system

Abstract

The system according to this embodiment aims to provide a customized news summary based on the user's interests and browsing history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a news summary based on the analysis results obtained by the analysis unit. The provision unit provides the news summary generated by the generation unit as a video.

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 conventional technology, customized news provision based on a user's interests and browsing history has not been sufficiently performed, and there is room for improvement.

[0005] The system according to an embodiment aims to provide a summary of news customized based on a user's interests and browsing history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates news summaries based on the analysis results obtained by the analysis unit. The provision unit provides the news summaries generated by the generation unit as video. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized news summary based on the user's interests and browsing history. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 three or more matters are expressed by connecting them with "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The news summary provision system according to an embodiment of the present invention is a system that provides a one-minute video of a news summary customized based on the user's interests and browsing history. This system is an AI service that provides a one-minute video of a news summary customized based on the user's interests and browsing history. Users can select a news digest that suits the time of day, such as "morning news digest" or "midday news digest," and efficiently gather information. Specifically, it consists of the following steps. First, the AI ​​collects the user's interests and browsing history and analyzes this data. Next, based on the analysis results, it generates a news summary that is optimal for the user. This summary is provided as a one-minute video. Users can select a news digest that suits the time of day, such as morning or midday, and efficiently gather information. For example, the system collects the user's interests and browsing history. For example, it collects data such as news articles the user has viewed in the past and topics that the user has shown interest in. This data is input to the AI. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the user's interests and browsing history and generates a news summary that is optimal for the user. For example, if the user is interested in politics, the AI ​​prioritizes summarizing politics-related news. The generated news summaries are provided as one-minute videos. Users can select news digests according to the time of day, such as morning or midday. For example, they can select the "Morning News Digest" during their morning commute and the "Midday News Digest" during their lunch break. This system allows users to efficiently gather information. It is an extremely convenient service for busy business people and users who want to efficiently process information. Furthermore, because AI analyzes user data and generates customized news videos, it is possible to provide information tailored to the user's interests. In addition, it integrates information from multiple news sources and simplifies the information through visual summaries. This prevents confusion due to information overload, and users can efficiently obtain the necessary information in a short amount of time.This innovative news service will enable users to efficiently acquire information and deepen their knowledge, leading to a society where more informed decision-making is possible. The news summary system will efficiently collect information by providing personalized news summaries based on the user's interests and browsing history.

[0029] The news summary provision system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's interests and browsing history. For example, the collection unit collects data such as news articles the user has previously viewed and topics the user has shown interest in. The collection unit can also use AI to analyze the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques and machine learning algorithms to analyze the user's interests and browsing history. The analysis unit can also use AI to generate news summaries based on the user's interests. The generation unit generates news summaries based on the analysis results obtained by the analysis unit. For example, the generation unit generates news summaries based on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The provision unit provides the news summary generated by the generation unit as a video. For example, the provision unit provides a news digest according to the time period selected by the user. The delivery unit can use AI to integrate information from multiple news sources and simplify the information through visual summaries. This allows the news summary delivery system to efficiently collect information by providing customized news summaries based on the user's interests and browsing history. For example, the collection unit collects the user's interests and browsing history. The collection unit collects data such as news articles the user has previously viewed and topics they have shown interest in. The collection unit can also use AI to analyze the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's interests and browsing history using data mining techniques and machine learning algorithms, for example. The analysis unit can also use AI to generate news summaries based on the user's interests. The generation unit generates news summaries based on the analysis results obtained by the analysis unit. The generation unit generates news summaries based on factors such as the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The delivery unit provides the news summary generated by the generation unit as a video.The service provider, for example, delivers news digests tailored to the user's selected time slot. The service provider can also use AI to integrate information from multiple news sources and simplify the information through visual summaries. This allows the news summary system to efficiently collect information by providing customized news summaries based on the user's interests and browsing history.

[0030] The data collection unit collects user interests and browsing history. For example, it collects data such as news articles that users have previously viewed and topics they have shown interest in. Specifically, it collects detailed data such as URLs of articles viewed by users on news sites and applications, viewing time, links clicked, and bookmarked articles. Furthermore, it also collects news articles that users have shared on social media, the content of their comments, and posts they have liked. This data is important for accurately understanding user interests and preferences. The data collection unit can also use AI to analyze user interests and browsing history. The AI ​​uses natural language processing technology to analyze the content of collected news articles and extract topics and keywords. This allows for a detailed analysis of what themes users are interested in. In addition, the AI ​​can learn user browsing patterns and detect changes in interests and new interests in real time. For example, if a user has recently been frequently viewing articles on a particular topic, the system can be adjusted to prioritize the collection of news related to that topic. This allows the data collection unit to achieve personalized news collection based on user interests and preferences, providing users with valuable information.

[0031] The analysis department analyzes the data collected by the collection department. For example, the analysis department uses data mining techniques and machine learning algorithms to analyze user interests and browsing history. Specifically, it uses clustering algorithms to classify user interests into multiple categories and evaluate the user's level of interest within each category. Furthermore, it can use collaborative filtering techniques to recommend the most relevant news to individual users, referencing data from other users with similar interests. The analysis department can also use AI to generate news summaries based on user interests. The AI ​​uses natural language generation technology to summarize the content of collected news articles and extract key information. For example, the AI ​​analyzes the article title, lead paragraph, and main points of the body text to generate a summary that allows users to quickly grasp the most important information. In addition, the AI ​​can customize the content of the summary based on the user's past browsing history and interests. For example, if a user is interested in economic news, the summary can be adjusted to prioritize economic-related information. This allows the analysis department to provide highly accurate news summaries based on user interests, helping users efficiently gather information.

