System

Abstract

An object of a system according to an embodiment is to provide highly reliable news according to a user's personal preference.SOLUTION: A system includes a news selection part, a news analysis part, a reliability evaluation part, and a news provision part. The news selection unit selects news according to the personal preference of the user. The news analysis unit analyzes the news selected by the news selection unit. The reliability evaluation unit evaluates the reliability of the news analyzed by the news analysis unit. The news providing unit provides the user with the news evaluated by the reliability evaluation 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 have problems of bias and reliability when providing news according to a user's personal preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide reliable news that meets the personal preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a news selection unit, a news analysis unit, a reliability evaluation unit, and a news provision unit. The news selection unit selects news according to a user's personal preferences. The news analysis unit analyzes the news selected by the news selection unit. The reliability evaluation unit evaluates the reliability of the news analyzed by the news analysis unit. The news provision unit provides the user with the news evaluated by the reliability evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide reliable news according to the user's personal preferences. [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 delivery system according to an embodiment of the present invention is a system that provides a news experience that is unbiased and fact-based according to the user's personal preferences. This allows the news delivery system to provide news that is unbiased and fact-based according to the user's personal preferences.

[0029] A news provision system according to an embodiment includes a news selection unit, a news analysis unit, a reliability evaluation unit, and a news provision unit. The news selection unit selects news according to a user's personal preferences. For example, the news selection unit analyzes a user's past news viewing history and interests to select news that is optimal for each individual user. The news selection unit can also analyze a user's social media activities to provide news that reflects changes in interests in real time. The news selection unit can also analyze a user's real-time behavioral data to provide news that is optimal for the user's current situation. The news analysis unit analyzes the news selected by the news selection unit. For example, the news analysis unit analyzes the content of a news article to extract fact-based information that is free from opinions and bias. The news analysis unit can also perform sentiment analysis of the news article to extract only facts that are free from emotional bias. The news analysis unit can also automatically translate the content of the news article into different languages ​​to provide neutral reporting from an international perspective. The reliability evaluation unit evaluates the reliability of the news analyzed by the news analysis unit. For example, the reliability evaluation unit evaluates the reliability of a news source and prioritizes providing reliable information. The reliability evaluation unit can also analyze past reporting history and reliability scores to evaluate the reliability of a news article and prioritize providing reliable information. The reliability evaluation unit can also integrate information from different perspectives to generate reliable reports to evaluate the reliability of a news article. The news providing unit provides the news evaluated by the reliability evaluation unit to the user. For example, the news providing unit customizes news based on the user's values ​​and preferences. The news providing unit can also estimate the user's emotions and prioritize providing news that elicits positive emotions. The news providing unit can also provide news that meets the user's preferences via voice through a voice assistant. This allows the news providing system according to the embodiment to provide unbiased, fact-based news that meets the user's personal preferences.For example, the output unit may display the news to the user through a web application or a mobile application, print the results using a printer if paper feedback is desired, or send the news via email directly to the user to provide quick feedback.

[0030] The news selection unit can analyze a user's past news browsing history and interests to select the most appropriate news for each individual user. The news selection unit, for example, analyzes interests based on the user's news browsing history. For example, it extracts keywords from the news browsing history to identify the user's interests. The news selection unit can also analyze the user's social media activity to provide news that reflects changes in interests in real time. For example, it analyzes the content of posts on social media and reactions such as "likes" to understand changes in interests. The news selection unit can also analyze the user's real-time behavioral data to provide news that is optimal for the situation at hand. For example, it can provide news related to the user's current location based on location information. This makes it possible to provide the most appropriate news based on the user's past news browsing history and interests.

[0031] The news analysis unit can analyze the content of news articles and extract fact-based information that is free from opinion and bias. The news analysis unit, for example, analyzes the content of news articles using natural language processing technology. For example, it uses text mining technology to extract keywords from news articles and eliminate opinion and bias. The news analysis unit can also use a sentiment analysis algorithm to calculate an emotional score for news articles and eliminate emotional bias. For example, it can remove emotional expressions and extract only the facts. The news analysis unit can also automatically translate the content of news articles into different languages ​​to provide neutral reporting from an international perspective. For example, it can translate into multiple languages ​​such as English, French, and Chinese. This allows the content of news articles to be analyzed and fact-based information to be free from opinion and bias.

[0032] The reliability evaluation unit can evaluate the reliability of a news source and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of a news source. For example, it analyzes the content of past reports of a news source and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of a news source based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of a news article and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of a news source and provide highly reliable information.

[0033] The news providing unit can customize news based on the user's values ​​and preferences. The news providing unit customizes news based on the user's values ​​and preferences, for example. For example, the news providing unit analyzes the user's past news browsing history and provides related news. The news providing unit can also analyze the user's subscription history and purchase history and simultaneously provide information on related products and services. For example, it can display advertisements for sporting goods along with sports news. The news providing unit can also provide news according to the user's preferences by voice through a voice assistant. For example, it can provide the latest news by voice during the morning commute. This allows news to be customized based on the user's values ​​and preferences.

