System

Abstract

An object of a system according to an embodiment is to examine the authenticity of information collected from social media and websites and provide real-time news to users.SOLUTION: A system includes a collection unit, an inspection unit, a generation unit, and a provision unit. The collection unit collects information from social media and websites. The close inspection part closely inspects the authenticity of the information collected by the collection part. The generation unit generates news based on the information reviewed by the reviewing unit. The provision unit provides the user with the news generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately examine the authenticity of information from social media and websites quickly and accurately and provide this information to users, and there is room for improvement.

[0005] The system according to the embodiment aims to scrutinize the authenticity of information collected from social media and websites and provide real-time news to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a scrutiny unit, a generation unit, and a provision unit. The collection unit collects information from social media and websites. The scrutiny unit scrutinizes the authenticity of the information collected by the collection unit. The generation unit generates news based on the information scrutinized by the scrutiny unit. The provision unit provides the news generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can scrutinize the authenticity of information collected from social media and websites and provide real-time news to users. [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 uses a generation AI to collect the latest information from social media and other online platforms, scrutinize the authenticity of the collected information, and provide real-time news to users. The news delivery system collects the latest information from social media and other online platforms, uses a generation AI to scrutinize the authenticity of the collected information, and generates real-time news based on the scrutinized information and provides it to users. For example, the news delivery system collects information based on specific keywords or topics. For example, it can collect information about specific incidents or disasters. The news delivery system then scrutinizes the authenticity of the collected information using a generation AI. The generation AI analyzes the collected information and distinguishes between highly reliable and less reliable information. For example, it can evaluate the reliability of the information by checking the source and consistency of the content. The news delivery system then generates real-time news based on the scrutinized information and provides it to users. The generation AI organizes the scrutinized information and generates news in a format that is easy for users to understand. For example, it can generate news articles or breaking news summarizing key points. This allows the news delivery system to receive reliable news in real time from the latest information overflowing on social media and other platforms. This improves the reliability of information and makes information gathering more efficient for users. For example, users can receive reliable real-time news, improving the accuracy and reliability of information. Also, users can quickly obtain the latest information, improving the efficiency of information gathering.

[0029] A news provision system according to an embodiment includes a collection unit, a review unit, a generation unit, and a provision unit. The collection unit collects information from social media and websites. The collection unit can collect information based on, for example, specific keywords or topics. The collection unit can acquire data using scraping technology or an API. For example, the collection unit can collect information related to specific incidents or disasters. The review unit reviews the authenticity of the information collected by the collection unit. The review unit can review the authenticity of the information based on, for example, the source of the information and the consistency of its content as evaluation criteria. The review unit can evaluate the reliability of the information based on reliable media sources or official announcements. For example, the review unit can evaluate whether the source of the information is reliable media. The generation unit generates news based on the information reviewed by the review unit. For example, the generation unit can generate news articles or breaking news summarizing important points based on the reviewed information. The generation unit can generate news using natural language generation technology or template-based generation technology. For example, the generation unit can extract important points and generate news in a format that is easy for users to understand. The providing unit provides the news generated by the generating unit to the user. The providing unit can provide the generated news to the user in, for example, a text format or a multimedia format. The providing unit can provide the news to the user via a website, a mobile app, email distribution, or other methods. For example, the providing unit can provide the generated news to the user via a website or a mobile app. As a result, the news providing system according to the embodiment can provide the user with reliable real-time news by collecting information from social media and online platforms, examining the authenticity of the information, and generating and providing the news.

[0030] The collection unit can collect information based on specific keywords or topics. For example, the collection unit can collect information based on specific keywords or topics. The collection unit can collect information related to specific incidents or disasters. For example, the collection unit can collect information based on specific keywords such as politics, economics, and sports. The collection unit can efficiently collect information related to specific topics. As a result, highly relevant information can be efficiently collected by collecting information based on specific keywords or topics. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a prompt to collect information based on specific keywords or topics to the generation AI, and cause the generation AI to collect information.