[0032] The generation unit generates news summaries based on the analysis results obtained by the analysis unit. The generation unit generates news summaries based, for example, on the length of the summary and the importance of the information being summarized. Specifically, the generation unit can adjust the length of the summary according to the user's settings and preferences. For example, it can provide a concise summary containing only the main points to users who prefer shorter summaries, and a longer summary containing more information to users who seek detailed information. The generation unit can also use AI to provide news summaries as one-minute videos. The AI ​​combines natural language generation and video generation technologies to visually represent news article summaries. For example, the AI ​​can display the main points of a news article as text and combine them with relevant images and video clips to generate a visually engaging summary video. Furthermore, the AI ​​can use speech synthesis technology to read the summary aloud. This allows users to efficiently understand news summaries in a short amount of time through both video and audio. By providing personalized summaries based on user interests, the generation unit supports users' information gathering and deepens their understanding of the news.

[0033] The delivery unit provides news summaries generated by the generation unit as videos. For example, the delivery unit can provide news digests tailored to the time slot selected by the user. Specifically, the delivery unit can automatically deliver news summary videos based on notification times set by the user. For example, if a user wants to check the news during their morning commute, the delivery unit can deliver a news digest at a specific time in the morning, allowing the user to efficiently check the latest news during their commute. The delivery unit can also use AI to integrate information from multiple news sources and simplify information through visual summaries. The AI ​​analyzes articles collected from different news sources, eliminates redundant information, and extracts and integrates the most important information. This eliminates the need for users to individually check multiple news sources, allowing them to efficiently obtain information from a single summary video. Furthermore, the delivery unit can collect user feedback and continuously improve the accuracy and content of the news summaries it provides. For example, by allowing users to rate and comment on the summaries provided, the system can more accurately understand the user's preferences and interests and reflect them in future summary generation. This enables the delivery unit to provide the most suitable news summaries for users and improve the efficiency of information gathering.

[0034] The data collection unit can collect data on the user's past browsing history and topics of interest. For example, the data collection unit can collect data such as news articles the user has previously viewed and topics of interest. The data collection unit can also use AI to analyze the user's interests and browsing history. For example, the data collection unit can collect data such as links the user has clicked, search keywords, and browsing time. The data collection unit can also use AI to identify the user's interests. This allows the data collection unit to provide more customized news summaries by collecting data on the user's past browsing history and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past browsing history into AI, which can then identify topics of interest.

[0035] The analysis unit can analyze collected data and generate news summaries based on user interests. For example, the analysis unit can analyze user interests and browsing history using data mining techniques and machine learning algorithms. The analysis unit can also use AI to generate news summaries based on user interests. For example, the analysis unit can analyze data such as news articles previously viewed by the user and topics they have shown interest in. The analysis unit can also use AI to identify user interests and generate news summaries. This allows the analysis unit to provide users with highly relevant information by generating news summaries based on their interests. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which can then identify user interests and generate news summaries.

[0036] The generation unit can provide the generated news summary as a one-minute video. The generation unit generates news summaries based, for example, on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The generation unit can, for example, set the video format, the method of summarizing the content, and the visual elements in order to provide the news summary as a video. The generation unit can also use AI to select the optimal method for providing the news summary as a video. This allows the generation unit to provide the news summary as a one-minute video, enabling users to efficiently gather information in a short amount of time. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the news summary into AI, which can then select the optimal method for providing it as a video.

[0037] The service provider can deliver news digests according to the time slot selected by the user. For example, the service provider can deliver news digests according to the time slot selected by the user. The service provider can also use AI to deliver information tailored to the user's lifestyle. For example, the service provider can allow the user to select "Morning News Digest" during their morning commute and "Lunchtime News Digest" during their lunch break. The service provider can also use AI to deliver the most suitable news digest for the time slot selected by the user. In this way, by delivering news digests according to the time slot selected by the user, the service provider can deliver information tailored to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the time slot selected by the user into the AI, and the AI ​​can provide the most suitable news digest.

[0038] The information provider can integrate information from multiple news sources and simplify the information through visual summarization. For example, the information provider can integrate information from multiple news sources and simplify the information through visual summarization. The information provider can also use AI to integrate information from multiple news sources and simplify the information through visual summarization. For example, the information provider can simplify information using visual elements such as graphs, charts, and icons. The information provider can also use AI to select the optimal method for visual summarization. As a result, by integrating information from multiple news sources and simplifying the information through visual summarization, the information provider can enable users to obtain the necessary information quickly and efficiently. Some or all of the above-described processes in the information provider may be performed using AI or not. For example, the information provider can input information from multiple news sources into AI, which can then select the optimal method for visual summarization.

[0039] The data collection unit can analyze a user's past browsing history and select the optimal data collection method. For example, the data collection unit can identify the times of day when a user frequently browses and collect data during those times. The data collection unit can also use AI to analyze a user's past browsing history. For example, if a user is using a specific device, the data collection unit can collect data in a way optimized for that device. The data collection unit can also use AI to select the optimal data collection method. For example, if a user shows interest in a particular topic, the data collection unit can prioritize collecting data related to that topic. This allows the data collection unit to select the optimal data collection method by analyzing a user's past browsing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's past browsing history into an AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest when collecting browsing history. For example, the data collection unit can collect only data related to topics the user is currently interested in. The data collection unit can also use AI to identify the user's current areas of interest. For example, if the user is interested in a particular category, the data collection unit will prioritize collecting data related to that category. The data collection unit can also use AI to perform filtering. For example, if the user is searching for a particular keyword, the data collection unit will collect data related to that keyword. This allows the data collection unit to collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into the AI, which can then perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting browsing history. For example, the data collection unit can prioritize the collection of news related to the user's current location. The data collection unit can also use AI to identify the user's geographical location. For example, if the user is traveling, the data collection unit can prioritize the collection of news related to their travel destination. The data collection unit can also use AI to prioritize the collection of highly relevant data. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of news related to that event. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting browsing history. For example, the data collection unit can collect data related to news that the user has shared on social media. The data collection unit can also use AI to analyze the user's social media activity. For example, the data collection unit can collect news related to accounts that the user follows on social media. The data collection unit can also use AI to collect relevant data. For example, the data collection unit can collect data related to posts that the user has "liked" on social media. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. The analysis unit can also use AI to assess the importance of the data. For example, the analysis unit will perform a concise analysis on data with low importance. The analysis unit can also use AI to adjust the level of detail of the analysis. For example, the analysis unit will perform an analysis with a moderate level of detail on data with moderate importance. In this way, the analysis unit can perform an analysis with an appropriate level of detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specialized political analysis algorithm to political news. The analysis unit can also use AI to identify data categories. For example, the analysis unit applies a specialized economic analysis algorithm to economic news. The analysis unit can also use AI to apply different analysis algorithms. For example, the analysis unit applies a specialized sports analysis algorithm to sports news. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data categories into the AI, and the AI ​​can apply different analysis algorithms.