[0034] The news providing unit can analyze the user's real-time behavioral data and provide news that is optimal for the situation at the time. The news providing unit, for example, analyzes the user's real-time behavioral data. For example, news related to the user's current location can be provided based on location information. The news providing unit can also analyze device usage and provide short news if the user is using a smartphone, and detailed news if the user is using a computer. The news providing unit can also analyze the user's real-time behavioral data and provide, for example, traffic information and weather forecasts during the user's commute, and industry news during the user's work. This makes it possible to provide optimal news based on the user's real-time behavioral data.

[0035] The news providing unit can analyze the user's social media activity and provide news that reflects changes in interests in real time. The news providing unit, for example, analyzes the user's social media activity. For example, it analyzes the content of posts on social media and reactions such as "likes" to understand changes in interests. The news providing unit can also analyze trends and topics on social media in real time to provide the latest news that matches the user's interests. The news providing unit can also analyze the social media activity of the user's followers and friends to provide news that shares common interests. This makes it possible to provide news that reflects changes in interests based on the user's social media activity.

[0036] The news providing unit can analyze a user's past subscription history and purchase history and simultaneously provide information on related products and services. The news providing unit, for example, analyzes a user's past subscription history and purchase history. For example, based on the news subscription history, advertisements for related products and services are displayed along with the news. The news providing unit can also provide related news and product information based on the user's purchase history. For example, information on eco-friendly products and services is provided to a user who is interested in environmental issues. The news providing unit can also automatically match products and services related to the content of a news article and suggest them to the user. For example, information on travel packages is provided along with travel news. This makes it possible to provide information on related products and services based on the user's past subscription history and purchase history.

[0037] The news providing unit can provide news according to the user's preferences by voice through the voice assistant. For example, the news providing unit provides a function of having the voice assistant read out news according to the user's preferences. For example, the latest news can be provided by voice during the morning commute. The news providing unit can also provide news based on the user's interests by voice through the voice assistant, thereby realizing information provision that does not rely on visual information. For example, the latest recipe news can be provided by voice while the user is cooking. The news providing unit can also provide related news by voice through the voice assistant based on the user's past news browsing history. For example, sports news can be provided by voice. This makes it possible to provide news according to the user's preferences by voice.

[0038] The news analysis unit can perform sentiment analysis of news articles and extract only facts with emotional bias removed. The news analysis unit, for example, uses a sentiment analysis algorithm to perform sentiment analysis of news articles. For example, it uses text mining technology to calculate the sentiment score of a news article. The news analysis unit can also identify emotional expressions and biases and extract only facts. For example, it can remove emotional expressions and extract only facts. The news analysis unit can also build a system that extracts only facts with emotional bias removed based on the results of the sentiment analysis of news articles. For example, it can remove parts with high sentiment scores and provide only facts. This makes it possible to remove emotional bias from news articles and provide only facts.

[0039] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news articles and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news articles. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news articles based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news articles and provide highly reliable information.

[0040] The news analysis unit can analyze the content of news articles and integrate information from different perspectives to generate neutral reports. The news analysis unit, for example, analyzes the content of news articles and develops an algorithm to integrate information from different perspectives. For example, it collects information from multiple news sources and analyzes and integrates the content. The news analysis unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, it compares information from different perspectives and generates balanced reports. The news analysis unit can also provide neutral reports by analyzing the content of news articles and integrating information from different perspectives. For example, it compares information from different perspectives and generates balanced reports. This makes it possible to integrate information from different perspectives in news articles and provide neutral reports.

[0041] The news analysis unit can automatically translate the content of news articles into different languages ​​and provide neutral reporting from an international perspective. The news analysis unit, for example, builds a system that automatically translates the content of news articles into different languages ​​and provides neutral reporting from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The news analysis unit can also provide neutral reporting from an international perspective based on the automatically translated news articles. For example, it provides reporting that takes different cultures and backgrounds into consideration. The news analysis unit can also develop a system that provides neutral reporting from an international perspective based on news articles translated into different languages. For example, it compares translated articles and generates neutral reporting. This makes it possible to automatically translate news articles into different languages ​​and provide neutral reporting from an international perspective.

[0042] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0043] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news sources and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news sources. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news sources based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news sources and provide highly reliable information.

[0044] The news analysis unit can analyze the content of news articles and preferentially extract and provide fact-based information. For example, the news analysis unit can analyze the content of news articles and develop an algorithm that preferentially extracts fact-based information. For example, emotional expressions are eliminated and only facts are extracted. The news analysis unit can also analyze the content of news articles and build a system that preferentially provides fact-based information. For example, fact-based information is highlighted. The news analysis unit can also provide highly reliable reporting by analyzing the content of news articles and preferentially extracting fact-based information. For example, fact-based information is automatically extracted and provided to the user. This allows fact-based information in news articles to be preferentially provided.

[0045] The credibility evaluation unit can integrate information from different perspectives to evaluate the credibility of a news article and generate reliable reporting. For example, the credibility evaluation unit develops an algorithm to integrate information from different perspectives to evaluate the credibility of a news article. For example, the credibility evaluation unit collects information from multiple news sources and analyzes and integrates the content. The credibility evaluation unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, the credibility evaluation unit compares information from different perspectives and generates balanced reporting. The credibility evaluation unit can also provide reliable reporting by analyzing the content of a news article and integrating information from different perspectives. For example, the credibility evaluation unit compares information from different perspectives and generates balanced reporting. This makes it possible to integrate information from different perspectives of a news article and provide reliable reporting.

