System

Abstract

An object of the system according to the embodiment is to customize news based on the preference and interest of a user and to efficiently provide the news.SOLUTION: A system includes an analysis unit, a customization unit, a generation unit, and an interface unit. The analysis unit analyzes the user's preference and interest. The customizing unit customizes the news based on the user's preference and interest analyzed by the analyzing unit. The generation unit summarizes and generates the news customized by the customization unit. The interface unit provides the user with the news generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies make it difficult for users to find relevant information from a large amount of news information, which can lead to stress and waste of time due to information overload.

[0005] The system according to the embodiment aims to customize and efficiently provide news based on the user's preferences and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a customization unit, a generation unit, and an interface unit. The analysis unit analyzes user preferences and interests. The customization unit customizes news based on the user preferences and interests analyzed by the analysis unit. The generation unit summarizes and generates the news customized by the customization unit. The interface unit provides the news generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide customized news based on the user's preferences and interests. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A news customization system according to an embodiment of the present invention customizes news based on a user's preferences and interests, and provides summaries and generated content using a generation AI. This reduces stress and time wasted due to information overload for users, and enables efficient information gathering.

[0029] A news customization system according to an embodiment includes an analysis unit, a customization unit, a generation unit, and an interface unit. The analysis unit analyzes a user's preferences and interests. For example, the analysis unit may analyze a user's browsing history to identify the user's preferences and interests. The analysis unit may also analyze survey results to identify the user's interests. The analysis unit may also analyze social media activity to identify the user's interests. The customization unit customizes news based on the user's preferences and interests analyzed by the analysis unit. For example, the customization unit selects news related to topics in which the user has expressed interest. The customization unit may also adjust the timing of news delivery to suit the user's preferences. The customization unit may also customize the format of the news to suit the user's preferences. The generation unit summarizes and generates news customized by the customization unit. For example, the generation unit summarizes a news article and extracts only important points. The generation unit may also generate new content to suit the user's preferences. The generation unit may also generate a summary based on the full text of a news article. The interface unit provides the news generated by the generation unit to the user. For example, the interface unit may provide news through a dedicated app. Alternatively, the interface unit may provide news through a website. Alternatively, the interface unit may provide news through notifications or emails. As a result, the news customization system according to the embodiment reduces stress and time wasted due to information overload for users and enables efficient information gathering.

[0030] The analysis unit can analyze a user's social media activity and identify topics of interest. For example, the analysis unit links the user's social media accounts and analyzes activity such as post content and "likes." For example, if there are many posts about a particular topic, news related to that topic is preferentially provided. The analysis unit also analyzes the user's following relationships and group participation status on social media to identify the user's interests. For example, if the user belongs to a particular community, news related to that community is provided. The analysis unit also analyzes the user's comment and share history on social media to identify topics of interest. For example, if there are many comments on a particular news article, news related to that topic is provided. In this way, topics of interest can be identified based on the user's social media activity.

[0031] The analysis unit can analyze the user's real-time behavioral data and provide news that reflects their interests at that time. For example, the analysis unit can analyze the user's location information and provide news related to that area. For example, if the user is in a particular city, the latest news about that city can be displayed preferentially. The analysis unit can also analyze the user's time of day and provide news appropriate for that time period. For example, a short news summary can be provided during the user's morning commute, and detailed news articles can be provided during the user's evening relaxation time. The analysis unit can also analyze the user's real-time behavioral data and provide news that reflects their interests at that time. For example, if the user is participating in a sporting event, news related to that sport can be provided. This allows news to be provided based on the user's real-time behavioral data.

[0032] The analysis unit can analyze the user's voice commands and identify topics of interest. For example, the analysis unit analyzes questions and commands given by the user to the voice assistant to identify topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The analysis unit can also analyze the history of voice commands to identify the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The analysis unit can also use voice recognition technology to analyze the content of the user's conversations and identify topics of interest. For example, news related to topics that the user frequently talks about in conversations with friends is provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0033] The analysis unit can analyze a user's purchasing history and provide related news. For example, the analysis unit analyzes a user's online shopping history and provides news related to the purchased products. For example, if a user purchases products from a specific brand, the latest news about that brand is displayed. The analysis unit also identifies the user's interests based on the purchasing history and provides related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The analysis unit also analyzes a user's purchasing history and provides related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on the user's purchasing history.

[0034] The customization unit can analyze the user's past news ratings and provide highly rated news preferentially. The customization unit, for example, analyzes news articles that the user has given high ratings to in the past and provides similar news preferentially. For example, it analyzes the trends in articles that the user has given "likes" or high ratings to. The customization unit also identifies the user's preferences based on past news rating data and provides highly rated news. For example, it analyzes ratings for specific genres or topics. The customization unit also analyzes the user's past news ratings and provides highly rated news preferentially. For example, it displays related news based on the trends in news that the user has given high ratings to. This makes it possible to provide highly rated news based on the user's past news ratings.