[0045] The analysis department can determine the priority of analysis based on when the data was collected. For example, the analysis department might prioritize the analysis of the most recent data. The analysis department can also use AI to identify when the data was collected. For example, the analysis department might analyze current data while referring to past data. The analysis department can also use AI to determine the priority of analysis. For example, the analysis department might prioritize the analysis of data collected during a specific period. This allows the analysis department to prioritize the analysis of the most recent information by determining the priority of analysis based on when the data was collected. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the data collection dates into the AI, and the AI ​​can determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data. The analysis unit can also use AI to evaluate the relevance of the data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit can also use AI to adjust the order of analysis. For example, the analysis unit may analyze data of moderate relevance appropriately. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0047] The generation unit can adjust the level of detail in news summaries based on the importance of the news when generating them. For example, the generation unit generates a detailed summary for news with high importance. The generation unit can also use AI to evaluate the importance of news. For example, the generation unit generates a concise summary for news with low importance. The generation unit can also use AI to adjust the level of detail in the summaries. For example, the generation unit generates a summary with appropriate detail for news with moderate importance. In this way, the generation unit can provide news summaries with appropriate detail by adjusting the level of detail in the summaries based on the importance of the news. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the news into the AI, and the AI ​​can adjust the level of detail in the summary.

[0048] The generation unit can apply different summarization algorithms depending on the news category when generating news summaries. For example, the generation unit can apply a specialized political summarization algorithm to political news. The generation unit can also use AI to identify the news category. For example, the generation unit can apply a specialized economic summarization algorithm to economic news. The generation unit can also use AI to apply different summarization algorithms. For example, the generation unit can apply a specialized sports summarization algorithm to sports news. In this way, the generation unit can provide more appropriate news summaries by applying different summarization algorithms depending on the news category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the news category into the AI, and the AI ​​can apply different summarization algorithms.

[0049] The generation unit can determine the priority of news summaries based on when the news was collected. For example, the generation unit prioritizes summarizing the most recent news. The generation unit can also use AI to identify when the news was collected. For example, the generation unit summarizes current news while referring to past news. The generation unit can also use AI to determine the priority of summaries. For example, the generation unit prioritizes summarizing news collected during a specific period. This allows the generation unit to prioritize summarizing the most recent information by determining the priority of summaries based on when the news was collected. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the news collection period into the AI, and the AI ​​can determine the priority of summaries.

[0050] The generation unit can adjust the order of news summaries based on their relevance when generating news summaries. For example, the generation unit prioritizes summarizing highly relevant news. The generation unit can also use AI to evaluate the relevance of news. For example, the generation unit postpones summarizing less relevant news. The generation unit can also use AI to adjust the order of summaries. For example, the generation unit moderately summarizes news of moderate relevance. In this way, the generation unit can prioritize summarizing highly relevant news by adjusting the order of summaries based on their relevance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of news into the AI, and the AI ​​can adjust the order of summaries.

[0051] The delivery unit can select the optimal delivery method when providing news digests by referring to the user's past selection history. For example, the delivery unit can select the optimal delivery method based on patterns of news digests previously selected by the user. The delivery unit can also analyze the user's past selection history using AI. For example, the delivery unit can select the optimal delivery method based on news digests selected by the user during a specific time period. The delivery unit can also select the optimal delivery method using AI. For example, the delivery unit can select the optimal delivery method based on news digests selected by the user on a specific device. In this way, the delivery unit can select the optimal delivery method by referring to the user's past selection history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past selection history into AI, and the AI ​​can select the optimal delivery method.

[0052] The delivery unit can customize the delivery method based on the user's current living situation when providing news digests. For example, if the user is commuting, the delivery unit can provide the news digest in audio format. The delivery unit can also use AI to identify the user's current living situation. For example, if the user is at home, the delivery unit can provide the news digest in video format. The delivery unit can also use AI to customize the delivery method. For example, if the user is exercising, the delivery unit can provide the news digest in short text format. This allows the delivery unit to provide more appropriate news digests by customizing the delivery method based on the user's current living situation. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's current living situation into the AI, which can then customize the delivery method.

[0053] The delivery unit can select the optimal delivery method when providing news digests, taking into account the user's geographical location information. For example, the delivery unit can prioritize providing news related to the user's current location. The delivery unit can also use AI to identify the user's geographical location information. For example, if the user is traveling, the delivery unit can prioritize providing news related to their travel destination. The delivery unit can also use AI to select the optimal delivery method. For example, if the user is attending a specific event, the delivery unit can prioritize providing news related to that event. In this way, the delivery unit can select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's geographical location information into AI, and the AI ​​can select the optimal delivery method.

[0054] The delivery unit can analyze the user's social media activity and suggest delivery methods when providing news digests. For example, the delivery unit can provide news digests related to news shared by the user on social media. The delivery unit can also use AI to analyze the user's social media activity. For example, the delivery unit can provide news digests related to accounts followed by the user on social media. The delivery unit can also use AI to suggest delivery methods. For example, the delivery unit can provide news digests related to posts liked by the user on social media. In this way, the delivery unit can suggest the optimal delivery method by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's social media activity into AI, and the AI ​​can suggest the optimal delivery method.