[0031] The scrutiny unit can scrutinize the authenticity of information using the source of the information or the consistency of its content as evaluation criteria. For example, the scrutiny unit can scrutinize the authenticity of information using the source of the information or the consistency of its content as evaluation criteria. The scrutiny unit can evaluate the reliability of information based on reliable media or official announcements. For example, the scrutiny unit can evaluate whether the source of the information is a reliable media. The scrutiny unit can also evaluate the consistency of the content of the information. For example, the scrutiny unit can check whether there are matches or contradictions with multiple information sources. This makes it possible to provide highly reliable information by scrutinizing the authenticity of information using the consistency of the source of the information or the content as evaluation criteria. Some or all of the above-mentioned processing in the scrutiny unit may be performed using, for example, AI, or may be performed without using AI. For example, the scrutiny unit can input a prompt to the generation AI to evaluate the source of the information or the consistency of its content, and cause the generation AI to scrutinize the authenticity of the information.

[0032] The generation unit can generate news articles and breaking news summarizing important points based on the scrutinized information. The generation unit can generate news articles and breaking news summarizing important points based on the scrutinized information, for example. The generation unit can generate news using natural language generation technology or template-based generation technology. For example, the generation unit can extract important points and generate news in a format that is easy for users to understand. The generation unit can also generate news using a generation AI. For example, the generation unit can input scrutinized information to the generation AI and cause the generation AI to generate news articles and breaking news. In this way, by generating news articles and breaking news summarizing important points based on the scrutinized information, it is possible to provide news that is easy for users to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0033] The providing unit can provide the generated news to the user in text format or multimedia format. For example, the providing unit can provide the generated news to the user in text format or multimedia format. The providing unit can provide the news via a website, a mobile app, email distribution, or other methods. For example, the providing unit can provide the generated news to the user through a website or a mobile app. The providing unit can also provide the generated news in text format or multimedia format such as images, videos, or audio. In this way, by providing the generated news in text format or multimedia format, news can be provided in a format that meets the user's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt to the generation AI to provide the generated news to the user, and cause the generation AI to provide the news.

[0034] The providing unit can collect user feedback and feed it back to the scrutiny unit or the generation unit. The providing unit can, for example, collect user feedback and feed it back to the scrutiny unit or the generation unit. The providing unit can collect user feedback by methods such as questionnaires, comments, and ratings. For example, the providing unit can collect ratings and comments that users have given to news and feed them back to the scrutiny unit or the generation unit. In this way, by collecting user feedback and feeding it back to the scrutiny unit or the generation unit, the accuracy of the system can be improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input a prompt to collect user feedback to the generation AI and cause the generation AI to collect feedback.

[0035] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit can analyze the user's past information collection history and select the optimal collection method. The collection unit can analyze the type, frequency, time period, etc. of information collected in the past. For example, the collection unit can prioritize collecting topics that the user frequently collected in the past. The collection unit can also prioritize collection methods (RSS feeds, social media, etc.) that the user has used in the past. Furthermore, the collection unit can predict the information to be collected in a specific time period from the user's past information collection history and select the optimal collection method. This allows the optimal collection method to be selected and information to be collected efficiently by analyzing the user's past information collection history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input a prompt to analyze the user's past information collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit may filter information based on the user's current areas of interest or topics when collecting information. For example, the collection unit may filter information based on the user's current areas of interest or topics when collecting information. The collection unit may identify the user's current areas of interest or topics based on the user's search history, browsing history, social media activity, etc. For example, the collection unit may collect only information related to topics in which the user is currently interested. The collection unit may also filter information using related keywords based on the user's areas of interest. Furthermore, the collection unit may collect information only from specific sources based on the user's current areas of interest. This allows highly relevant information to be collected by filtering information based on the user's current areas of interest or topics. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input a prompt identifying the user's current areas of interest or topics to the generation AI and cause the generation AI to filter the information.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. The collection unit can collect information using voice recognition technology, text analysis technology, image recognition technology, etc. For example, if the user is using voice input, the collection unit can collect information using voice recognition technology. Also, if the user is using text input, the collection unit can collect information using text analysis technology. Furthermore, if the user is using image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a prompt to the generation AI to select a collection means depending on the user's input method, and cause the generation AI to select the collection means.