[0035] The customization unit can analyze the user's voice commands and identify topics of interest. For example, the customization unit can analyze questions and commands the user makes to the voice assistant and identify topics of interest. For example, related news can be provided based on a request such as "Tell me the latest technology news." The customization unit can also analyze the history of voice commands and identify the user's interests. For example, news related to topics that have been frequently requested in the past can be displayed preferentially. The customization unit can also use voice recognition technology to analyze the content of the user's conversations and identify topics of interest. For example, news related to topics that the user frequently talks about in conversations with friends can be provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0036] The customization unit can analyze a user's purchasing history and provide related news. For example, the customization unit can analyze a user's online shopping history and provide news related to the purchased products. For example, if a user purchases products from a specific brand, the latest news about that brand can be displayed. The customization unit can also identify a user's interests based on the purchasing history and provide related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities can be displayed preferentially. The customization unit can also analyze a user's purchasing history and provide related news. For example, if a user purchases a lot of books in a specific genre, news about that genre can be provided. This makes it possible to provide related news based on a user's purchasing history.

[0037] The generation unit can analyze the user's past news ratings when summarizing news, and provide highly rated summaries preferentially. For example, the generation unit analyzes news articles that the user has previously given high ratings, and provide similar summaries preferentially. For example, the generation unit analyzes the trends of articles that the user has given "likes" or high ratings. The generation unit also identifies the user's preferences based on past news rating data, and provides highly rated summaries. For example, the generation unit analyzes ratings for specific genres or topics. The generation unit also analyzes the user's past news ratings, and provides highly rated summaries preferentially. For example, related summaries are displayed based on the trends of news that the user has given high ratings. This makes it possible to provide highly rated summaries based on the user's past news ratings.

[0038] The generation unit can track changes in a user's interests in news summaries and provide summaries based on the user's latest interests. For example, the generation unit analyzes the user's past browsing history to track changes in interests. For example, if interest in a particular topic is increasing, a summary related to that topic is provided. The generation unit also tracks changes in a user's interests in real time and provides summaries based on the user's latest interests. For example, summaries are customized based on recent search history and browsing history. The generation unit also tracks changes in a user's interests and provides summaries based on the user's latest interests. For example, changes in interests are predicted based on past data and relevant summaries are displayed. This makes it possible to provide summaries that match the user's latest interests based on changes in their interests.

[0039] The generation unit can analyze the user's voice commands to identify topics of interest in news summaries. For example, the generation unit analyzes questions and commands given by the user to the voice assistant to identify topics of interest. For example, the generation unit provides relevant summaries based on requests such as "Tell me the latest technology news." The generation unit can also analyze the voice command history to identify the user's interests. For example, it can prioritize displaying summaries related to topics that have been frequently requested in the past. The generation unit can also analyze the content of the user's conversations using voice recognition technology to identify topics of interest. For example, it can provide summaries related to topics that the user frequently talks about in conversations with friends. This makes it possible to identify topics of interest based on the user's voice commands.

[0040] The generation unit can analyze a user's purchasing history to provide relevant summaries for news summaries. For example, the generation unit can analyze a user's online shopping history to provide summaries related to purchased products. For example, if a user has purchased products from a specific brand, the latest summaries related to that brand are displayed. The generation unit can also identify a user's interests based on the purchasing history and provide relevant summaries. For example, if a user has purchased a lot of outdoor equipment, summaries related to outdoor activities are preferentially displayed. The generation unit can also analyze a user's purchasing history to provide relevant summaries. For example, if a user has purchased a lot of books in a specific genre, summaries related to that genre are provided. This allows relevant summaries to be provided based on the user's purchasing history.

[0041] The interface unit can analyze the user's past operation history and provide an easy-to-use interface. The interface unit can, for example, analyze the user's past operation history and provide an easy-to-use interface. For example, it can place frequently used functions and buttons in prominent positions. The interface unit can also customize the interface to suit the user's preferences based on the operation history. For example, it can use the user's preferred colors and fonts. The interface unit can also analyze the user's operation history and provide an easy-to-use interface. For example, it can automatically save previously used functions and settings and reflect them the next time they are used. This makes it possible to provide an easy-to-use interface based on the user's past operation history.

[0042] The interface unit can analyze the user's real-time behavioral data and provide an interface that meets the user's needs at that time. The interface unit, for example, analyzes the user's real-time behavioral data and provides an interface that meets the user's needs at that time. For example, when the user is on the move, a simple and easy-to-use interface is provided. The interface unit also displays content that meets the user's needs based on the behavioral data. For example, when the user is at work, business-related news is displayed preferentially. The interface unit also analyzes the user's real-time behavioral data and provides an interface that meets the user's needs at that time. For example, when the user is relaxing, entertainment-related content is displayed. This makes it possible to provide an interface that meets the user's needs at that time based on the user's real-time behavioral data.

[0043] The interface unit can analyze the user's voice commands and identify topics of interest. For example, the interface unit analyzes questions and commands given by the user to the voice assistant and identifies topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The interface unit can also analyze the history of voice commands and identify the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The interface unit can also use voice recognition technology to analyze the content of the user's conversation and identify topics of interest. For example, news related to topics that the user frequently discusses in conversations with friends is provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0044] The interface unit can analyze a user's purchasing history and provide related news. For example, the interface unit can analyze a user's online shopping history and provide news related to the purchased items. For example, if a user purchases items from a specific brand, the latest news about that brand is displayed. The interface unit can also identify a user's interests based on the purchasing history and provide related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The interface unit can also analyze a user's purchasing history and provide related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on a user's purchasing history.