[0046] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0047] The news analysis unit can perform sentiment analysis of news articles and extract only facts with emotional bias removed. The news analysis unit, for example, uses a sentiment analysis algorithm to perform sentiment analysis of news articles. For example, it uses text mining technology to calculate the sentiment score of a news article. The news analysis unit can also identify emotional expressions and biases and extract only facts. For example, it can remove emotional expressions and extract only facts. The news analysis unit can also build a system that extracts only facts with emotional bias removed based on the results of the sentiment analysis of news articles. For example, it can remove parts with high sentiment scores and provide only facts. This makes it possible to remove emotional bias from news articles and provide only facts.

[0048] The news analysis unit can analyze the content of news articles and integrate information from different perspectives to generate neutral reports. The news analysis unit, for example, analyzes the content of news articles and develops an algorithm to integrate information from different perspectives. For example, it collects information from multiple news sources and analyzes and integrates the content. The news analysis unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, it compares information from different perspectives and generates balanced reports. The news analysis unit can also provide neutral reports by analyzing the content of news articles and integrating information from different perspectives. For example, it compares information from different perspectives and generates balanced reports. This makes it possible to integrate information from different perspectives in news articles and provide neutral reports.

[0049] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news articles and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news articles. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news articles based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news articles and provide highly reliable information.

[0050] The news analysis unit can automatically translate the content of news articles into different languages ​​and provide neutral reporting from an international perspective. The news analysis unit, for example, builds a system that automatically translates the content of news articles into different languages ​​and provides neutral reporting from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The news analysis unit can also provide neutral reporting from an international perspective based on the automatically translated news articles. For example, it provides reporting that takes different cultures and backgrounds into consideration. The news analysis unit can also develop a system that provides neutral reporting from an international perspective based on news articles translated into different languages. For example, it compares translated articles and generates neutral reporting. This makes it possible to automatically translate news articles into different languages ​​and provide neutral reporting from an international perspective.

[0051] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0052] The news providing unit can analyze the user's real-time behavioral data and provide news that is optimal for the situation at the time. The news providing unit, for example, analyzes the user's real-time behavioral data. For example, news related to the user's current location can be provided based on location information. The news providing unit can also analyze device usage and provide short news if the user is using a smartphone, and detailed news if the user is using a computer. The news providing unit can also analyze the user's real-time behavioral data and provide, for example, traffic information and weather forecasts during the user's commute, and industry news during the user's work. This makes it possible to provide optimal news based on the user's real-time behavioral data.

[0053] The news providing unit can analyze a user's past subscription history and purchase history and simultaneously provide information on related products and services. The news providing unit, for example, analyzes a user's past subscription history and purchase history. For example, based on the news subscription history, advertisements for related products and services are displayed along with the news. The news providing unit can also provide related news and product information based on the user's purchase history. For example, information on eco-friendly products and services is provided to a user who is interested in environmental issues. The news providing unit can also automatically match products and services related to the content of a news article and suggest them to the user. For example, information on travel packages is provided along with travel news. This makes it possible to provide information on related products and services based on the user's past subscription history and purchase history.

[0054] The news providing unit can provide news according to the user's preferences by voice through the voice assistant. For example, the news providing unit provides a function of having the voice assistant read out news according to the user's preferences. For example, the latest news can be provided by voice during the morning commute. The news providing unit can also provide news based on the user's interests by voice through the voice assistant, thereby realizing information provision that does not rely on visual information. For example, the latest recipe news can be provided by voice while the user is cooking. The news providing unit can also provide related news by voice through the voice assistant based on the user's past news browsing history. For example, sports news can be provided by voice. This makes it possible to provide news according to the user's preferences by voice.

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

[0056] The news delivery system can also monitor the user's health status and provide health-related news. For example, it can analyze data obtained from the user's fitness tracker or smartwatch to provide the latest health research and advice. It can also analyze the user's food log to provide nutritional news and recipes. It can also use the user's sleep data to provide news and information on improving sleep quality.

[0057] The news providing system can also provide news related to the user's hobbies and special skills. For example, if the user is interested in music, the system can provide the latest music news and concert information. If the user is interested in sports, the system can provide game results and player interviews. Furthermore, if the user is interested in cooking, the system can provide news about new recipes and cooking tips.

[0058] The news system can also analyze a user's learning history and provide education-related news. For example, if a user is taking an online course, it can provide the latest research and trends related to that field. If a user is preparing for a specific certification exam, it can provide exam-related news and advice. And if a user is learning a new language, it can provide culture and news related to that language.

[0059] The news system can also analyze the user's travel history and provide travel-related news. For example, it can provide the latest tourist information and event information related to places the user has visited in the past. It can also provide news and safety information about travel destinations the user is planning. It can also provide news about the culture and history of travel destinations that the user is interested in.