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

[0056] The data collection unit can collect not only user interests and browsing history, but also the content of newsletters and email magazines that users subscribe to. For example, if a user subscribes to a specific newsletter, the data collection unit can collect the content of that newsletter to gain a more accurate understanding of the user's interests. Similarly, if a user subscribes to an email magazine, the data collection unit can collect its content to generate news summaries based on the user's interests. This allows the data collection unit to provide more customized news summaries by considering not only the user's interests and browsing history, but also the content of the newsletters and email magazines they subscribe to.

[0057] The analytics department can also consider users' social media activity when generating news summaries based on their interests and browsing history. For example, by analyzing news articles that users have shared on social media and posts from accounts they follow, the analytics department can more accurately understand users' interests. Furthermore, by analyzing posts that users have "liked" and articles they have commented on on social media, the analytics department can generate news summaries that reflect users' interests. In this way, the analytics department can provide more relevant news summaries by considering users' social media activity.

[0058] The generation unit can provide news summaries as one-minute videos in a format optimized for the user's device. For example, if the user is using a smartphone, it can select a video format that matches the smartphone's screen size. If the user is using a tablet, it can provide a video format optimized for the tablet's screen size. Furthermore, if the user is using a desktop computer, it can provide a video format optimized for a large screen. As a result, the generation unit can provide news summaries in a video format optimized for the user's device, thereby providing a more comfortable viewing experience.

[0059] The delivery department can take the user's calendar information into consideration when providing news digests at times selected by the user. For example, if the user has an appointment on their calendar, the timing of news digest delivery can be adjusted to match that appointment. Furthermore, if the user is in a specific situation, such as being in a meeting or traveling, the delivery method of the news digest can be changed accordingly. In addition, news related to specific events can be prioritized based on the user's calendar. This allows the delivery department to provide news digests at a more appropriate time by considering the user's calendar information.

[0060] The news provider can evaluate the reliability of news when integrating information from multiple news sources. For example, it can use an algorithm to evaluate the reliability of news sources and prioritize the integration of information from reliable sources. Furthermore, the provider can fact-check the content of news and eliminate misinformation and fake news. In addition, the provider can provide users with information on news reliability, enabling them to select reliable information. In this way, by evaluating the reliability of news, the provider can provide users with reliable news digests.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user interests and browsing history. For example, it collects data such as news articles the user has previously viewed and topics they have shown interest in. The data collection unit can also use AI to analyze user interests and browsing history. Step 2: The analysis department analyzes the data collected by the collection department. For example, it uses data mining techniques and machine learning algorithms to analyze user interests and browsing history. The analysis department can also use AI to generate news summaries based on user interests. Step 3: The generation unit generates a news summary based on the analysis results obtained by the analysis unit. For example, it generates news summaries based on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. Step 4: The delivery unit provides a video summary of the news generated by the generation unit. For example, it can provide a news digest tailored to a time period selected by the user. The delivery unit can also use AI to integrate information from multiple news sources and simplify the information through visual summaries.

[0063] (Example of form 2)The news summary provision system according to an embodiment of the present invention is a system that provides a one-minute video of a news summary customized based on the user's interests and browsing history. This system is an AI service that provides a one-minute video of a news summary customized based on the user's interests and browsing history. Users can select a news digest that suits the time of day, such as "morning news digest" or "midday news digest," and efficiently gather information. Specifically, it consists of the following steps. First, the AI ​​collects the user's interests and browsing history and analyzes this data. Next, based on the analysis results, it generates a news summary that is optimal for the user. This summary is provided as a one-minute video. Users can select a news digest that suits the time of day, such as morning or midday, and efficiently gather information. For example, the system collects the user's interests and browsing history. For example, it collects data such as news articles the user has viewed in the past and topics that the user has shown interest in. This data is input to the AI. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the user's interests and browsing history and generates a news summary that is optimal for the user. For example, if the user is interested in politics, the AI ​​prioritizes summarizing politics-related news. The generated news summaries are provided as one-minute videos. Users can select news digests according to the time of day, such as morning or midday. For example, they can select the "Morning News Digest" during their morning commute and the "Midday News Digest" during their lunch break. This system allows users to efficiently gather information. It is an extremely convenient service for busy business people and users who want to efficiently process information. Furthermore, because AI analyzes user data and generates customized news videos, it is possible to provide information tailored to the user's interests. In addition, it integrates information from multiple news sources and simplifies the information through visual summaries. This prevents confusion due to information overload, and users can efficiently obtain the necessary information in a short amount of time.This innovative news service will enable users to efficiently acquire information and deepen their knowledge, leading to a society where more informed decision-making is possible. The news summary system will efficiently collect information by providing personalized news summaries based on the user's interests and browsing history.

[0064] The news summary provision system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's interests and browsing history. For example, the collection unit collects data such as news articles the user has previously viewed and topics the user has shown interest in. The collection unit can also use AI to analyze the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques and machine learning algorithms to analyze the user's interests and browsing history. The analysis unit can also use AI to generate news summaries based on the user's interests. The generation unit generates news summaries based on the analysis results obtained by the analysis unit. For example, the generation unit generates news summaries based on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The provision unit provides the news summary generated by the generation unit as a video. For example, the provision unit provides a news digest according to the time period selected by the user. The delivery unit can use AI to integrate information from multiple news sources and simplify the information through visual summaries. This allows the news summary delivery system to efficiently collect information by providing customized news summaries based on the user's interests and browsing history. For example, the collection unit collects the user's interests and browsing history. The collection unit collects data such as news articles the user has previously viewed and topics they have shown interest in. The collection unit can also use AI to analyze the user's interests and browsing history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the user's interests and browsing history using data mining techniques and machine learning algorithms, for example. The analysis unit can also use AI to generate news summaries based on the user's interests. The generation unit generates news summaries based on the analysis results obtained by the analysis unit. The generation unit generates news summaries based on factors such as the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The delivery unit provides the news summary generated by the generation unit as a video.The service provider, for example, delivers news digests tailored to the user's selected time slot. The service provider can also use AI to integrate information from multiple news sources and simplify the information through visual summaries. This allows the news summary system to efficiently collect information by providing customized news summaries based on the user's interests and browsing history.