[0045] In order to reduce information overload, the interface unit can analyze the user's past news ratings and provide highly rated news preferentially. The interface unit, for example, analyzes news articles that the user has given high ratings to in the past and provides similar news preferentially. For example, it analyzes the trends in articles that users have given "likes" or high ratings to. The interface unit also identifies the user's preferences based on past news rating data and provides highly rated news. For example, it analyzes ratings for specific genres or topics. The interface unit also analyzes the user's past news ratings and provides highly rated news preferentially. For example, it displays related news based on the trends in news that users have given high ratings to. This makes it possible to provide highly rated news based on the user's past news ratings.

[0046] In order to reduce information overload, the interface unit can track changes in a user's interests and provide news based on their latest interests. For example, the interface unit analyzes a user's past browsing history to track changes in interests. For example, if interest in a particular topic is increasing, news related to that topic is provided. The interface unit also tracks changes in a user's interests in real time and provides news based on their latest interests. For example, news is customized based on recent search history and browsing history. The interface unit also tracks changes in a user's interests and provides news based on their latest interests. For example, changes in interests are predicted based on past data and related news is displayed. This makes it possible to provide news that matches the user's latest interests based on changes in their interests.

[0047] In order to reduce information overload, the interface unit can analyze voice commands and identify topics of interest. For example, the interface unit analyzes questions and commands given by the user to the voice assistant and identifies topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The interface unit also analyzes the history of voice commands and identifies the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The interface unit also uses voice recognition technology to analyze the content of the user's conversation and identify topics of interest. For example, news related to topics that the user frequently discusses in conversations with friends is provided. In this way, topics of interest can be identified based on the user's voice commands.

[0048] The interface unit can analyze a user's purchase history and provide related news to reduce information overload. For example, the interface unit analyzes a user's online shopping history and provides news related to purchased items. For example, if a user purchases a specific brand of product, the latest news about that brand is displayed. The interface unit also identifies the user's interests based on the purchase history and provides related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The interface unit also analyzes a user's purchase history and provides related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on the user's purchase history.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can also analyze the user's health data and provide health-related news. For example, it can analyze data obtained from the user's fitness tracker or smartwatch and provide the latest health news and advice. The analysis unit can also analyze the user's food records and provide nutrition-related news. For example, if the user consumes a lot of a particular ingredient, it can provide health information related to that ingredient. The analysis unit can also analyze the user's sleep data and provide sleep-related news. For example, it can provide news and advice to promote good quality sleep based on the user's sleep patterns. This makes it possible to provide health-related news based on the user's health data.

[0051] The customization unit can also provide news based on the user's hobbies and skills. For example, if the user's hobby is a particular sport, the customization unit can provide the latest news and event information related to that sport. If the user is interested in a particular art or craft, the customization unit can provide news and tutorials related to that field. For example, if the user is interested in painting, the customization unit can provide the latest art news and articles on techniques. The customization unit can also provide news related to the user's skills. For example, if the user's skill is cooking, the customization unit can provide news about new recipes and cooking tips. This makes it possible to provide relevant news based on the user's hobbies and skills.

[0052] The generator may also provide a news summary tailored to the user's learning style. For example, a summary including graphs and charts may be provided for visual learners. The generator may also provide an audio summary for auditory learners. For example, a function may be provided that reads the news article summary aloud. The generator may also provide an interactive summary for experiential learners. For example, a summary including quizzes and surveys related to the news article may be provided. This allows the generator to provide a summary tailored to the user's learning style.

[0053] The interface unit can also analyze the user's device usage status and provide an optimal interface. For example, if the user is using a smartphone, it can provide an interface optimized for mobile devices. Furthermore, if the user is using a tablet, it can provide an interface suitable for a large screen. For example, it can provide a layout optimized for touch operations. Furthermore, if the user is using a desktop, it can provide an interface suitable for keyboard and mouse operations. For example, it can provide shortcut keys and drag-and-drop functions. This makes it possible to provide an optimal interface based on the user's device usage status.

[0054] The interface unit can also analyze user feedback and improve the interface. For example, it can analyze feedback provided by users and identify areas for improvement in usability and design. The interface unit can also add new functions and options based on user feedback. For example, it can implement functions requested by users. The interface unit can also analyze user feedback in real time and immediately improve the interface. For example, it can quickly correct areas where users express dissatisfaction. This allows the interface to be improved based on user feedback.

[0055] The interface unit can also analyze a user's behavioral patterns and provide a predictive interface. For example, if a user tends to view specific news during a specific time period, news related to that time period can be displayed preferentially. The interface unit can also predict and provide the next functions or information that the user will need based on the user's behavioral patterns. For example, it can predict the functions that the user will use frequently and make them accessible with one click. The interface unit can also analyze a user's behavioral patterns and provide a customized interface. For example, it can provide an optimal layout and design based on the user's past behavior. This makes it possible to provide a predictive interface based on the user's behavioral patterns.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis unit analyzes the user's preferences and interests. For example, the analysis unit may analyze the user's past browsing history, survey results, and social media activity to identify the user's preferences and interests. Step 2: The customization unit customizes the news based on the user's preferences and interests analyzed by the analysis unit. For example, the customization unit selects news related to topics the user has expressed interest in and adjusts the timing and format of news delivery to suit the user's preferences. Step 3: The generator summarizes and generates news customized by the customization unit. For example, the generator summarizes news articles and extracts only the key points. It can also generate new content tailored to the user's preferences. Step 4: The interface unit provides the news generated by the generation unit to the user. For example, the interface unit provides the news through a dedicated app, a website, notifications, or emails.