[0060] The news system can also provide relevant news based on the magazines and books a user subscribes to. For example, if a user subscribes to fashion magazines, the system can provide news about the latest fashion trends and interviews with designers. If a user subscribes to science magazines, the system can provide news about the latest scientific research and discoveries. Furthermore, if a user prefers fiction novels, the system can provide news related to that genre and interviews with authors.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The news selection unit selects news according to the user's personal preferences. For example, the news selection unit analyzes the user's past news browsing history and interests to select the most appropriate news for each individual user. The news selection unit can also analyze the user's social media activity and provide news that reflects changes in interests in real time. Furthermore, the news selection unit can analyze the user's real-time behavioral data to provide the most appropriate news for the situation at hand. Step 2: The news analysis unit analyzes the news selected by the news selection unit. For example, the news analysis unit analyzes the content of news articles to extract fact-based information without opinion or bias. The news analysis unit can also perform sentiment analysis of news articles to extract only facts without emotional bias. Furthermore, the news analysis unit can automatically translate the content of news articles into different languages ​​to provide neutral reporting from an international perspective. Step 3: The credibility evaluation unit evaluates the credibility of the news analyzed by the news analysis unit. For example, the credibility evaluation unit evaluates the credibility of the news source and provides highly reliable information preferentially. The credibility evaluation unit can also analyze past reporting history and credibility scores to evaluate the credibility of a news article and provide highly reliable information preferentially. Furthermore, the credibility evaluation unit can integrate information from different perspectives to evaluate the credibility of a news article and generate highly reliable reports. Step 4: The news providing unit provides the news evaluated by the reliability evaluation unit to the user. For example, the news providing unit customizes the news based on the user's values ​​and preferences. The news providing unit can also estimate the user's emotions and provide news that elicits positive emotions preferentially. Furthermore, the news providing unit can provide news according to the user's preferences by voice through a voice assistant. This allows the news providing system according to the embodiment to provide unbiased, fact-based news according to the user's personal preferences.

[0063] (Example 2) A news delivery system according to an embodiment of the present invention is a system that provides a news experience that is unbiased and fact-based according to the user's personal preferences. This allows the news delivery system to provide news that is unbiased and fact-based according to the user's personal preferences.

[0064] A news provision system according to an embodiment includes a news selection unit, a news analysis unit, a reliability evaluation unit, and a news provision unit. The news selection unit selects news according to a user's personal preferences. For example, the news selection unit analyzes a user's past news viewing history and interests to select news that is optimal for each individual user. The news selection unit can also analyze a user's social media activities to provide news that reflects changes in interests in real time. The news selection unit can also analyze a user's real-time behavioral data to provide news that is optimal for the user's current situation. The news analysis unit analyzes the news selected by the news selection unit. For example, the news analysis unit analyzes the content of a news article to extract fact-based information that is free from opinions and bias. The news analysis unit can also perform sentiment analysis of the news article to extract only facts that are free from emotional bias. The news analysis unit can also automatically translate the content of the news article into different languages ​​to provide neutral reporting from an international perspective. The reliability evaluation unit evaluates the reliability of the news analyzed by the news analysis unit. For example, the reliability evaluation unit evaluates the reliability of a news source and prioritizes providing reliable information. The reliability evaluation unit can also analyze past reporting history and reliability scores to evaluate the reliability of a news article and prioritize providing reliable information. The reliability evaluation unit can also integrate information from different perspectives to generate reliable reports to evaluate the reliability of a news article. The news providing unit provides the news evaluated by the reliability evaluation unit to the user. For example, the news providing unit customizes news based on the user's values ​​and preferences. The news providing unit can also estimate the user's emotions and prioritize providing news that elicits positive emotions. The news providing unit can also provide news that meets the user's preferences via voice through a voice assistant. This allows the news providing system according to the embodiment to provide unbiased, fact-based news that meets the user's personal preferences.For example, the output unit may display the news to the user through a web application or a mobile application, print the results using a printer if paper feedback is desired, or send the news via email directly to the user to provide quick feedback.

[0065] The news selection unit can analyze a user's past news browsing history and interests to select the most appropriate news for each individual user. The news selection unit, for example, analyzes interests based on the user's news browsing history. For example, it extracts keywords from the news browsing history to identify the user's interests. The news selection unit can also analyze the user's social media activity to provide news that reflects changes in interests in real time. For example, it analyzes the content of posts on social media and reactions such as "likes" to understand changes in interests. The news selection unit can also analyze the user's real-time behavioral data to provide news that is optimal for the situation at hand. For example, it can provide news related to the user's current location based on location information. This makes it possible to provide the most appropriate news based on the user's past news browsing history and interests.

[0066] The news analysis unit can analyze the content of news articles and extract fact-based information that is free from opinion and bias. The news analysis unit, for example, analyzes the content of news articles using natural language processing technology. For example, it uses text mining technology to extract keywords from news articles and eliminate opinion and bias. The news analysis unit can also use a sentiment analysis algorithm to calculate an emotional score for news articles and eliminate emotional bias. For example, it can remove emotional expressions and extract only the facts. The news analysis unit can also automatically translate the content of news articles into different languages ​​to provide neutral reporting from an international perspective. For example, it can translate into multiple languages ​​such as English, French, and Chinese. This allows the content of news articles to be analyzed and fact-based information to be free from opinion and bias.

[0067] The reliability evaluation unit can evaluate the reliability of a news source and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of a news source. For example, it analyzes the content of past reports of a news source and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of a news source based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of a news article and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of a news source and provide highly reliable information.