[0065] The data collection unit collects user interests and browsing history. For example, it collects data such as news articles that users have previously viewed and topics they have shown interest in. Specifically, it collects detailed data such as URLs of articles viewed by users on news sites and applications, viewing time, links clicked, and bookmarked articles. Furthermore, it also collects news articles that users have shared on social media, the content of their comments, and posts they have liked. This data is important for accurately understanding user interests and preferences. The data collection unit can also use AI to analyze user interests and browsing history. The AI ​​uses natural language processing technology to analyze the content of collected news articles and extract topics and keywords. This allows for a detailed analysis of what themes users are interested in. In addition, the AI ​​can learn user browsing patterns and detect changes in interests and new interests in real time. For example, if a user has recently been frequently viewing articles on a particular topic, the system can be adjusted to prioritize the collection of news related to that topic. This allows the data collection unit to achieve personalized news collection based on user interests and preferences, providing users with valuable information.

[0066] The analysis department analyzes the data collected by the collection department. For example, the analysis department uses data mining techniques and machine learning algorithms to analyze user interests and browsing history. Specifically, it uses clustering algorithms to classify user interests into multiple categories and evaluate the user's level of interest within each category. Furthermore, it can use collaborative filtering techniques to recommend the most relevant news to individual users, referencing data from other users with similar interests. The analysis department can also use AI to generate news summaries based on user interests. The AI ​​uses natural language generation technology to summarize the content of collected news articles and extract key information. For example, the AI ​​analyzes the article title, lead paragraph, and main points of the body text to generate a summary that allows users to quickly grasp the most important information. In addition, the AI ​​can customize the content of the summary based on the user's past browsing history and interests. For example, if a user is interested in economic news, the summary can be adjusted to prioritize economic-related information. This allows the analysis department to provide highly accurate news summaries based on user interests, helping users efficiently gather information.

[0067] The generation unit generates news summaries based on the analysis results obtained by the analysis unit. The generation unit generates news summaries based, for example, on the length of the summary and the importance of the information being summarized. Specifically, the generation unit can adjust the length of the summary according to the user's settings and preferences. For example, it can provide a concise summary containing only the main points to users who prefer shorter summaries, and a longer summary containing more information to users who seek detailed information. The generation unit can also use AI to provide news summaries as one-minute videos. The AI ​​combines natural language generation and video generation technologies to visually represent news article summaries. For example, the AI ​​can display the main points of a news article as text and combine them with relevant images and video clips to generate a visually engaging summary video. Furthermore, the AI ​​can use speech synthesis technology to read the summary aloud. This allows users to efficiently understand news summaries in a short amount of time through both video and audio. By providing personalized summaries based on user interests, the generation unit supports users' information gathering and deepens their understanding of the news.

[0068] The delivery unit provides news summaries generated by the generation unit as videos. For example, the delivery unit can provide news digests tailored to the time slot selected by the user. Specifically, the delivery unit can automatically deliver news summary videos based on notification times set by the user. For example, if a user wants to check the news during their morning commute, the delivery unit can deliver a news digest at a specific time in the morning, allowing the user to efficiently check the latest news during their commute. The delivery unit can also use AI to integrate information from multiple news sources and simplify information through visual summaries. The AI ​​analyzes articles collected from different news sources, eliminates redundant information, and extracts and integrates the most important information. This eliminates the need for users to individually check multiple news sources, allowing them to efficiently obtain information from a single summary video. Furthermore, the delivery unit can collect user feedback and continuously improve the accuracy and content of the news summaries it provides. For example, by allowing users to rate and comment on the summaries provided, the system can more accurately understand the user's preferences and interests and reflect them in future summary generation. This enables the delivery unit to provide the most suitable news summaries for users and improve the efficiency of information gathering.

[0069] The data collection unit can collect data on the user's past browsing history and topics of interest. For example, the data collection unit can collect data such as news articles the user has previously viewed and topics of interest. The data collection unit can also use AI to analyze the user's interests and browsing history. For example, the data collection unit can collect data such as links the user has clicked, search keywords, and browsing time. The data collection unit can also use AI to identify the user's interests. This allows the data collection unit to provide more customized news summaries by collecting data on the user's past browsing history and topics of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past browsing history into AI, which can then identify topics of interest.

[0070] The analysis unit can analyze collected data and generate news summaries based on user interests. For example, the analysis unit can analyze user interests and browsing history using data mining techniques and machine learning algorithms. The analysis unit can also use AI to generate news summaries based on user interests. For example, the analysis unit can analyze data such as news articles previously viewed by the user and topics they have shown interest in. The analysis unit can also use AI to identify user interests and generate news summaries. This allows the analysis unit to provide users with highly relevant information by generating news summaries based on their interests. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which can then identify user interests and generate news summaries.

[0071] The generation unit can provide the generated news summary as a one-minute video. The generation unit generates news summaries based, for example, on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. The generation unit can, for example, set the video format, the method of summarizing the content, and the visual elements in order to provide the news summary as a video. The generation unit can also use AI to select the optimal method for providing the news summary as a video. This allows the generation unit to provide the news summary as a one-minute video, enabling users to efficiently gather information in a short amount of time. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the news summary into AI, which can then select the optimal method for providing it as a video.

[0072] The service provider can deliver news digests according to the time slot selected by the user. For example, the service provider can deliver news digests according to the time slot selected by the user. The service provider can also use AI to deliver information tailored to the user's lifestyle. For example, the service provider can allow the user to select "Morning News Digest" during their morning commute and "Lunchtime News Digest" during their lunch break. The service provider can also use AI to deliver the most suitable news digest for the time slot selected by the user. In this way, by delivering news digests according to the time slot selected by the user, the service provider can deliver information tailored to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the time slot selected by the user into the AI, and the AI ​​can provide the most suitable news digest.