[0038] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. The collection unit can acquire the user's geographical location information using GPS data, an IP address, a location information service, etc. For example, the collection unit can prioritize collecting news related to the area where the user is currently located. The collection unit can also prioritize collecting information related to places the user has visited in the past. Furthermore, the collection unit can prioritize collecting information related to travel destinations the user is planning. This makes it possible to provide important information related to the area by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input an information collection prompt that takes into account the user's geographical location information to the generation AI, causing the generation AI to collect information.

[0039] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can analyze the social media activity based on the user's posted content, the number of likes, the number of followers, etc. For example, the collection unit can preferentially collect information from accounts the user follows on social media. The collection unit can also collect information related to posts the user has "liked" or shared on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect related information. This allows for efficient collection of related information by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input a prompt to analyze the user's social media activity to the generation AI and cause the generation AI to collect information.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit can reflect past feedback based on the user's evaluation comments, survey results, usage history, etc. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also exclude information from information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This enables optimal information collection for the user by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input prompts that reflect the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0041] The review unit can refer to past data to evaluate the reliability of the information source during review. For example, the review unit can refer to past data to evaluate the reliability of the information source during review. The review unit can evaluate the reliability of the information source based on past news articles, reliability evaluation data, etc. For example, the review unit can evaluate whether the information source has provided reliable information in the past. The review unit can also evaluate whether the information source has provided false information in the past. Furthermore, the review unit can evaluate what topics the information source has provided in the past. In this way, by referring to past data to evaluate the reliability of the information source, reliable information can be provided. Some or all of the above-mentioned processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input a prompt to evaluate the reliability of the information source to the generation AI and have the generation AI refer to past data.

[0042] The review unit may cross-check multiple sources to evaluate the consistency of the information content during review. For example, the review unit may cross-check multiple sources to evaluate the consistency of the information content during review. The review unit may evaluate the consistency of the information content based on different media, official announcements, third-party reports, etc. For example, the review unit may evaluate whether the content of the information provided by multiple sources is consistent. The review unit may also evaluate whether the timing of the information provided by multiple sources is consistent. Furthermore, the review unit may evaluate whether the details of the information provided by multiple sources are consistent. This allows for cross-checking multiple sources to evaluate the consistency of the information content, thereby providing more reliable information. Some or all of the above-described processing in the review unit may be performed using, or without, AI. For example, the review unit may input a prompt to the generation AI to evaluate the consistency of the information content and cause the generation AI to perform the cross-check.

[0043] The scrutiny unit can take into account attribute information of the sender of the information to evaluate the reliability of the information during scrutiny. For example, the scrutiny unit can take into account attribute information of the sender of the information to evaluate the reliability of the information during scrutiny. The scrutiny unit can evaluate the reliability of the information based on the sender's occupation, past reliability, expertise, etc. For example, the scrutiny unit can evaluate whether the sender of the information is an expert. The scrutiny unit can also evaluate whether the sender of the information has provided reliable information in the past. Furthermore, the scrutiny unit can evaluate whether the sender of the information belongs to a specific organization or group. In this way, by taking into account the attribute information of the sender of the information, reliable information can be provided. Some or all of the above-mentioned processing in the scrutiny unit may be performed using, for example, AI, or may be performed without using AI. For example, the scrutiny unit can input a prompt to evaluate the attribute information of the sender of the information to the generation AI and cause the generation AI to perform the evaluation.

[0044] The reconciliation unit can evaluate the reliability of information taking into account the geographical distribution of the information during reconciliation. For example, the reconciliation unit can evaluate the reliability of information taking into account the geographical distribution of the information during reconciliation. The reconciliation unit can evaluate the reliability of information based on the frequency of occurrence in each region, geographical relevance, etc. For example, if the information is concentrated in a specific region, the reconciliation unit can evaluate the reliability of the region. Furthermore, if the information is distributed over a wide area, the reconciliation unit can evaluate the reliability of the region. Furthermore, if the information is only transmitted from a specific region, the reconciliation unit can evaluate the reliability of the region. In this way, by evaluating the reliability taking into account the geographical distribution of the information, it is possible to provide reliable information related to the region. Some or all of the above-described processing in the reconciliation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconciliation unit can input a prompt to evaluate the geographical distribution of the information to the generation AI and cause the generation AI to perform the evaluation.