[0058] (Example 2) A news customization system according to an embodiment of the present invention customizes news based on a user's preferences and interests, and provides summaries and generated content using a generation AI. This reduces stress and time wasted due to information overload for users, and enables efficient information gathering.

[0059] A news customization system according to an embodiment includes an analysis unit, a customization unit, a generation unit, and an interface unit. The analysis unit analyzes a user's preferences and interests. For example, the analysis unit may analyze a user's browsing history to identify the user's preferences and interests. The analysis unit may also analyze survey results to identify the user's interests. The analysis unit may also analyze social media activity to identify the user's interests. The customization unit customizes news based on the user's preferences and interests analyzed by the analysis unit. For example, the customization unit selects news related to topics in which the user has expressed interest. The customization unit may also adjust the timing of news delivery to suit the user's preferences. The customization unit may also customize the format of the news to suit the user's preferences. The generation unit summarizes and generates news customized by the customization unit. For example, the generation unit summarizes a news article and extracts only important points. The generation unit may also generate new content to suit the user's preferences. The generation unit may also generate a summary based on the full text of a news article. The interface unit provides the news generated by the generation unit to the user. For example, the interface unit may provide news through a dedicated app. Alternatively, the interface unit may provide news through a website. Alternatively, the interface unit may provide news through notifications or emails. As a result, the news customization system according to the embodiment reduces stress and time wasted due to information overload for users and enables efficient information gathering.

[0060] The analysis unit can use the emotion estimation function to analyze the user's emotions when viewing the news and prioritize news that elicits positive emotions. The analysis unit, for example, analyzes the user's facial expressions and voice in real time when viewing the news and calculates an emotion score. For example, the analysis unit analyzes the user's emotions using a camera or microphone and prioritizes displaying news that elicits positive emotions. The analysis unit also accumulates emotional data of the user when viewing the news and selects news that elicits positive emotions based on past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The analysis unit also uses the emotion estimation function to analyze the user's emotions when viewing the news in real time and provides news that elicits positive emotions. For example, it prioritizes displaying news with a high user emotion score. In this way, positive emotions can be elicited by selecting news based on the user's emotions.

[0061] The analysis unit can analyze a user's social media activity and identify topics of interest. For example, the analysis unit links the user's social media accounts and analyzes activity such as post content and "likes." For example, if there are many posts about a particular topic, news related to that topic is preferentially provided. The analysis unit also analyzes the user's following relationships and group participation status on social media to identify the user's interests. For example, if the user belongs to a particular community, news related to that community is provided. The analysis unit also analyzes the user's comment and share history on social media to identify topics of interest. For example, if there are many comments on a particular news article, news related to that topic is provided. In this way, topics of interest can be identified based on the user's social media activity.

[0062] The analysis unit can analyze the user's real-time behavioral data and provide news that reflects their interests at that time. For example, the analysis unit can analyze the user's location information and provide news related to that area. For example, if the user is in a particular city, the latest news about that city can be displayed preferentially. The analysis unit can also analyze the user's time of day and provide news appropriate for that time period. For example, a short news summary can be provided during the user's morning commute, and detailed news articles can be provided during the user's evening relaxation time. The analysis unit can also analyze the user's real-time behavioral data and provide news that reflects their interests at that time. For example, if the user is participating in a sporting event, news related to that sport can be provided. This allows news to be provided based on the user's real-time behavioral data.

[0063] The analysis unit can analyze the user's voice commands and identify topics of interest. For example, the analysis unit analyzes questions and commands given by the user to the voice assistant to identify topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The analysis unit can also analyze the history of voice commands to identify the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The analysis unit can also use voice recognition technology to analyze the content of the user's conversations and identify topics of interest. For example, news related to topics that the user frequently talks about in conversations with friends is provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0064] The analysis unit can analyze a user's purchasing history and provide related news. For example, the analysis unit analyzes a user's online shopping history and provides news related to the purchased products. For example, if a user purchases products from a specific brand, the latest news about that brand is displayed. The analysis unit also identifies the user's interests based on the purchasing history and provides related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The analysis unit also analyzes a user's purchasing history and provides related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on the user's purchasing history.

[0065] The analysis unit uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. The analysis unit, for example, analyzes facial expressions and voices of a user when viewing news in real time and calculates an emotion score. For example, the analysis unit analyzes the user's emotions using a camera or microphone and preferentially displays news that elicits positive emotions. The analysis unit also accumulates emotional data of the user when viewing news, and selects news that elicits positive emotions based on past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The analysis unit also uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. For example, it preferentially displays news with a high user emotion score. This makes it possible to provide news that elicits positive emotions based on the user's emotions.