[0073] The information provider can integrate information from multiple news sources and simplify the information through visual summarization. For example, the information provider can integrate information from multiple news sources and simplify the information through visual summarization. The information provider can also use AI to integrate information from multiple news sources and simplify the information through visual summarization. For example, the information provider can simplify information using visual elements such as graphs, charts, and icons. The information provider can also use AI to select the optimal method for visual summarization. As a result, by integrating information from multiple news sources and simplifying the information through visual summarization, the information provider can enable users to obtain the necessary information quickly and efficiently. Some or all of the above-described processes in the information provider may be performed using AI or not. For example, the information provider can input information from multiple news sources into AI, which can then select the optimal method for visual summarization.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. The data collection unit can also use AI to estimate the user's emotions. For example, if the user is excited, the data collection unit can collect data immediately to reflect real-time interests. The data collection unit can also use AI to adjust the collection timing based on the user's emotions. For example, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. In this way, the data collection unit can collect data at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the AI, which can then adjust the timing of the data collection.

[0075] The data collection unit can analyze a user's past browsing history and select the optimal data collection method. For example, the data collection unit can identify the times of day when a user frequently browses and collect data during those times. The data collection unit can also use AI to analyze a user's past browsing history. For example, if a user is using a specific device, the data collection unit can collect data in a way optimized for that device. The data collection unit can also use AI to select the optimal data collection method. For example, if a user shows interest in a particular topic, the data collection unit can prioritize collecting data related to that topic. This allows the data collection unit to select the optimal data collection method by analyzing a user's past browsing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's past browsing history into an AI, which can then select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current areas of interest when collecting browsing history. For example, the data collection unit can collect only data related to topics the user is currently interested in. The data collection unit can also use AI to identify the user's current areas of interest. For example, if the user is interested in a particular category, the data collection unit will prioritize collecting data related to that category. The data collection unit can also use AI to perform filtering. For example, if the user is searching for a particular keyword, the data collection unit will collect data related to that keyword. This allows the data collection unit to collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into the AI, which can then perform the filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to topics of interest. The data collection unit can also use AI to estimate the user's emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to relaxing content. The data collection unit can also use AI to prioritize the data to collect. For example, if the user is excited, the data collection unit will prioritize collecting data related to stimulating content. This allows the data collection unit to collect more appropriate data by prioritizing data based on 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) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting browsing history. For example, the data collection unit can prioritize the collection of news related to the user's current location. The data collection unit can also use AI to identify the user's geographical location. For example, if the user is traveling, the data collection unit can prioritize the collection of news related to their travel destination. The data collection unit can also use AI to prioritize the collection of highly relevant data. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of news related to that event. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data when collecting browsing history. For example, the data collection unit can collect data related to news that the user has shared on social media. The data collection unit can also use AI to analyze the user's social media activity. For example, the data collection unit can collect news related to accounts that the user follows on social media. The data collection unit can also use AI to collect relevant data. For example, the data collection unit can collect data related to posts that the user has "liked" on social media. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. The analysis unit can also use AI to estimate the user's emotions. For example, if the user is in a hurry, the analysis unit can perform a concise analysis and provide essential information. The analysis unit can also use AI to adjust the data analysis method. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis method based on 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) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the data analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. The analysis unit can also use AI to assess the importance of the data. For example, the analysis unit will perform a concise analysis on data with low importance. The analysis unit can also use AI to adjust the level of detail of the analysis. For example, the analysis unit will perform an analysis with a moderate level of detail on data with moderate importance. In this way, the analysis unit can perform an analysis with an appropriate level of detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specialized political analysis algorithm to political news. The analysis unit can also use AI to identify data categories. For example, the analysis unit applies a specialized economic analysis algorithm to economic news. The analysis unit can also use AI to apply different analysis algorithms. For example, the analysis unit applies a specialized sports analysis algorithm to sports news. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data categories into the AI, and the AI ​​can apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize analyzing topics of interest. The analysis unit can also use AI to estimate the user's emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing relaxing content. The analysis unit can also use AI to determine the priority of analysis. For example, if the user is excited, the analysis unit will prioritize analyzing stimulating content. This allows the analysis unit to provide more appropriate analysis results by determining the priority of analysis based on 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) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can then determine the priority of analysis.

[0084] The analysis department can determine the priority of analysis based on when the data was collected. For example, the analysis department might prioritize the analysis of the most recent data. The analysis department can also use AI to identify when the data was collected. For example, the analysis department might analyze current data while referring to past data. The analysis department can also use AI to determine the priority of analysis. For example, the analysis department might prioritize the analysis of data collected during a specific period. This allows the analysis department to prioritize the analysis of the most recent information by determining the priority of analysis based on when the data was collected. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the data collection dates into the AI, and the AI ​​can determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data. The analysis unit can also use AI to evaluate the relevance of the data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit can also use AI to adjust the order of analysis. For example, the analysis unit may analyze data of moderate relevance appropriately. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0086] The generation unit can estimate the user's emotions and adjust the news summary generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a detailed news summary. The generation unit can also estimate the user's emotions using AI. For example, if the user is in a hurry, the generation unit will generate a concise news summary. The generation unit can also adjust the news summary generation method using AI. For example, if the user is excited, the generation unit will generate a visually appealing news summary. In this way, the generation unit can provide more appropriate news summaries by adjusting the news summary generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, and the AI ​​can adjust the news summary generation method.

[0087] The generation unit can adjust the level of detail in news summaries based on the importance of the news when generating them. For example, the generation unit generates a detailed summary for news with high importance. The generation unit can also use AI to evaluate the importance of news. For example, the generation unit generates a concise summary for news with low importance. The generation unit can also use AI to adjust the level of detail in the summaries. For example, the generation unit generates a summary with appropriate detail for news with moderate importance. In this way, the generation unit can provide news summaries with appropriate detail by adjusting the level of detail in the summaries based on the importance of the news. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the news into the AI, and the AI ​​can adjust the level of detail in the summary.

[0088] The generation unit can apply different summarization algorithms depending on the news category when generating news summaries. For example, the generation unit can apply a specialized political summarization algorithm to political news. The generation unit can also use AI to identify the news category. For example, the generation unit can apply a specialized economic summarization algorithm to economic news. The generation unit can also use AI to apply different summarization algorithms. For example, the generation unit can apply a specialized sports summarization algorithm to sports news. In this way, the generation unit can provide more appropriate news summaries by applying different summarization algorithms depending on the news category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the news category into the AI, and the AI ​​can apply different summarization algorithms.