[0068] The news providing unit can customize news based on the user's values ​​and preferences. The news providing unit customizes news based on the user's values ​​and preferences, for example. For example, the news providing unit analyzes the user's past news browsing history and provides related news. The news providing unit can also analyze the user's subscription history and purchase history and simultaneously provide information on related products and services. For example, it can display advertisements for sporting goods along with sports news. The news providing unit can also provide news according to the user's preferences by voice through a voice assistant. For example, it can provide the latest news by voice during the morning commute. This allows news to be customized based on the user's values ​​and preferences.

[0069] The news providing unit can estimate the user's emotions and provide news that elicits positive emotions with priority. The news providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, it analyzes the user's facial expressions and voice when reading the news and calculates an emotion score. The news providing unit can also monitor the user's heart rate and galvanic skin response when reading the news and analyze the emotional state in real time. For example, it calculates an emotion score based on heart rate fluctuations. The news providing unit can also combine the user's past news reading history with emotional response data to predict news that elicits positive emotions and provide it with priority. For example, it can display news with a high positive emotion score with priority. This makes it possible to provide positive news based on the user's emotions.

[0070] The news providing unit can analyze the user's real-time behavioral data and provide news that is optimal for the situation at the time. The news providing unit, for example, analyzes the user's real-time behavioral data. For example, news related to the user's current location can be provided based on location information. The news providing unit can also analyze device usage and provide short news if the user is using a smartphone, and detailed news if the user is using a computer. The news providing unit can also analyze the user's real-time behavioral data and provide, for example, traffic information and weather forecasts during the user's commute, and industry news during the user's work. This makes it possible to provide optimal news based on the user's real-time behavioral data.

[0071] The news providing unit can analyze the user's social media activity and provide news that reflects changes in interests in real time. The news providing unit, for example, analyzes the user's social media activity. For example, it analyzes the content of posts on social media and reactions such as "likes" to understand changes in interests. The news providing unit can also analyze trends and topics on social media in real time to provide the latest news that matches the user's interests. The news providing unit can also analyze the social media activity of the user's followers and friends to provide news that shares common interests. This makes it possible to provide news that reflects changes in interests based on the user's social media activity.

[0072] The news providing unit can analyze a user's past subscription history and purchase history and simultaneously provide information on related products and services. The news providing unit, for example, analyzes a user's past subscription history and purchase history. For example, based on the news subscription history, advertisements for related products and services are displayed along with the news. The news providing unit can also provide related news and product information based on the user's purchase history. For example, information on eco-friendly products and services is provided to a user who is interested in environmental issues. The news providing unit can also automatically match products and services related to the content of a news article and suggest them to the user. For example, information on travel packages is provided along with travel news. This makes it possible to provide information on related products and services based on the user's past subscription history and purchase history.

[0073] The news providing unit can provide news according to the user's preferences by voice through the voice assistant. For example, the news providing unit provides a function of having the voice assistant read out news according to the user's preferences. For example, the latest news can be provided by voice during the morning commute. The news providing unit can also provide news based on the user's interests by voice through the voice assistant, thereby realizing information provision that does not rely on visual information. For example, the latest recipe news can be provided by voice while the user is cooking. The news providing unit can also provide related news by voice through the voice assistant based on the user's past news browsing history. For example, sports news can be provided by voice. This makes it possible to provide news according to the user's preferences by voice.

[0074] The news providing unit can use the emotion estimation function to analyze the emotions of a user when viewing news in real time and provide news feedback according to the emotions. The news providing unit, for example, analyzes the facial expressions and voice of the user when viewing news and calculates an emotion score. For example, it uses facial expression recognition technology to estimate emotions from the user's facial expressions. The news providing unit can also monitor the user's heart rate and galvanic skin response when viewing news and analyze the emotional state in real time. For example, it can calculate an emotion score based on heart rate fluctuations. The news providing unit can also combine the user's past news viewing history with emotional response data to provide feedback according to the emotions and adjust the content of the news. For example, it can preferentially display news with a high emotion score. This makes it possible to provide news feedback based on the user's emotions.

[0075] The news analysis unit can perform sentiment analysis of news articles and extract only facts with emotional bias removed. The news analysis unit, for example, uses a sentiment analysis algorithm to perform sentiment analysis of news articles. For example, it uses text mining technology to calculate the sentiment score of a news article. The news analysis unit can also identify emotional expressions and biases and extract only facts. For example, it can remove emotional expressions and extract only facts. The news analysis unit can also build a system that extracts only facts with emotional bias removed based on the results of the sentiment analysis of news articles. For example, it can remove parts with high sentiment scores and provide only facts. This makes it possible to remove emotional bias from news articles and provide only facts.

[0076] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news articles and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news articles. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news articles based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news articles and provide highly reliable information.

[0077] The news analysis unit can analyze the content of news articles and integrate information from different perspectives to generate neutral reports. The news analysis unit, for example, analyzes the content of news articles and develops an algorithm to integrate information from different perspectives. For example, it collects information from multiple news sources and analyzes and integrates the content. The news analysis unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, it compares information from different perspectives and generates balanced reports. The news analysis unit can also provide neutral reports by analyzing the content of news articles and integrating information from different perspectives. For example, it compares information from different perspectives and generates balanced reports. This makes it possible to integrate information from different perspectives in news articles and provide neutral reports.