[0066] The customization unit can use the emotion estimation function to preferentially select news that elicits positive emotions. For example, the customization unit analyzes the user's facial expressions and voice in real time when reading the news and calculates an emotion score. For example, the customization unit analyzes the user's emotions using a camera or microphone and preferentially displays news that elicits positive emotions. The customization unit also accumulates emotional data of the user when reading the news and selects news that elicits positive emotions based on the user's past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The customization unit also uses the emotion estimation function to analyze the user's emotions when reading the news in real time and provides news that elicits positive emotions. For example, it preferentially displays news with a high user emotion score. This makes it possible to preferentially select news that elicits positive emotions based on the user's emotions.

[0067] The customization unit can analyze the user's past news ratings and provide highly rated news preferentially. The customization unit, for example, analyzes news articles that the user has given high ratings to in the past and provides similar news preferentially. For example, it analyzes the trends in articles that the user has given "likes" or high ratings to. The customization unit also identifies the user's preferences based on past news rating data and provides highly rated news. For example, it analyzes ratings for specific genres or topics. The customization unit also analyzes the user's past news ratings and provides highly rated news preferentially. For example, it displays related news based on the trends in news that the user has given high ratings to. This makes it possible to provide highly rated news based on the user's past news ratings.

[0068] The customization unit can analyze the user's voice commands and identify topics of interest. For example, the customization unit can analyze questions and commands the user makes to the voice assistant and identify topics of interest. For example, related news can be provided based on a request such as "Tell me the latest technology news." The customization unit can also analyze the history of voice commands and identify the user's interests. For example, news related to topics that have been frequently requested in the past can be displayed preferentially. The customization unit can also use voice recognition technology to analyze the content of the user's conversations and identify topics of interest. For example, news related to topics that the user frequently talks about in conversations with friends can be provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0069] The customization unit can analyze a user's purchasing history and provide related news. For example, the customization unit can analyze a user's online shopping history and provide news related to the purchased products. For example, if a user purchases products from a specific brand, the latest news about that brand can be displayed. The customization unit can also identify a user's interests based on the purchasing history and provide related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities can be displayed preferentially. The customization unit can also analyze a user's purchasing history and provide related news. For example, if a user purchases a lot of books in a specific genre, news about that genre can be provided. This makes it possible to provide related news based on a user's purchasing history.

[0070] The customization unit uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. The customization unit, for example, analyzes facial expressions and voices of the user when viewing news in real time and calculates an emotion score. For example, the customization unit analyzes the user's emotions using a camera or microphone and preferentially displays news that elicits positive emotions. The customization unit also accumulates emotional data of the user when viewing news, and selects news that elicits positive emotions based on past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The customization unit also uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. For example, it preferentially displays news with a high user emotion score. This makes it possible to provide news that elicits positive emotions based on the user's emotions.

[0071] The generation unit can use the emotion estimation function to generate summaries that evoke positive emotions in news summaries. For example, when summarizing a news article, the generation unit generates summaries that evoke positive emotions based on the user's emotion estimation data. For example, the generation unit emphasizes positive elements in the article to provide a summary that is pleasant to the user. The generation unit also uses the emotion estimation function to extract positive parts of the news article and generate a summary centered on those parts. For example, the generation unit provides a summary that includes success stories and positive comments. The generation unit also analyzes the user's emotional reactions in real time to generate summaries that evoke positive emotions. For example, the generation unit provides a summary that emphasizes parts to which the user responded positively. This makes it possible to generate summaries that evoke positive emotions based on the user's emotions.

[0072] The generation unit can analyze the user's past news ratings when summarizing news, and provide highly rated summaries preferentially. For example, the generation unit analyzes news articles that the user has previously given high ratings, and provide similar summaries preferentially. For example, the generation unit analyzes the trends of articles that the user has given "likes" or high ratings. The generation unit also identifies the user's preferences based on past news rating data, and provides highly rated summaries. For example, the generation unit analyzes ratings for specific genres or topics. The generation unit also analyzes the user's past news ratings, and provides highly rated summaries preferentially. For example, related summaries are displayed based on the trends of news that the user has given high ratings. This makes it possible to provide highly rated summaries based on the user's past news ratings.

[0073] The generation unit can track changes in a user's interests in news summaries and provide summaries based on the user's latest interests. For example, the generation unit analyzes the user's past browsing history to track changes in interests. For example, if interest in a particular topic is increasing, a summary related to that topic is provided. The generation unit also tracks changes in a user's interests in real time and provides summaries based on the user's latest interests. For example, summaries are customized based on recent search history and browsing history. The generation unit also tracks changes in a user's interests and provides summaries based on the user's latest interests. For example, changes in interests are predicted based on past data and relevant summaries are displayed. This makes it possible to provide summaries that match the user's latest interests based on changes in their interests.

[0074] The generation unit can analyze the user's voice commands to identify topics of interest in news summaries. For example, the generation unit analyzes questions and commands given by the user to the voice assistant to identify topics of interest. For example, the generation unit provides relevant summaries based on requests such as "Tell me the latest technology news." The generation unit can also analyze the voice command history to identify the user's interests. For example, it can prioritize displaying summaries related to topics that have been frequently requested in the past. The generation unit can also analyze the content of the user's conversations using voice recognition technology to identify topics of interest. For example, it can provide summaries related to topics that the user frequently talks about in conversations with friends. This makes it possible to identify topics of interest based on the user's voice commands.