[0089] The generation 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 relaxed, the generation unit will generate a longer summary. The generation unit can also use AI to estimate the user's emotions. For example, if the user is in a hurry, the generation unit will generate a shorter summary. The generation unit can also use AI to adjust the length of the summary. For example, if the user is excited, the generation unit will generate a visually appealing summary. In this way, the generation unit can provide more appropriate news summaries by adjusting the length of the summary based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can then adjust the length of the summary.

[0090] The generation unit can determine the priority of news summaries based on when the news was collected. For example, the generation unit prioritizes summarizing the most recent news. The generation unit can also use AI to identify when the news was collected. For example, the generation unit summarizes current news while referring to past news. The generation unit can also use AI to determine the priority of summaries. For example, the generation unit prioritizes summarizing news collected during a specific period. This allows the generation unit to prioritize summarizing the most recent information by determining the priority of summaries based on when the news was collected. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the news collection period into the AI, and the AI ​​can determine the priority of summaries.

[0091] The generation unit can adjust the order of news summaries based on their relevance when generating news summaries. For example, the generation unit prioritizes summarizing highly relevant news. The generation unit can also use AI to evaluate the relevance of news. For example, the generation unit postpones summarizing less relevant news. The generation unit can also use AI to adjust the order of summaries. For example, the generation unit moderately summarizes news of moderate relevance. In this way, the generation unit can prioritize summarizing highly relevant news by adjusting the order of summaries based on their relevance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of news into the AI, and the AI ​​can adjust the order of summaries.

[0092] The delivery unit can estimate the user's emotions and adjust how the news digest is delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit will provide a detailed news digest. The delivery unit can also use AI to estimate the user's emotions. For example, if the user is in a hurry, the delivery unit will provide a concise news digest. The delivery unit can also use AI to adjust how the news digest is delivered. For example, if the user is excited, the delivery unit will provide a visually appealing news digest. In this way, the delivery unit can provide a more appropriate news digest by adjusting how it is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into AI, and the AI ​​can adjust how the news digest is delivered.

[0093] The delivery unit can select the optimal delivery method when providing news digests by referring to the user's past selection history. For example, the delivery unit can select the optimal delivery method based on patterns of news digests previously selected by the user. The delivery unit can also analyze the user's past selection history using AI. For example, the delivery unit can select the optimal delivery method based on news digests selected by the user during a specific time period. The delivery unit can also select the optimal delivery method using AI. For example, the delivery unit can select the optimal delivery method based on news digests selected by the user on a specific device. In this way, the delivery unit can select the optimal delivery method by referring to the user's past selection history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past selection history into AI, and the AI ​​can select the optimal delivery method.

[0094] The delivery unit can customize the delivery method based on the user's current living situation when providing news digests. For example, if the user is commuting, the delivery unit can provide the news digest in audio format. The delivery unit can also use AI to identify the user's current living situation. For example, if the user is at home, the delivery unit can provide the news digest in video format. The delivery unit can also use AI to customize the delivery method. For example, if the user is exercising, the delivery unit can provide the news digest in short text format. This allows the delivery unit to provide more appropriate news digests by customizing the delivery method based on the user's current living situation. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's current living situation into the AI, which can then customize the delivery method.

[0095] The service provider can estimate the user's emotions and prioritize news digests based on those emotions. For example, if the user is relaxed, the service provider will prioritize topics of interest. The service provider can also use AI to estimate the user's emotions. For example, if the user is stressed, the service provider will prioritize relaxing content. The service provider can also use AI to prioritize news digests. For example, if the user is excited, the service provider will prioritize stimulating content. This allows the service provider to provide more appropriate news digests by prioritizing them based on 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) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, which can then determine the priority of news digests.

[0096] The delivery unit can select the optimal delivery method when providing news digests, taking into account the user's geographical location information. For example, the delivery unit can prioritize providing news related to the user's current location. The delivery unit can also use AI to identify the user's geographical location information. For example, if the user is traveling, the delivery unit can prioritize providing news related to their travel destination. The delivery unit can also use AI to select the optimal delivery method. For example, if the user is attending a specific event, the delivery unit can prioritize providing news related to that event. In this way, the delivery unit can select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's geographical location information into AI, and the AI ​​can select the optimal delivery method.

[0097] The delivery unit can analyze the user's social media activity and suggest delivery methods when providing news digests. For example, the delivery unit can provide news digests related to news shared by the user on social media. The delivery unit can also use AI to analyze the user's social media activity. For example, the delivery unit can provide news digests related to accounts followed by the user on social media. The delivery unit can also use AI to suggest delivery methods. For example, the delivery unit can provide news digests related to posts liked by the user on social media. In this way, the delivery unit can suggest the optimal delivery method by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's social media activity into AI, and the AI ​​can suggest the optimal delivery method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can collect not only user interests and browsing history, but also the content of newsletters and email magazines that users subscribe to. For example, if a user subscribes to a specific newsletter, the data collection unit can collect the content of that newsletter to gain a more accurate understanding of the user's interests. Similarly, if a user subscribes to an email magazine, the data collection unit can collect its content to generate news summaries based on the user's interests. This allows the data collection unit to provide more customized news summaries by considering not only the user's interests and browsing history, but also the content of the newsletters and email magazines they subscribe to.

[0100] The analytics department can also consider users' social media activity when generating news summaries based on their interests and browsing history. For example, by analyzing news articles that users have shared on social media and posts from accounts they follow, the analytics department can more accurately understand users' interests. Furthermore, by analyzing posts that users have "liked" and articles they have commented on on social media, the analytics department can generate news summaries that reflect users' interests. In this way, the analytics department can provide more relevant news summaries by considering users' social media activity.