[0045] The review unit can improve the reliability of information by referring to related past news articles during review. For example, the review unit can improve the reliability of information by referring to related past news articles during review. The review unit can evaluate the reliability of information based on the reliability, relevance, publication date, etc. of past news articles. For example, the review unit can preferentially trust information that matches past news articles. The review unit can also review information that contradicts past news articles. Furthermore, the review unit can understand the background of the information by referring to past news articles. This makes it possible to improve the reliability of information by referring to related past news articles. Some or all of the above-mentioned processing in the review unit may be performed, for example, using AI or without AI. For example, the review unit can input a prompt to refer to past news articles to the generation AI and cause the generation AI to perform the reference.

[0046] The scrutiny unit can evaluate the reliability of the information by taking into account its market value during the scrutiny. For example, the scrutiny unit can evaluate the reliability of the information by taking into account its market value during the scrutiny. The scrutiny unit can evaluate the reliability of the information based on factors such as economic impact and the balance between supply and demand. For example, the scrutiny unit can evaluate the impact of the information on the market. Furthermore, if the information is related to a specific market, the scrutiny unit can evaluate the reliability of that market. Furthermore, if the information has market value, the scrutiny unit can evaluate its reliability. This makes it possible to provide highly reliable information by taking into account the market value of the information. Some or all of the above-described processing in the scrutiny unit may be performed using, or without, AI. For example, the scrutiny unit can input a prompt to evaluate the market value of the information to the generation AI and cause the generation AI to perform the evaluation.

[0047] The generation unit can adjust the level of detail of the news based on the importance of the information when generating news. For example, the generation unit can adjust the level of detail of the news based on the importance of the information when generating news. The generation unit can adjust the level of detail of the news based on the importance of the information, the user's level of interest, etc. For example, the generation unit can generate detailed news for important information. The generation unit can also generate concise news for less important information. Furthermore, the generation unit can adjust the level of detail of the news according to the importance of the information. As a result, by adjusting the level of detail of the news based on the importance of the information, important information for the user can be provided in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to adjust the level of detail of the news based on the importance of the information, and cause the generation AI to generate news.

[0048] The generation unit can apply different generation algorithms depending on the category of information when generating news. For example, the generation unit can apply different generation algorithms depending on the category of information when generating news. The generation unit can generate news using generation algorithms such as natural language generation and template-based generation. For example, the generation unit can apply a generation algorithm including detailed analysis to political news. The generation unit can apply a visually appealing generation algorithm to entertainment news. The generation unit can also apply a generation algorithm including real-time scores and highlights to sports news. In this way, by applying different generation algorithms depending on the category of information, it is possible to provide news that is optimal for the category. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input a prompt to the generation AI to apply a generation algorithm depending on the category of information, and cause the generation AI to generate news.

[0049] The generation unit can improve the accuracy of news generation by referring to the user's past news browsing history when generating news. For example, the generation unit can improve the accuracy of news generation by referring to the user's past news browsing history when generating news. The generation unit can improve the accuracy of news generation based on the type of article viewed by the user, the viewing time, frequency, etc. For example, the generation unit can analyze trends in news viewed by the user in the past and generate related news. The generation unit can also generate news by referring to news styles that the user has previously rated highly. Furthermore, the generation unit can generate optimal news based on the user's past news browsing history. In this way, by referring to the user's past news browsing history, it is possible to provide news that is highly relevant to the user. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input a prompt that analyzes the user's past news browsing history to the generation AI and cause the generation AI to generate news.

[0050] The generation unit can determine the priority of news based on the time of submission of information when generating news. For example, the generation unit can determine the priority of news based on the time of submission of information when generating news. The generation unit can determine the priority of news based on the latest information, past information, the time period in which the information was submitted, etc. For example, the generation unit can generate news with priority given to the latest information. The generation unit can also lower the priority of older information based on its importance. Furthermore, the generation unit can determine the priority of news by grouping together information that was recently submitted. In this way, by determining the priority of news based on the time of submission of the information, the latest information can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to determine the priority of news based on the time of submission of information, and cause the generation AI to generate news.