[0075] The generation unit can analyze a user's purchasing history to provide relevant summaries for news summaries. For example, the generation unit can analyze a user's online shopping history to provide summaries related to purchased products. For example, if a user has purchased products from a specific brand, the latest summaries related to that brand are displayed. The generation unit can also identify a user's interests based on the purchasing history and provide relevant summaries. For example, if a user has purchased a lot of outdoor equipment, summaries related to outdoor activities are preferentially displayed. The generation unit can also analyze a user's purchasing history to provide relevant summaries. For example, if a user has purchased a lot of books in a specific genre, summaries related to that genre are provided. This allows relevant summaries to be provided based on the user's purchasing history.

[0076] The generation unit uses the emotion estimation function to analyze the emotions of a user when reading the news in real time and provide summaries that elicit positive emotions. For example, the generation unit analyzes the user's facial expressions and voice when reading the news in real time and calculates an emotion score. For example, the generation unit analyzes the user's emotions using a camera or microphone and preferentially displays summaries that elicit positive emotions. The generation unit also accumulates emotional data of the user when reading the news and selects summaries that elicit positive emotions based on past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The generation unit also uses the emotion estimation function to analyze the user's emotions when reading the news in real time and provide summaries that elicit positive emotions. For example, it preferentially displays summaries with a high user emotion score. This makes it possible to provide summaries that elicit positive emotions based on the user's emotions.

[0077] The interface unit can use the emotion estimation function to provide an interface that corresponds to the user's emotions. The interface unit, for example, analyzes the user's emotions in real time and provides an interface that corresponds to those emotions. For example, if the user is feeling stressed, it uses a design and color scheme that promotes relaxation. The interface unit also uses the emotion estimation function to display content that corresponds to the user's emotions. For example, if the user is expressing positive emotions, it prioritizes displaying upbeat news and fun content. The interface unit also accumulates user emotion data and customizes the interface based on past emotional responses. For example, it uses designs and layouts that the user has responded positively to in the past. This makes it possible to provide an interface that corresponds to the user's emotions.

[0078] The interface unit can analyze the user's past operation history and provide an easy-to-use interface. The interface unit can, for example, analyze the user's past operation history and provide an easy-to-use interface. For example, it can place frequently used functions and buttons in prominent positions. The interface unit can also customize the interface to suit the user's preferences based on the operation history. For example, it can use the user's preferred colors and fonts. The interface unit can also analyze the user's operation history and provide an easy-to-use interface. For example, it can automatically save previously used functions and settings and reflect them the next time they are used. This makes it possible to provide an easy-to-use interface based on the user's past operation history.

[0079] The interface unit can analyze the user's real-time behavioral data and provide an interface that meets the user's needs at that time. The interface unit, for example, analyzes the user's real-time behavioral data and provides an interface that meets the user's needs at that time. For example, when the user is on the move, a simple and easy-to-use interface is provided. The interface unit also displays content that meets the user's needs based on the behavioral data. For example, when the user is at work, business-related news is displayed preferentially. The interface unit also analyzes the user's real-time behavioral data and provides an interface that meets the user's needs at that time. For example, when the user is relaxing, entertainment-related content is displayed. This makes it possible to provide an interface that meets the user's needs at that time based on the user's real-time behavioral data.

[0080] The interface unit can analyze the user's voice commands and identify topics of interest. For example, the interface unit analyzes questions and commands given by the user to the voice assistant and identifies topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The interface unit can also analyze the history of voice commands and identify the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The interface unit can also use voice recognition technology to analyze the content of the user's conversation and identify topics of interest. For example, news related to topics that the user frequently discusses in conversations with friends is provided. This makes it possible to identify topics of interest based on the user's voice commands.

[0081] The interface unit can analyze a user's purchasing history and provide related news. For example, the interface unit can analyze a user's online shopping history and provide news related to the purchased items. For example, if a user purchases items from a specific brand, the latest news about that brand is displayed. The interface unit can also identify a user's interests based on the purchasing history and provide related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The interface unit can also analyze a user's purchasing history and provide related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on a user's purchasing history.

[0082] The interface unit can use the emotion estimation function to analyze the emotions of a user when viewing news in real time and provide an interface that elicits positive emotions. For example, the interface unit analyzes facial expressions and voices of a user when viewing news in real time and calculates an emotion score. For example, the interface unit uses a camera or microphone to analyze the user's emotions and provides an interface that elicits positive emotions. The interface unit also accumulates emotional data of the user when viewing news and provides an interface that elicits positive emotions based on past emotional reactions. For example, it uses designs and layouts that have elicited positive reactions in the past. The interface unit also uses the emotion estimation function to analyze the emotions of a user when viewing news in real time and provides an interface that elicits positive emotions. For example, it preferentially displays designs and layouts with high user emotion scores. This makes it possible to provide an interface that elicits positive emotions based on the user's emotions.

[0083] In reducing information overload, the interface unit can use an emotion estimation function to prioritize providing news that reduces stress to the user. For example, the interface unit analyzes the user's facial expressions and voice in real time when reading the news and calculates an emotion score. For example, the interface unit analyzes the user's emotions using a camera or microphone and prioritizes displaying news that reduces stress. The interface unit also accumulates emotional data of the user when reading the news and selects news that reduces stress based on past emotional reactions. For example, it analyzes trends in news that have had a relaxing effect in the past. The interface unit also uses the emotion estimation function to analyze the user's emotions in real time when reading the news and provides news that reduces stress. For example, if the user's emotion score is low, it displays relaxing news. This makes it possible to prioritize providing news that reduces stress to the user.