[0078] The news analysis unit can automatically translate the content of news articles into different languages ​​and provide neutral reporting from an international perspective. The news analysis unit, for example, builds a system that automatically translates the content of news articles into different languages ​​and provides neutral reporting from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The news analysis unit can also provide neutral reporting from an international perspective based on the automatically translated news articles. For example, it provides reporting that takes different cultures and backgrounds into consideration. The news analysis unit can also develop a system that provides neutral reporting from an international perspective based on news articles translated into different languages. For example, it compares translated articles and generates neutral reporting. This makes it possible to automatically translate news articles into different languages ​​and provide neutral reporting from an international perspective.

[0079] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0080] The news analysis unit can use the emotion estimation function to collect users' emotional reactions to news articles and provide emotionally neutral reports. The news analysis unit, for example, collects users' emotional reactions to news articles in real time and provides emotionally neutral reports based on that data. For example, articles with a high number of positive emotional reactions are preferentially displayed. The news analysis unit can also use the emotion estimation function to analyze users' emotional scores for news articles and build a system that provides emotionally neutral reports. For example, articles with low emotional scores are eliminated. The news analysis unit can also provide emotionally neutral reports based on users' emotional reaction data. For example, parts with high emotional scores are deleted and only facts are provided. This makes it possible to provide emotionally neutral reports based on users' emotional reactions to news articles.

[0081] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news sources and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news sources. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news sources based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news sources and provide highly reliable information.

[0082] The news analysis unit can analyze the content of news articles and preferentially extract and provide fact-based information. For example, the news analysis unit can analyze the content of news articles and develop an algorithm that preferentially extracts fact-based information. For example, emotional expressions are eliminated and only facts are extracted. The news analysis unit can also analyze the content of news articles and build a system that preferentially provides fact-based information. For example, fact-based information is highlighted. The news analysis unit can also provide highly reliable reporting by analyzing the content of news articles and preferentially extracting fact-based information. For example, fact-based information is automatically extracted and provided to the user. This allows fact-based information in news articles to be preferentially provided.

[0083] The credibility evaluation unit can integrate information from different perspectives to evaluate the credibility of a news article and generate reliable reporting. For example, the credibility evaluation unit develops an algorithm to integrate information from different perspectives to evaluate the credibility of a news article. For example, the credibility evaluation unit collects information from multiple news sources and analyzes and integrates the content. The credibility evaluation unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, the credibility evaluation unit compares information from different perspectives and generates balanced reporting. The credibility evaluation unit can also provide reliable reporting by analyzing the content of a news article and integrating information from different perspectives. For example, the credibility evaluation unit compares information from different perspectives and generates balanced reporting. This makes it possible to integrate information from different perspectives of a news article and provide reliable reporting.

[0084] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0085] The news analysis unit can use the emotion estimation function to collect users' emotional reactions to news articles and provide emotionally reliable news reports. The news analysis unit, for example, collects users' emotional reactions to news articles in real time and provides emotionally reliable news reports based on that data. For example, articles with a high number of positive emotional reactions are preferentially displayed. The news analysis unit can also use the emotion estimation function to analyze users' emotional scores for news articles and build a system that provides emotionally reliable news reports. For example, articles with low emotional scores are eliminated. The news analysis unit can also provide emotionally reliable news reports based on user emotional reaction data. For example, parts with high emotional scores are deleted and only facts are provided. This makes it possible to provide emotionally reliable news reports based on users' emotional reactions to news articles.

[0086] The news analysis unit can perform sentiment analysis of news articles and extract only facts with emotional bias removed. The news analysis unit, for example, uses a sentiment analysis algorithm to perform sentiment analysis of news articles. For example, it uses text mining technology to calculate the sentiment score of a news article. The news analysis unit can also identify emotional expressions and biases and extract only facts. For example, it can remove emotional expressions and extract only facts. The news analysis unit can also build a system that extracts only facts with emotional bias removed based on the results of the sentiment analysis of news articles. For example, it can remove parts with high sentiment scores and provide only facts. This makes it possible to remove emotional bias from news articles and provide only facts.

[0087] The news analysis unit can analyze the content of news articles and integrate information from different perspectives to generate neutral reports. The news analysis unit, for example, analyzes the content of news articles and develops an algorithm to integrate information from different perspectives. For example, it collects information from multiple news sources and analyzes and integrates the content. The news analysis unit can also build a system that collects news articles from different perspectives and analyzes and integrates the content. For example, it compares information from different perspectives and generates balanced reports. The news analysis unit can also provide neutral reports by analyzing the content of news articles and integrating information from different perspectives. For example, it compares information from different perspectives and generates balanced reports. This makes it possible to integrate information from different perspectives in news articles and provide neutral reports.

[0088] The reliability evaluation unit can analyze past reporting history and reliability scores to evaluate the reliability of news articles and provide highly reliable information preferentially. The reliability evaluation unit, for example, analyzes past reporting history to evaluate the reliability of news articles. For example, it analyzes the content of past reports from news sources and calculates a reliability score. The reliability evaluation unit can also evaluate the reliability of news articles based on the reliability score and provide highly reliable information preferentially. For example, it can display news sources with high reliability scores preferentially. The reliability evaluation unit can also integrate information from different perspectives to evaluate the reliability of news articles and generate highly reliable reports. For example, it collects information from multiple news sources and generates highly reliable reports. This makes it possible to evaluate the reliability of news articles and provide highly reliable information.

