of unread news articles and news links, extracts important points, and creates a summary. The summarization unit uses a generative AI to condense long news articles into short summaries. The generative AI, for example, uses a text generation AI (e.g., LLM) to extract the main points of the news article and generate the summary. The generative AI analyzes the content of the news article, extracts important information, and creates the summary. The delivery unit provides the information summarized by the summarization unit to the user. The delivery unit provides the summarized information to the user, for example, by using the notification function of a news app or messenger app. The delivery unit provides the summarized information quickly so that the user can efficiently obtain the information. As a result, the information provision system according to this embodiment allows the user to efficiently catch up on the latest topics. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can provide information using an AI model for providing summarized information to the user.

[0060] The summarization unit can analyze the content of unread news articles or news links, extract key points, and create summaries. For example, the summarization unit analyzes the content of unread news articles, extracts key points, and creates summaries. The summarization unit uses generative AI to extract the main points of news articles and generate summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze the content of news articles, extract key information, and create summaries. The generative AI analyzes the content of news articles, extracts key information, and creates summaries. For example, the summarization unit analyzes the content of news links, extracts key points, and creates summaries. The summarization unit uses generative AI to extract the main points of news links and generate summaries. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze the content of news links, extract key information, and create summaries. The generative AI analyzes the content of news links, extracts key information, and creates summaries. This allows the summarization unit to efficiently summarize the content of unread news articles and news links.

[0061] The service provider can provide summarized information to users using the notification function of a news app or messenger app. For example, the service provider can provide summarized information to users using the notification function of a news app. The service provider can quickly provide summarized information to users using the notification function of a news app. For example, the service provider can provide summarized information to users using the notification function of a messenger app. The service provider can quickly provide summarized information to users using the notification function of a messenger app. This allows the service provider to quickly provide summarized information to users.

[0062] The data collection unit can collect information on news links sent by friends via messenger apps or articles already viewed in news apps. For example, the data collection unit collects information on news links sent by friends via messenger apps. The data collection unit obtains read status information for news links from messenger apps to understand which news links the user has already read. For example, the data collection unit collects information on articles already viewed in news apps. The data collection unit obtains read status information for articles from news apps to understand which news articles the user has already read. This allows the data collection unit to efficiently collect news information that the user has already viewed.

[0063] The extraction unit can identify unread news articles in news apps or unread news links in messenger apps. For example, the extraction unit identifies unread news articles in news apps. The extraction unit retrieves information on unread news articles from news apps and identifies news articles that the user has not yet read. For example, the extraction unit identifies unread news links in messenger apps. The extraction unit retrieves information on unread news links from messenger apps and identifies news links that the user has not yet read. In this way, the extraction unit can efficiently identify news information that the user has not yet read.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting read information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. By estimating the user's emotions and collecting the information when the user is relaxed, the data collection unit reduces the user's burden. For example, if the user is relaxed, the data collection unit can immediately collect read information and provide the latest information. By estimating the user's emotions and collecting the information when the user is relaxed, the data collection unit enables the user to obtain information efficiently. For example, if the user is busy, the data collection unit can adjust the collection timing and collect the information when the user is calm. By estimating the user's emotions and collecting the information when the user is calm, the data collection unit enables the user to obtain information efficiently. This allows the data collection unit to collect read information at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0065] The data collection unit can analyze the user's past browsing history and select a collection method. For example, the data collection unit prioritizes collecting news categories that the user frequently views. By analyzing the user's past browsing history and prioritizing the collection of news categories that the user frequently views, the data collection unit efficiently collects information that the user is interested in. For example, the data collection unit analyzes the trends of articles that the user has viewed for extended periods in the past and prioritizes collecting similar content. By analyzing the user's past browsing history and analyzing the trends of articles that the user has viewed for extended periods in the past, the data collection unit efficiently collects information that the user is interested in. For example, if the user tends to browse at a specific time of day, the data collection unit will adjust the collection to match that time of day. By analyzing the user's past browsing history and adjusting the collection to match that time of day, the user can efficiently obtain information. This allows the data collection unit to collect read information in the most optimal way based on the user's past browsing history.