[0084] In order to reduce information overload, the interface unit can analyze the user's past news ratings and provide highly rated news preferentially. The interface unit, for example, analyzes news articles that the user has given high ratings to in the past and provides similar news preferentially. For example, it analyzes the trends in articles that users have given "likes" or high ratings to. The interface unit also identifies the user's preferences based on past news rating data and provides highly rated news. For example, it analyzes ratings for specific genres or topics. The interface unit also analyzes the user's past news ratings and provides highly rated news preferentially. For example, it displays related news based on the trends in news that users have given high ratings to. This makes it possible to provide highly rated news based on the user's past news ratings.

[0085] In order to reduce information overload, the interface unit can track changes in a user's interests and provide news based on their latest interests. For example, the interface unit analyzes a user's past browsing history to track changes in interests. For example, if interest in a particular topic is increasing, news related to that topic is provided. The interface unit also tracks changes in a user's interests in real time and provides news based on their latest interests. For example, news is customized based on recent search history and browsing history. The interface unit also tracks changes in a user's interests and provides news based on their latest interests. For example, changes in interests are predicted based on past data and related news is displayed. This makes it possible to provide news that matches the user's latest interests based on changes in their interests.

[0086] In order to reduce information overload, the interface unit can analyze voice commands and identify topics of interest. For example, the interface unit analyzes questions and commands given by the user to the voice assistant and identifies topics of interest. For example, related news is provided based on a request such as "Tell me the latest technology news." The interface unit also analyzes the history of voice commands and identifies the user's interests. For example, news related to topics that have been frequently requested in the past is displayed preferentially. The interface unit also uses voice recognition technology to analyze the content of the user's conversation and identify topics of interest. For example, news related to topics that the user frequently discusses in conversations with friends is provided. In this way, topics of interest can be identified based on the user's voice commands.

[0087] The interface unit can analyze a user's purchase history and provide related news to reduce information overload. For example, the interface unit analyzes a user's online shopping history and provides news related to purchased items. For example, if a user purchases a specific brand of product, the latest news about that brand is displayed. The interface unit also identifies the user's interests based on the purchase history and provides related news. For example, if a user purchases a lot of outdoor equipment, news about outdoor activities is displayed preferentially. The interface unit also analyzes a user's purchase history and provides related news. For example, if a user purchases a lot of books in a specific genre, news about that genre is provided. This makes it possible to provide related news based on the user's purchase history.

[0088] The interface unit uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. The interface unit, for example, analyzes facial expressions and voices of a user when viewing news in real time and calculates an emotion score. For example, the interface unit analyzes the user's emotions using a camera or microphone and preferentially displays news that elicits positive emotions. The interface unit also accumulates emotional data of the user when viewing news, and selects news that elicits positive emotions based on past emotional reactions. For example, it analyzes trends in news that have elicited positive reactions in the past. The interface unit also uses the emotion estimation function to analyze the emotions of a user when viewing news in real time, and can provide news that elicits positive emotions. For example, it preferentially displays news with a high user emotion score. This makes it possible to provide news that elicits positive emotions based on the user's emotions.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The analysis unit can also analyze the user's health data and provide health-related news. For example, it can analyze data obtained from the user's fitness tracker or smartwatch and provide the latest health news and advice. The analysis unit can also analyze the user's food records and provide nutrition-related news. For example, if the user consumes a lot of a particular ingredient, it can provide health information related to that ingredient. The analysis unit can also analyze the user's sleep data and provide sleep-related news. For example, it can provide news and advice to promote good quality sleep based on the user's sleep patterns. This makes it possible to provide health-related news based on the user's health data.

[0091] The analysis unit can also use the user emotion estimation function to provide relaxing news to users when they are feeling stressed. For example, if it is estimated that the user is feeling stressed, it can provide news about relaxing music or natural scenery. The analysis unit can also provide motivational news to users when they are showing positive emotions. For example, it can prioritize displaying news that includes success stories and positive stories. The analysis unit can also accumulate user emotion data and select relaxing news for users based on past emotional responses. For example, it can analyze trends in news that have had a relaxing effect in the past. This makes it possible to provide relaxing news based on the user's emotions.

[0092] The customization unit can also provide news based on the user's hobbies and skills. For example, if the user's hobby is a particular sport, the customization unit can provide the latest news and event information related to that sport. If the user is interested in a particular art or craft, the customization unit can provide news and tutorials related to that field. For example, if the user is interested in painting, the customization unit can provide the latest art news and articles on techniques. The customization unit can also provide news related to the user's skills. For example, if the user's skill is cooking, the customization unit can provide news about new recipes and cooking tips. This makes it possible to provide relevant news based on the user's hobbies and skills.