[0101] The generation unit can provide news summaries as one-minute videos in a format optimized for the user's device. For example, if the user is using a smartphone, it can select a video format that matches the smartphone's screen size. If the user is using a tablet, it can provide a video format optimized for the tablet's screen size. Furthermore, if the user is using a desktop computer, it can provide a video format optimized for a large screen. As a result, the generation unit can provide news summaries in a video format optimized for the user's device, thereby providing a more comfortable viewing experience.

[0102] The delivery department can take the user's calendar information into consideration when providing news digests at times selected by the user. For example, if the user has an appointment on their calendar, the timing of news digest delivery can be adjusted to match that appointment. Furthermore, if the user is in a specific situation, such as being in a meeting or traveling, the delivery method of the news digest can be changed accordingly. In addition, news related to specific events can be prioritized based on the user's calendar. This allows the delivery department to provide news digests at a more appropriate time by considering the user's calendar information.

[0103] The news provider can evaluate the reliability of news when integrating information from multiple news sources. For example, it can use an algorithm to evaluate the reliability of news sources and prioritize the integration of information from reliable sources. Furthermore, the provider can fact-check the content of news and eliminate misinformation and fake news. In addition, the provider can provide users with information on news reliability, enabling them to select reliable information. In this way, by evaluating the reliability of news, the provider can provide users with reliable news digests.

[0104] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is relaxed, it can prioritize collecting data related to entertainment and hobbies. If the user is stressed, it can prioritize collecting data related to relaxing content. Furthermore, if the user is excited, it can prioritize collecting data related to stimulating content. In this way, the data collection unit can collect more appropriate data by adjusting the types of data collected based on the user's emotions.

[0105] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on those emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise results can be provided. Furthermore, if the user is excited, visually appealing analysis results can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation method based on the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the content of the news summary based on those emotions. For example, if the user is relaxed, it can prioritize summarizing positive news. If the user is stressed, it can prioritize summarizing relaxing content. Furthermore, if the user is excited, it can prioritize summarizing stimulating news. In this way, the generation unit can provide more appropriate news summaries by adjusting the content based on the user's emotions.

[0107] The delivery unit can estimate the user's emotions and adjust the timing of news digest delivery based on those emotions. For example, if the user is relaxed, the news digest can be delivered in a relaxed manner. If the user is in a hurry, a concise news digest can be delivered immediately. Furthermore, if the user is excited, a stimulating news digest can be delivered. In this way, the delivery unit can provide more appropriate news digests by adjusting the timing of news digest delivery based on the user's emotions.

[0108] The delivery unit can estimate the user's emotions and adjust the format of the news digest based on those emotions. For example, if the user is relaxed, a news digest with video and audio can be provided. If the user is in a hurry, a concise news digest in text format can be provided. Furthermore, if the user is excited, a visually engaging news digest can be provided. In this way, the delivery unit can provide a more appropriate news digest by adjusting the format of the news digest based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects user interests and browsing history. For example, it collects data such as news articles the user has previously viewed and topics they have shown interest in. The data collection unit can also use AI to analyze user interests and browsing history. Step 2: The analysis department analyzes the data collected by the collection department. For example, it uses data mining techniques and machine learning algorithms to analyze user interests and browsing history. The analysis department can also use AI to generate news summaries based on user interests. Step 3: The generation unit generates a news summary based on the analysis results obtained by the analysis unit. For example, it generates news summaries based on the length of the summary and the importance of the information being summarized. The generation unit can also use AI to provide the news summary as a one-minute video. Step 4: The delivery unit provides a video summary of the news generated by the generation unit. For example, it can provide a news digest tailored to a time period selected by the user. The delivery unit can also use AI to integrate information from multiple news sources and simplify the information through visual summaries.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user interests and browsing history using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit generates a news summary using the specific processing unit 290 of the data processing unit 12. The provision unit provides the news summary generated by the control unit 46A of the smart device 14 as a video. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's interests and browsing history using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit generates a news summary, for example, in the specific processing unit 290 of the data processing unit 12. The provision unit provides the news summary generated by the control unit 46A of the smart glasses 214 as a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation 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 collects user interests and browsing history using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit generates a news summary using the specific processing unit 290 of the data processing unit 12. The provision unit provides the news summary generated by the control unit 46A of the headset terminal 314 as a video. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user interests and browsing history using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit generates a news summary, for example, in the specific processing unit 290 of the data processing unit 12. The provision unit provides the news summary generated by the control unit 46A of the robot 414 as a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects user interests and browsing history, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a news summary based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides a summary of the news generated by the generation unit as a video, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the user's past browsing history and topics they have shown interest in. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed, and news summaries are generated based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The generated news summary is provided as a one-minute video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides news digests tailored to the time slot selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It integrates information from multiple news sources and simplifies the information through visual summaries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of browsing history collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past browsing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting browsing history, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting browsing history, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting browsing history, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's sentiment and adjust the news summary generation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating news summaries, adjust the level of detail in the summary based on the importance of the news. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating news summaries, different summarization algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating news summaries, the priority of the summaries is determined based on when the news was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating news summaries, the order of the summaries is adjusted based on the relevance of the news items. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate user sentiment and adjust how news digests are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing news digests, the system selects the optimal delivery method by referring to the user's past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing news digests, 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 28) The aforementioned supply unit is, It estimates user sentiment and prioritizes news digests based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing news digests, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing news digests, we analyze users' social media activity and propose delivery methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 data collection unit that collects user interests and browsing history, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a news summary based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides a summary of the news generated by the generation unit as a video, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the user's past browsing history and topics they have shown interest in. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed, and news summaries are generated based on the user's interests. The system according to feature 1.

4. The generating unit is The generated news summary is provided as a one-minute video. The system according to feature 1.

5. The aforementioned supply unit is, Provides news digests tailored to the time slot selected by the user. The system according to feature 1.

6. The aforementioned supply unit is, It integrates information from multiple news sources and simplifies the information through visual summaries. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of browsing history collection based on the estimated emotions. The system according to feature 1.

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

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