[0066] The data collection unit can filter the collected read information based on the user's current areas of interest. For example, the data collection unit prioritizes collecting read information related to topics the user is currently interested in. By understanding the user's current areas of interest and prioritizing the collection of relevant read information, the data collection unit efficiently collects information that the user is interested in. For example, if the user has shown interest in a particular news category, the data collection unit prioritizes collecting read information in that category. By understanding the user's current areas of interest and prioritizing the collection of read information in that category, the data collection unit efficiently collects information that the user is interested in. For example, the data collection unit collects relevant read information based on keywords the user has recently searched for. By understanding the user's current areas of interest and collecting relevant read information based on recently searched keywords, the data collection unit efficiently collects information that the user is interested in. This allows the data collection unit to collect the most relevant read information based on the user's current areas of interest.

[0067] The data collection unit can estimate the user's emotions and determine the priority of the read information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting entertainment-related read information. By estimating the user's emotions and prioritizing entertainment-related read information when the user is excited, the data collection unit efficiently collects information that interests the user. For example, if the user is calm, the data collection unit will prioritize collecting business-related read information. By estimating the user's emotions and prioritizing business-related read information when the user is calm, the data collection unit efficiently collects information that interests the user. For example, if the user is tired, the data collection unit will prioritize collecting read information with relaxing content. By estimating the user's emotions and prioritizing read information with relaxing content when the user is tired, the data collection unit efficiently collects information that interests the user. This allows the data collection unit to collect read information with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0068] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location when collecting read information. For example, the data collection unit prioritizes collecting news related to the user's current location. By considering the user's geographical location and prioritizing the collection of news related to the user's current location, the data collection unit efficiently collects information of interest to the user. For example, if the user is traveling, the data collection unit prioritizes collecting news related to the travel destination. By considering the user's geographical location and prioritizing the collection of news related to the travel destination, the data collection unit efficiently collects information of interest to the user. For example, if the user is interested in a particular region, the data collection unit prioritizes collecting news related to that region. By considering the user's geographical location and prioritizing the collection of news related to a particular region, the data collection unit efficiently collects information of interest to the user. This allows the data collection unit to collect optimal read information based on the user's geographical location.

[0069] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting read information. For example, the data collection unit can collect news articles that the user has shared on social media. By analyzing the user's social media activity and collecting news articles that the user has shared on social media, the data collection unit can efficiently collect information that the user is interested in. For example, the data collection unit can collect posts from accounts that the user follows on social media. By analyzing the user's social media activity and collecting posts from accounts that the user follows on social media, the data collection unit can efficiently collect information that the user is interested in. For example, the data collection unit can collect news articles that the user has "liked" on social media. By analyzing the user's social media activity and collecting news articles that the user has "liked" on social media, the data collection unit can efficiently collect information that the user is interested in. This allows the data collection unit to collect the most relevant read information based on the user's social media activity.

[0070] The extraction unit can estimate the user's emotions and adjust the criteria for extracting unread topics based on the estimated emotions. For example, if the user is excited, the extraction unit will prioritize extracting entertainment-related unread topics. By estimating the user's emotions and prioritizing entertainment-related unread topics when the user is excited, the extraction unit efficiently extracts information that the user is interested in. For example, if the user is calm, the extraction unit will prioritize extracting business-related unread topics. By estimating the user's emotions and prioritizing business-related unread topics when the user is calm, the extraction unit efficiently extracts information that the user is interested in. For example, if the user is tired, the extraction unit will prioritize extracting unread topics with relaxing content. By estimating the user's emotions and prioritizing unread topics with relaxing content when the user is tired, the extraction unit efficiently extracts information that the user is interested in. This allows the extraction unit to extract unread topics with optimal criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The extraction unit can improve the accuracy of its extraction based on the interrelationships of news articles. For example, the extraction unit groups related news articles and extracts unread topics. The extraction unit efficiently extracts unread topics by considering the interrelationships of news articles and grouping related news articles. For example, the extraction unit analyzes the sources and links of news articles and extracts highly relevant unread topics. The extraction unit efficiently extracts highly relevant unread topics by considering the interrelationships of news articles and analyzing the sources and links of news articles. For example, the extraction unit extracts related unread topics based on common keywords in news articles. The extraction unit efficiently extracts related unread topics based on common keywords in news articles and considers the interrelationships of news articles. In this way, the extraction unit can improve the accuracy of its extraction by considering the interrelationships of news articles.