[0089] The news analysis unit can automatically translate the content of news articles into different languages ​​and provide neutral reporting from an international perspective. The news analysis unit, for example, builds a system that automatically translates the content of news articles into different languages ​​and provides neutral reporting from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The news analysis unit can also provide neutral reporting from an international perspective based on the automatically translated news articles. For example, it provides reporting that takes different cultures and backgrounds into consideration. The news analysis unit can also develop a system that provides neutral reporting from an international perspective based on news articles translated into different languages. For example, it compares translated articles and generates neutral reporting. This makes it possible to automatically translate news articles into different languages ​​and provide neutral reporting from an international perspective.

[0090] The news analysis unit can convert the content of news articles into visual notes or infographics, and provide them in a format that is visually easy to understand. For example, the news analysis unit builds a system that converts the content of news articles into visual notes and provides them in a format that is visually easy to understand. For example, it can show important points using diagrams or icons. The news analysis unit can also convert the content of news articles into infographics and provide them in a format that is visually easy to understand. For example, it can display data and statistical information in graphs and charts. The news analysis unit can also develop tools that automatically generate visual notes and infographics, and visually display the content of news articles. For example, it can provide a function for visualization using drag and drop. This makes it possible to provide news articles in a format that is visually easy to understand.

[0091] The news analysis unit can use the emotion estimation function to collect users' emotional reactions to news articles and provide emotionally neutral reports. The news analysis unit, for example, collects users' emotional reactions to news articles in real time and provides emotionally neutral reports based on that data. For example, articles with a high number of positive emotional reactions are preferentially displayed. The news analysis unit can also use the emotion estimation function to analyze users' emotional scores for news articles and build a system that provides emotionally neutral reports. For example, articles with low emotional scores are eliminated. The news analysis unit can also provide emotionally neutral reports based on users' emotional reaction data. For example, parts with high emotional scores are deleted and only facts are provided. This makes it possible to provide emotionally neutral reports based on users' emotional reactions to news articles.

[0092] The news providing unit can estimate the user's emotions and provide news that elicits positive emotions with priority. The news providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, it analyzes the user's facial expressions and voice when reading the news and calculates an emotion score. The news providing unit can also monitor the user's heart rate and galvanic skin response when reading the news and analyze the emotional state in real time. For example, it calculates an emotion score based on heart rate fluctuations. The news providing unit can also combine the user's past news reading history with emotional response data to predict news that elicits positive emotions and provide it with priority. For example, it can display news with a high positive emotion score with priority. This makes it possible to provide positive news based on the user's emotions.

[0093] The news providing unit can analyze the user's real-time behavioral data and provide news that is optimal for the situation at the time. The news providing unit, for example, analyzes the user's real-time behavioral data. For example, news related to the user's current location can be provided based on location information. The news providing unit can also analyze device usage and provide short news if the user is using a smartphone, and detailed news if the user is using a computer. The news providing unit can also analyze the user's real-time behavioral data and provide, for example, traffic information and weather forecasts during the user's commute, and industry news during the user's work. This makes it possible to provide optimal news based on the user's real-time behavioral data.

[0094] The news providing unit can analyze a user's past subscription history and purchase history and simultaneously provide information on related products and services. The news providing unit, for example, analyzes a user's past subscription history and purchase history. For example, based on the news subscription history, advertisements for related products and services are displayed along with the news. The news providing unit can also provide related news and product information based on the user's purchase history. For example, information on eco-friendly products and services is provided to a user who is interested in environmental issues. The news providing unit can also automatically match products and services related to the content of a news article and suggest them to the user. For example, information on travel packages is provided along with travel news. This makes it possible to provide information on related products and services based on the user's past subscription history and purchase history.

[0095] The news providing unit can provide news according to the user's preferences by voice through the voice assistant. For example, the news providing unit provides a function of having the voice assistant read out news according to the user's preferences. For example, the latest news can be provided by voice during the morning commute. The news providing unit can also provide news based on the user's interests by voice through the voice assistant, thereby realizing information provision that does not rely on visual information. For example, the latest recipe news can be provided by voice while the user is cooking. The news providing unit can also provide related news by voice through the voice assistant based on the user's past news browsing history. For example, sports news can be provided by voice. This makes it possible to provide news according to the user's preferences by voice.

[0096] The news providing unit can use the emotion estimation function to analyze the emotions of a user when viewing news in real time and provide news feedback according to the emotions. The news providing unit, for example, analyzes the facial expressions and voice of the user when viewing news and calculates an emotion score. For example, it uses facial expression recognition technology to estimate emotions from the user's facial expressions. The news providing unit can also monitor the user's heart rate and galvanic skin response when viewing news and analyze the emotional state in real time. For example, it can calculate an emotion score based on heart rate fluctuations. The news providing unit can also combine the user's past news viewing history with emotional response data to provide feedback according to the emotions and adjust the content of the news. For example, it can preferentially display news with a high emotion score. This makes it possible to provide news feedback based on the user's emotions.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The news delivery system can also monitor the user's health status and provide health-related news. For example, it can analyze data obtained from the user's fitness tracker or smartwatch to provide the latest health research and advice. It can also analyze the user's food log to provide nutritional news and recipes. It can also use the user's sleep data to provide news and information on improving sleep quality.