[0051] The generation unit can adjust the order of news based on the relevance of the information when generating news. For example, the generation unit can adjust the order of news based on the relevance of the information when generating news. The generation unit can adjust the order of news based on topic matching, user interest, etc. For example, the generation unit can prioritize highly relevant information when generating news. The generation unit can also postpone the order of less relevant information in the news. Furthermore, the generation unit can optimize the order of news based on the relevance of the information. As a result, by adjusting the order of news based on the relevance of the information, it is possible to provide information that is highly relevant to the user preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to adjust the order of news based on the relevance of the information, and cause the generation AI to generate news.

[0052] The generation unit can adjust the use of technical terms in the news according to the user's level of expertise when generating news. For example, the generation unit can adjust the use of technical terms in the news according to the user's level of expertise when generating news. The generation unit can evaluate the user's level of expertise based on the user's occupation, past browsing history, survey results, etc. For example, if the user has technical expertise, the generation unit can generate news that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate news that avoids technical terms. Furthermore, the generation unit can adjust the use of technical terms in the news according to the user's level of expertise. This makes it possible to provide news that is easy for the user to understand by adjusting the use of technical terms in the news according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input a prompt to the generation AI that adjusts the use of technical terms in the news according to the user's level of expertise, and cause the generation AI to generate news.

[0053] The providing unit can select the optimal delivery method by referring to the user's past operation history when providing news. For example, the providing unit can select the optimal delivery method by referring to the user's past operation history when providing news. The providing unit can analyze the operation history based on click history, viewing time, operation frequency, etc. For example, the providing unit can prioritize the use of a delivery method (text, video, etc.) that the user has previously preferred. The providing unit can also select the optimal delivery method for a specific time period based on the user's past operation history. Furthermore, the providing unit can suggest the optimal delivery method based on the user's past operation history. This allows news to be provided in the optimal format for the user by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt that analyzes the user's past operation history to the generation AI, causing the generation AI to select the optimal delivery method.

[0054] The providing unit can customize the news content provided according to the user's current task when providing news. For example, the providing unit can customize the news content provided according to the user's current task when providing news. The providing unit can identify the current task based on calendar information, data from a task management app, etc. For example, the providing unit can provide short, to-the-point news when the user is at work. The providing unit can provide news with detailed information when the user is on a break. Furthermore, the providing unit can provide news in audio format when the user is on the move. This allows the news to be provided in an optimal format for the user by customizing the news content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt identifying the user's current task to the generating AI and cause the generating AI to customize the news content provided.

[0055] The providing unit can improve the news providing method by reflecting user feedback when providing news. For example, the providing unit can improve the news providing method by reflecting user feedback when providing news. The providing unit can improve the news providing method based on user feedback, analysis of usage, and the like. For example, the providing unit can preferentially use a news providing method that the user has given a high rating. The providing unit can also exclude a news providing method that the user has given a low rating. Furthermore, the providing unit can optimize the news providing method based on user feedback. In this way, by improving the news providing method by reflecting user feedback, news can be provided in a format that is optimal for the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input a prompt that reflects user feedback to the generating AI and cause the generating AI to improve the news providing method.

[0056] The provision unit can select the optimal delivery method by taking into account the user's device information when providing news. For example, the provision unit can select the optimal delivery method by taking into account the user's device information when providing news. The provision unit can acquire device information based on the device type, OS, browser information, etc. For example, if the user is using a smartphone, the provision unit can provide a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the provision unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the provision unit can provide a delivery method that is concise and highly visible. This allows news to be provided in the optimal format for the user by taking into account the user's device information. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input a prompt to the generation AI to select a delivery method taking into account the user's device information, and cause the generation AI to select the delivery method.