[0072] The extraction unit can apply different extraction algorithms to each category of news articles during the extraction process. For example, the extraction unit applies an extraction algorithm using sentiment analysis to news articles in the entertainment category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm using sentiment analysis to news articles in the entertainment category, the extraction unit efficiently extracts information that users are interested in. For example, the extraction unit applies an extraction algorithm based on economic indicators to news articles in the business category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm based on economic indicators to news articles in the business category, the extraction unit efficiently extracts information that users are interested in. For example, the extraction unit applies an extraction algorithm based on match results and athlete performance to news articles in the sports category. By applying different extraction algorithms to each category of news articles, and applying an extraction algorithm based on match results and athlete performance to news articles in the sports category, the extraction unit efficiently extracts information that users are interested in. This allows the extraction unit to apply the optimal extraction algorithm for each category of news articles.

[0073] The extraction unit can estimate the user's emotions and determine the priority of topics to extract based on the estimated emotions. For example, if the user is excited, the extraction unit will prioritize extracting entertainment-related topics. By estimating the user's emotions and prioritizing entertainment-related topics when the user is excited, the extraction unit efficiently extracts information that the user is interested in. For example, if the user is calm, the extraction unit will prioritize extracting business-related topics. By estimating the user's emotions and prioritizing business-related topics when the user is calm, the extraction unit efficiently extracts information that the user is interested in. For example, if the user is tired, the extraction unit will prioritize extracting topics that promote relaxation. By estimating the user's emotions and prioritizing topics that promote relaxation when the user is tired, the extraction unit efficiently extracts information that the user is interested in. This allows the extraction unit to extract topics with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0074] The extraction unit can perform extraction based on the geographical distribution of news articles. For example, the extraction unit prioritizes extracting news articles related to the user's current location. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to the user's current location, the extraction unit efficiently extracts information of interest to the user. For example, if the user is traveling, the extraction unit prioritizes extracting news articles related to the travel destination. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to the travel destination, the extraction unit efficiently extracts information of interest to the user. For example, if the user is interested in a specific region, the extraction unit prioritizes extracting news articles related to that region. By considering the geographical distribution of news articles and prioritizing the extraction of news articles related to that region, the extraction unit efficiently extracts information of interest to the user. In this way, the extraction unit can improve the accuracy of extraction by considering the geographical distribution of news articles.

[0075] The extraction unit can improve the accuracy of its extraction based on related literature of news articles. For example, the extraction unit analyzes the sources and links of news articles to extract highly relevant unread topics. The extraction unit efficiently extracts highly relevant unread topics by referring to related literature of news articles and analyzing the sources and links of news articles. For example, the extraction unit extracts relevant unread topics based on common keywords in news articles. The extraction unit efficiently extracts relevant unread topics by referring to related literature of news articles and analyzing the common keywords of news articles. For example, the extraction unit improves the accuracy of its extraction by referring to related literature of news articles. The extraction unit efficiently extracts highly relevant unread topics by referring to related literature of news articles and analyzing the sources and links of news articles. As a result, the extraction unit can improve the accuracy of its extraction by referring to related literature of news articles.