[0093] The generator may also provide a news summary tailored to the user's learning style. For example, a summary including graphs and charts may be provided for visual learners. The generator may also provide an audio summary for auditory learners. For example, a function may be provided that reads the news article summary aloud. The generator may also provide an interactive summary for experiential learners. For example, a summary including quizzes and surveys related to the news article may be provided. This allows the generator to provide a summary tailored to the user's learning style.

[0094] The generation unit can also use the emotion estimation function to generate summaries that elicit positive emotions from users when summarizing news. For example, when summarizing a news article, a summary that elicits positive emotions is generated based on the user's emotion estimation data. For example, the generation unit can emphasize positive elements in the article to provide a summary that is pleasant to the user. The generation unit can also use the emotion estimation function to extract positive parts of a news article and generate a summary that focuses on those parts. For example, the generation unit can provide a summary that includes success stories and positive comments. The generation unit can also analyze the user's emotional reactions in real time to generate summaries that elicit positive emotions. For example, the generation unit can provide a summary that emphasizes parts to which the user showed a positive reaction. This makes it possible to generate summaries that elicit positive emotions based on the user's emotions.

[0095] The interface unit can also analyze the user's device usage status and provide an optimal interface. For example, if the user is using a smartphone, it can provide an interface optimized for mobile devices. Furthermore, if the user is using a tablet, it can provide an interface suitable for a large screen. For example, it can provide a layout optimized for touch operations. Furthermore, if the user is using a desktop, it can provide an interface suitable for keyboard and mouse operations. For example, it can provide shortcut keys and drag-and-drop functions. This makes it possible to provide an optimal interface based on the user's device usage status.

[0096] The interface unit can also use the emotion estimation function to analyze the emotions of a user when viewing the news in real time, and provide an interface that elicits positive emotions. For example, the interface unit can analyze the user's facial expressions and voice when viewing the news in real time and calculate an emotion score. For example, the interface unit can use a camera or microphone to analyze the user's emotions and provide an interface that elicits positive emotions. The interface unit can also accumulate emotional data of the user when viewing the news, and provide an interface that elicits positive emotions based on past emotional reactions. For example, the interface unit can use designs and layouts that have elicited positive reactions in the past. The interface unit can also use the emotion estimation function to analyze the emotions of a user when viewing the news in real time, and provide an interface that elicits positive emotions. For example, designs and layouts with high user emotion scores are preferentially displayed. This makes it possible to provide an interface that elicits positive emotions based on the user's emotions.

[0097] The interface unit can also analyze user feedback and improve the interface. For example, it can analyze feedback provided by users and identify areas for improvement in usability and design. The interface unit can also add new functions and options based on user feedback. For example, it can implement functions requested by users. The interface unit can also analyze user feedback in real time and immediately improve the interface. For example, it can quickly correct areas where users express dissatisfaction. This allows the interface to be improved based on user feedback.

[0098] The interface unit can also analyze a user's behavioral patterns and provide a predictive interface. For example, if a user tends to view specific news during a specific time period, news related to that time period can be displayed preferentially. The interface unit can also predict and provide the next functions or information that the user will need based on the user's behavioral patterns. For example, it can predict the functions that the user will use frequently and make them accessible with one click. The interface unit can also analyze a user's behavioral patterns and provide a customized interface. For example, it can provide an optimal layout and design based on the user's past behavior. This makes it possible to provide a predictive interface based on the user's behavioral patterns.

[0099] The interface unit can also use the emotion estimation function to analyze the user's stress level and provide an interface that reduces stress. For example, if it is estimated that the user is feeling stressed, it uses a design and color scheme that promotes relaxation. The interface unit can also use the emotion estimation function to display content according to the user's stress level. For example, if the user is feeling stressed, it can provide news or videos that have a relaxing effect. The interface unit can also accumulate the user's stress data and customize the interface based on the user's past stress responses. For example, it can use designs and layouts that have shown a relaxing effect on the user in the past. This makes it possible to provide an interface that reduces stress based on the user's stress level.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The analysis unit analyzes the user's preferences and interests. For example, the analysis unit may analyze the user's past browsing history, survey results, and social media activity to identify the user's preferences and interests. Step 2: The customization unit customizes the news based on the user's preferences and interests analyzed by the analysis unit. For example, the customization unit selects news related to topics the user has expressed interest in and adjusts the timing and format of news delivery to suit the user's preferences. Step 3: The generator summarizes and generates news customized by the customization unit. For example, the generator summarizes news articles and extracts only the key points. It can also generate new content tailored to the user's preferences. Step 4: The interface unit provides the news generated by the generation unit to the user. For example, the interface unit provides the news through a dedicated app, a website, notifications, or emails.

[0102] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit for analyzing user preferences and interests; a customization unit that customizes news based on the user's preferences and interests analyzed by the analysis unit; a generating unit that summarizes and generates the news customized by the customizing unit; an interface unit that provides the news generated by the generation unit to a user; A system characterized by:

2. The analysis unit Analyzing the emotions of the user when browsing news, and preferentially selecting the news that elicits positive emotions.

2. The system of claim 1.

3. The analysis unit Analyzing the user's social media activity to identify topics of interest 2. The system of claim 1.

4. The analysis unit Analyzing real-time behavioral data of the user and providing the news that reflects the user's interests at that time 2. The system of claim 1.

5. The analysis unit Analyzing the user's voice commands to identify topics of interest 2. The system of claim 1.

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