[0099] The news providing system can also provide news related to the user's hobbies and special skills. For example, if the user is interested in music, the system can provide the latest music news and concert information. If the user is interested in sports, the system can provide game results and player interviews. Furthermore, if the user is interested in cooking, the system can provide news about new recipes and cooking tips.

[0100] The news system can also analyze a user's learning history and provide education-related news. For example, if a user is taking an online course, it can provide the latest research and trends related to that field. If a user is preparing for a specific certification exam, it can provide exam-related news and advice. And if a user is learning a new language, it can provide culture and news related to that language.

[0101] The news system can also analyze the user's travel history and provide travel-related news. For example, it can provide the latest tourist information and event information related to places the user has visited in the past. It can also provide news and safety information about travel destinations the user is planning. It can also provide news about the culture and history of travel destinations that the user is interested in.

[0102] The news system can also provide relevant news based on the magazines and books a user subscribes to. For example, if a user subscribes to fashion magazines, the system can provide news about the latest fashion trends and interviews with designers. If a user subscribes to science magazines, the system can provide news about the latest scientific research and discoveries. Furthermore, if a user prefers fiction novels, the system can provide news related to that genre and interviews with authors.

[0103] The news provider can also estimate the user's emotions and provide news that helps reduce stress with priority. For example, if the user is feeling stressed, news related to relaxation and mental health can be provided. If the user is feeling relaxed, news related to entertainment and hobbies can be provided. Furthermore, it is possible to select news that elicits positive emotions according to the user's emotional state.

[0104] The news provider can also estimate the user's emotions and provide news that provides emotional support preferentially. For example, if the user is sad, it can provide news that gives encouragement and hope. If the user is angry, it can provide news and information to help the user regain their composure. Furthermore, if the user is feeling anxious, it can select news that provides a sense of security.

[0105] The news provider can also estimate the user's emotions and provide news to maintain emotional balance. For example, if the user is overly excited, news to help them regain their composure can be provided. Also, if the user is depressed, news and information to cheer them up can be provided. Furthermore, it is possible to select news to maintain emotional balance according to the user's emotional state.

[0106] The news provider can also estimate the user's emotions and provide news that elicits emotional empathy. For example, if the user feels lonely, news that elicits empathy and connection can be provided. Also, if the user feels joy, news and information that allows the user to share that emotion can be provided. Furthermore, it is possible to select news that elicits empathy depending on the user's emotional state.

[0107] The news provider can also estimate the user's emotions and provide news to encourage emotional growth. For example, if the user is interested in self-improvement, news related to self-development and skill development can be provided. If the user is seeking a new challenge, news and information related to challenges and adventures can be provided. Furthermore, news to encourage emotional growth can be selected according to the user's emotional state.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The news selection unit selects news according to the user's personal preferences. For example, the news selection unit analyzes the user's past news browsing history and interests to select the most appropriate news for each individual user. The news selection unit can also analyze the user's social media activity and provide news that reflects changes in interests in real time. Furthermore, the news selection unit can analyze the user's real-time behavioral data to provide the most appropriate news for the situation at hand. Step 2: The news analysis unit analyzes the news selected by the news selection unit. For example, the news analysis unit analyzes the content of news articles to extract fact-based information without opinion or bias. The news analysis unit can also perform sentiment analysis of news articles to extract only facts without emotional bias. Furthermore, the news analysis unit can automatically translate the content of news articles into different languages ​​to provide neutral reporting from an international perspective. Step 3: The credibility evaluation unit evaluates the credibility of the news analyzed by the news analysis unit. For example, the credibility evaluation unit evaluates the credibility of the news source and provides highly reliable information preferentially. The credibility evaluation unit can also analyze past reporting history and credibility scores to evaluate the credibility of a news article and provide highly reliable information preferentially. Furthermore, the credibility evaluation unit can integrate information from different perspectives to evaluate the credibility of a news article and generate highly reliable reports. Step 4: The news providing unit provides the news evaluated by the reliability evaluation unit to the user. For example, the news providing unit customizes the news based on the user's values ​​and preferences. The news providing unit can also estimate the user's emotions and provide news that elicits positive emotions preferentially. Furthermore, the news providing unit can provide news according to the user's preferences by voice through a voice assistant. This allows the news providing system according to the embodiment to provide unbiased, fact-based news according to the user's personal preferences.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

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

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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]

[0177] 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. a news selection unit that selects news according to the user's personal preferences; a news analysis unit that analyzes the news selected by the news selection unit; a reliability evaluation unit that evaluates the reliability of the news analyzed by the news analysis unit; a news providing unit that provides the news evaluated by the reliability evaluation unit to a user. A system characterized by:

2. The news selection unit Analyze the user's past news browsing history and interests to select the most suitable news for each individual user.

2. The system of claim 1.

3. The news analysis unit Analyze the content of news articles and extract factual information without opinion or bias 2. The system of claim 1.

4. The reliability evaluation unit Evaluate the reliability of news sources and prioritize providing such reliable information 2. The system of claim 1.

5. The news providing unit Customize news based on the user's values ​​and preferences 2. The system of claim 1.

Images