[0076] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit provides a detailed summary. By estimating the user's emotions and providing a detailed summary when the user is relaxed, the summarization unit can efficiently obtain information that interests the user. For example, if the user is in a hurry, the summarization unit provides a concise summary. By estimating the user's emotions and providing a concise summary when the user is in a hurry, the summarization unit can efficiently obtain information that interests the user. For example, if the user is excited, the summarization unit provides a summary that includes visually stimulating expressions. By estimating the user's emotions and providing a summary that includes visually stimulating expressions when the user is excited, the summarization unit can efficiently obtain information that interests the user. This allows the summarization unit to provide a summary in the most appropriate way 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The summarization function can adjust the level of detail in a summary based on the importance of the news article during summary generation. For example, the summarization function provides a detailed summary for high-importance news articles. By evaluating the importance of news articles and providing detailed summaries for high-importance articles, the summarization function can efficiently obtain information of interest to the user. For example, the summarization function provides a concise summary for low-importance news articles. By evaluating the importance of news articles and providing concise summaries for low-importance articles, the summarization function can efficiently obtain information of interest to the user. For example, the summarization function adjusts the length of the summary according to the importance of the news article. By evaluating the importance of news articles and adjusting the length of the summary according to the importance of the news article, the summarization function can efficiently obtain information of interest to the user. This allows the summarization function to provide summaries with the optimal level of detail according to the importance of the news article.

[0078] The summarization unit can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization unit applies a summarization algorithm that uses sentiment analysis to news articles in the entertainment category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm that uses sentiment analysis to news articles in the entertainment category, the summarization unit efficiently summarizes information that users are interested in. For example, the summarization unit applies a summarization algorithm based on economic indicators to news articles in the business category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm based on economic indicators to news articles in the business category, the summarization unit efficiently summarizes information that users are interested in. For example, the summarization unit applies a summarization algorithm based on match results and athlete performance to news articles in the sports category. By applying different summarization algorithms for each category of news articles, and applying a summarization algorithm based on match results and athlete performance to news articles in the sports category, the summarization unit efficiently summarizes information that users are interested in. This allows the summarization unit to apply the optimal summarization algorithm for each category of news articles.

[0079] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit provides a short, concise summary. By estimating the user's emotions and providing a short, concise summary when the user is in a hurry, the summarization unit can efficiently obtain information of interest to the user. For example, if the user is relaxed, the summarization unit provides a longer summary with detailed explanations. By estimating the user's emotions and providing a longer summary with detailed explanations when the user is relaxed, the summarization unit can efficiently obtain information of interest to the user. For example, if the user is excited, the summarization unit provides a summary with visually stimulating expressions. By estimating the user's emotions and providing a summary with visually stimulating expressions when the user is excited, the summarization unit can efficiently obtain information of interest to the user. This allows the summarization unit to provide a summary of the optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0080] The summarization function can determine the priority of summaries based on when the news articles were submitted. For example, the summarization function prioritizes summarizing the most recent news articles. By considering the submission date of news articles and prioritizing summarizing the most recent news articles, the summarization function can efficiently provide users with information of interest. For example, the summarization function provides a concise summary for older news articles. By considering the submission date of news articles and providing a concise summary for older articles, the summarization function can efficiently provide users with information of interest. For example, the summarization function adjusts the level of detail in the summary according to the submission date. By considering the submission date of news articles and adjusting the level of detail in the summary according to the submission date, the summarization function can efficiently provide users with information of interest. This allows the summarization function to provide summaries with the optimal priority according to the submission date of news articles.

[0081] The summarization unit can adjust the order of summaries based on the relevance of the news articles during summary generation. For example, the summarization unit prioritizes summarizing highly relevant news articles. By evaluating the relevance of news articles and prioritizing their summarization, the summarization unit can efficiently obtain information of interest to the user. For example, the summarization unit provides a concise summary for less relevant news articles. By evaluating the relevance of news articles and providing a concise summary for less relevant news articles, the summarization unit can efficiently obtain information of interest to the user. For example, the summarization unit adjusts the order of summaries according to the relevance of the news articles. By evaluating the relevance of news articles and adjusting the order of summaries according to the relevance of the news articles, the summarization unit can efficiently obtain information of interest to the user. This allows the summarization unit to provide summaries in the optimal order according to the relevance of the news articles.

[0082] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider will deliver the information in a calm tone. The information provider can estimate the user's emotions and, if the user is nervous, deliver the information in a calm tone to make it easier for the user to receive the information. For example, if the user is relaxed, the information provider will deliver the information in a cheerful tone. The information provider can estimate the user's emotions and, if the user is relaxed, deliver the information in a cheerful tone to make it easier for the user to receive the information. For example, if the user is in a hurry, the information provider will deliver the information quickly and concisely. The information provider can estimate the user's emotions and, if the user is in a hurry, deliver the information quickly and concisely to make it easier for the user to receive the information. This allows the information provider to deliver information in the most optimal way according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The information provider can select the method of providing information based on the user's past browsing history. For example, the provider can prioritize providing relevant information based on the news categories the user has frequently viewed in the past. By referring to the user's past browsing history and prioritizing the provision of relevant information based on the news categories the user has frequently viewed in the past, the provider can efficiently provide information that interests the user. For example, the provider can analyze the trends of articles the user has viewed for extended periods in the past and prioritize providing similar content. By referring to the user's past browsing history and analyzing the trends of articles the user has viewed for extended periods in the past, the provider can efficiently provide information that interests the user. For example, if the user tends to browse at a specific time of day, the provider can provide information tailored to that time of day. By referring to the user's past browsing history and providing information tailored to that time of day, the provider can efficiently provide information that interests the user. This allows the provider to provide information in the most optimal way based on the user's past browsing history.

[0084] The information provider can customize the method of delivery based on the user's current living situation. For example, if the user is commuting, the information provider can provide information via audio. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it via audio when the user is commuting. For example, if the user is relaxing at home, the information provider can provide information visually. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it visually when the user is relaxing at home. For example, if the user is exercising, the information provider can provide information in concise text. The information provider considers the user's current living situation and makes it easier for the user to receive the information by providing it in concise text when the user is exercising. This allows the information provider to deliver information using the most appropriate method according to the user's current living situation.

[0085] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is excited, the information provider will prioritize providing entertainment-related information. By estimating the user's emotions and prioritizing entertainment-related information when the user is excited, the information provider efficiently provides information that interests the user. For example, if the user is calm, the information provider will prioritize providing business-related information. By estimating the user's emotions and prioritizing business-related information when the user is calm, the information provider efficiently provides information that interests the user. For example, if the user is tired, the information provider will prioritize providing relaxing content. By estimating the user's emotions and prioritizing relaxing content when the user is tired, the information provider efficiently provides information that interests the user. This allows the information provider to provide information with the optimal priority 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.

[0086] The information provider can select the method of delivery based on the user's geographical location when providing information. For example, the provider can prioritize providing news related to the user's current location. By considering the user's geographical location and prioritizing news related to the user's current location, the provider can efficiently provide information of interest to the user. For example, if the user is traveling, the provider can prioritize providing news related to the travel destination. By considering the user's geographical location and prioritizing news related to the travel destination, the provider can efficiently provide information of interest to the user. For example, if the user is interested in a particular region, the provider can prioritize providing news related to that region. By considering the user's geographical location and prioritizing news related to a particular region, the provider can efficiently provide information of interest to the user. This allows the provider to deliver information in the most optimal way based on the user's geographical location.

[0087] The information provider can analyze the user's social media activity and propose a method of delivery when providing information. For example, the information provider can provide relevant information based on news articles shared by the user on social media. By analyzing the user's social media activity and providing relevant information based on news articles shared by the user on social media, the information provider can efficiently provide information that the user is interested in. For example, the information provider can provide relevant information based on the content of posts from accounts that the user follows on social media. By analyzing the user's social media activity and providing relevant information based on the content of posts from accounts that the user follows on social media, the information provider can efficiently provide information that the user is interested in. For example, the information provider can provide relevant information based on news articles that the user "liked" on social media. By analyzing the user's social media activity and providing relevant information based on news articles that the user "liked" on social media, the information provider can efficiently provide information that the user is interested in. This allows the information provider to provide information using the most appropriate method based on the user's social media activity.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can analyze a user's past subscription history and prioritize collecting information from specific news sources. For example, if a user frequently visits a particular news site, it will prioritize collecting news from that site. By analyzing a user's subscription history and prioritizing information from news sources the user trusts, the data collection unit efficiently collects information that interests the user. For example, if a user prefers to read articles by a particular journalist, it will prioritize collecting articles by that journalist. This allows the data collection unit to collect the most relevant information based on the user's subscription history.

[0090] The extraction unit can analyze a user's past search history and prioritize extracting relevant unread topics. For example, it can extract relevant news articles based on keywords the user has previously searched for. By analyzing the user's search history and prioritizing topics that the user is likely to be interested in, the extraction unit enables users to obtain information efficiently. For example, if a user has shown interest in a particular topic, the extraction unit will prioritize extracting unread news articles related to that topic. In this way, the extraction unit can extract the most relevant topics based on the user's search history.

[0091] The summarization function can evaluate the reliability of news articles and prioritize summarizing the most reliable information. For example, it can prioritize summarizing information from reliable news sources. By evaluating the reliability of news articles and prioritizing reliable information, the summarization function enables users to obtain accurate information. For example, the summarization function can evaluate the reliability of the sources and authors cited in news articles and prioritize summarizing the most reliable information. This allows the summarization function to provide the most appropriate summary based on the reliability of the news articles.

[0092] The service provider can adjust the method of information delivery according to the type of device the user is using. For example, if a user is using a smartphone, the service provider will primarily provide information through short text and images. By considering the type of device the user is using and providing information in the most appropriate format, the service provider makes it easier for users to receive the information. For example, if a user is using a tablet, the service provider will provide detailed information and interactive content. This allows the service provider to deliver information in the most optimal way for each user's device.

[0093] The data collection unit can adjust the frequency of information collection based on the user's internet connection status. For example, if the user is using a high-speed internet connection, it will collect information more frequently. By considering the user's internet connection status and collecting information at the optimal frequency, the data collection unit enables the user to obtain information efficiently. For example, if the user is using a slow internet connection, the data collection unit will reduce the frequency of information collection. This allows the data collection unit to collect information at the optimal frequency according to the user's internet connection status.

[0094] The extraction unit can estimate the user's emotions and adjust the tone of the news extracted based on those emotions. For example, if the user is stressed, it will prioritize extracting positive news. The extraction unit improves the user's mood by estimating their emotions and prioritizing positive news if the user is stressed. If the user is relaxed, for example, the extraction unit will extract neutral news. This allows the extraction unit to extract news in the optimal tone according to the user's emotions.

[0095] The summarization section can estimate the user's emotions and adjust the style of the summary based on those emotions. For example, if the user is excited, it can provide a visually engaging summary. By estimating the user's emotions and providing a visually engaging summary when the user is excited, the summarization section can keep the user engaged. For example, if the user is calm, it can provide a detailed summary. This allows the summarization section to provide a summary in the most appropriate style according to the user's emotions.

[0096] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is busy, it can delay the delivery of information. By estimating the user's emotions and delaying information delivery when the user is busy, the information delivery unit reduces the user's burden. For example, if the user is relaxed, the information delivery unit can provide information immediately. In this way, the information delivery unit can provide information at the optimal timing according to the user's emotions.

[0097] The data collection unit can estimate the user's emotions and adjust the types of information it collects based on those emotions. For example, if the user is sad, it prioritizes collecting encouraging messages and positive news. By estimating the user's emotions and prioritizing the collection of encouraging messages and positive news if the user is sad, the data collection unit improves the user's mood. For example, if the user is excited, the data collection unit collects entertainment-related information. This allows the data collection unit to collect the most relevant information according to the user's emotions.

[0098] The information provider can estimate the user's emotions and adjust the format of information delivery based on those emotions. For example, if the user is relaxed, the information provider will deliver it in a format that includes a lot of visual content. By estimating the user's emotions and delivering information in a format that includes a lot of visual content when the user is relaxed, the information provider makes it easier for the user to receive the information. If the user is in a hurry, for example, the information provider will deliver it in a concise text format. In this way, the information provider can deliver information in the most optimal format according to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects news information that the user has already read. For example, the data collection unit collects news links sent by friends via messenger apps and information on articles already viewed in news apps. The data collection unit centrally manages this information and understands which news articles the user has already read. Step 2: The extraction unit extracts topics that the user has not yet read, based on the read information collected by the collection unit. For example, the extraction unit identifies unread news articles in news apps or unread news links in messenger apps. Step 3: The summarization unit summarizes the topics extracted by the extraction unit. For example, the summarization unit analyzes the content of unread news articles or news links, extracts key points, and creates a summary. It uses a generation AI to condense long news articles into short summaries. Step 4: The provider unit provides the user with the information summarized by the summary unit. The provider unit provides the summarized information to the user, for example, by using the notification function of a news app or messenger app. This ensures that the user can obtain the information efficiently by providing the summarized information quickly.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, extraction unit, summarization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects read information from messenger apps and news apps. The extraction unit is implemented by the identification processing unit 290 of the data processing device 12 and extracts unread topics based on the collected read information. The summarization unit is implemented by the identification processing unit 290 of the data processing device 12 and summarizes the extracted topics. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the summarized information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, extraction unit, summarization unit, and provision 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 read information from messenger apps and news apps. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts unread topics based on the collected read information. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the extracted topics. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the summarized information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, extraction unit, summarization unit, and provision 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 read information from messenger and news applications. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts unread topics based on the collected read information. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the extracted topics. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the summarized information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, extraction unit, summarization unit, and provision unit, is implemented, for example, by 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 read information from messenger apps and news apps. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts unread topics based on the collected read information. The summarization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and summarizes the extracted topics. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the summarized information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection unit that collects read receipts, An extraction unit extracts unread topics based on the read information collected by the aforementioned collection unit, A summarization unit that summarizes the topics extracted by the extraction unit, The system comprises a providing unit that provides the information summarized by the summarizing unit. A system characterized by the following features. (Note 2) The summary section above is, Analyze the content of unread news articles or news links, extract key points, and create a summary. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Use the notification feature of news or messenger apps to provide users with summarized information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information from news links sent by friends via messenger apps or articles already viewed in news apps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The extraction unit is Identify unread news articles in news apps or unread news links in messenger apps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting read receipts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past browsing history and select a data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting read information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the read information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting read information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting read receipts, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The extraction unit is The system estimates the user's emotions and adjusts the criteria for extracting unread topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The extraction unit is During extraction, improve the accuracy of the extraction based on the relationships between news articles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is During extraction, a different extraction algorithm is applied for each category of news articles. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is It estimates the user's emotions and determines the priority of topics to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is During extraction, the extraction is performed based on the geographical distribution of news articles. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is During extraction, improve the accuracy of the extraction based on related literature for news articles. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, When generating summaries, adjust the level of detail in the summary based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, When generating summaries, prioritize summaries based on when the news articles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, the method of delivery is selected based on the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the method of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the method of delivery will be selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and propose methods for providing the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system comprising a server device and a terminal device used by a user, A collection unit that acquires and centrally manages read information indicating news articles and news links already viewed by the user from messenger and news apps installed on the terminal device, An extraction unit identifies and extracts news articles and news links that are not included in the read information managed by the collection unit, from among the news articles and news links obtainable from the news app and the messenger app, as unread topics. A summarization unit that summarizes the unread topics extracted by the extraction unit, The system comprises a providing unit that provides information summarized by the summarizing unit, The summarization unit estimates the user's emotions and adjusts the length of the summary based on the estimated emotions, providing a short, to-the-point summary if the user is in a hurry, and a longer summary with more detailed explanations if the user is relaxed. A system characterized by the following features.

2. The summary section above is, The content of the unread news articles or news links, which are the extracted unread topics, is analyzed, and the key points are extracted to create the summary. The system according to feature 1.

3. The aforementioned supply unit is, The summarized information is provided to the user using the notification function of the news app or messenger app on the terminal device. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the user's emotions and, based on the estimated emotions, adjusts the timing of acquiring the read receipt information so that it delays the acquisition of the read receipt information if the user is experiencing stress. The system according to feature 1.

5. The aforementioned collection unit is The system analyzes the user's past browsing history and selects a method for acquiring read information, prioritizing news categories that the user frequently views. The system according to feature 1.

6. The aforementioned collection unit is When acquiring the aforementioned read information, filtering is performed to prioritize the acquisition of read information related to the user's current areas of interest. The system according to feature 1.

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