[0057] The providing unit can provide news in multiple languages ​​according to the user's language setting when providing news. For example, the providing unit can provide news in multiple languages ​​according to the user's language setting when providing news. The providing unit can acquire the language setting based on the browser's language setting, the user's profile information, etc. For example, the providing unit can automatically set the news language based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can provide news in that language. This makes the provided content multilingual according to the user's language setting, thereby providing news in a format that is easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input a prompt to the generation AI to make the provided content multilingual according to the user's language setting, and cause the generation AI to perform multilingualization of the provided content.

[0058] The providing unit can analyze the user's social media activity and provide related news when providing news. For example, the providing unit can analyze the user's social media activity and provide related news when providing news. The providing unit can analyze the user's social media activity based on the user's posted content, the number of likes, the number of followers, etc. For example, the providing unit can provide news about places the user has checked in on social media. The providing unit can also analyze the user's posted content on social media and provide related news. Furthermore, the providing unit can provide related news by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide news that is highly relevant to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input a prompt to analyze the user's social media activity to the generation AI and cause the generation AI to provide news.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The news delivery system can also collect the user's schedule information and adjust the timing of news delivery based on the user's schedule. For example, the collection unit can obtain the user's schedule from a calendar app and provide short, concise news before important meetings or events. It can also provide detailed news articles when the user has free time. Furthermore, it can provide news in audio format when the user is on the move. This allows news to be delivered according to the user's schedule, improving the ease of information delivery.

[0061] The news delivery system can further analyze a user's purchasing history and customize the news content based on the user's interests. For example, the collection unit can acquire a user's online shopping history and provide news related to products and services in which the user is interested. It can also provide news related to products that the user has previously purchased preferentially. Furthermore, if the user's purchasing history indicates an interest in a particular brand or category, it can provide news related to that brand or category. This makes it possible to provide news that is tailored to the user's interests, thereby improving the relevance of information.

[0062] The news delivery system can further analyze the user's reading history and customize the news content based on the user's reading preferences. For example, the collection unit can obtain the reading history from the user's e-reader and provide news related to genres and authors in which the user is interested. It can also provide news related to books the user has read in the past with priority. Furthermore, if the user is interested in a particular topic, it can provide news related to that topic. This makes it possible to provide news according to the user's reading preferences and improve the relevance of information.

[0063] The news delivery system can further analyze the user's travel history and customize the news content based on the user's travel destinations. For example, the collection unit can obtain the user's travel history from a travel booking site and provide news related to places the user has visited. It can also provide news related to travel destinations the user is planning with priority. Furthermore, if the user is interested in a particular region, it can provide news related to that region. This makes it possible to provide news based on the user's travel history, improving the relevance of information.

[0064] The news delivery system can further analyze the activities of the user's social media followers and customize the news content based on the interests of the followers. For example, the collection unit can obtain the posts of the followers from the user's social media accounts and provide news related to topics that the followers are interested in. It can also provide information related to news shared by the followers preferentially. Furthermore, if a follower is participating in a specific event or campaign, it can provide news related to that event or campaign. This makes it possible to provide news based on the activities of the user's social media followers, thereby improving the relevance of the information.

[0065] The news provision system can further analyze the user's learning history and customize the news content based on the user's learning progress. For example, the collection unit can obtain the user's learning history from the user's online learning platform and provide news related to the topics the user is studying. It can also provide news related to content the user has previously learned preferentially. Furthermore, if the user is interested in a specific skill or knowledge, it can provide news related to that skill or knowledge. This makes it possible to provide news based on the user's learning history, thereby improving the relevance of information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects information from social media and websites. The collection department collects information based on specific keywords or topics and obtains data using scraping technology or APIs. For example, the collection department can collect information about a specific incident or disaster. Step 2: The Scrutiny Department scrutinizes the authenticity of the information collected by the Collection Department. The Scrutiny Department scrutinizes the authenticity of the information based on the source of the information and the consistency of its content, and evaluates the reliability of the information based on reliable media and official announcements. For example, the Scrutiny Department evaluates whether the source of the information is a reliable media. Step 3: The generation unit generates news based on the information reviewed by the review unit. The generation unit generates news articles or breaking news summarizing important points based on the reviewed information, and generates the news using natural language generation technology or template-based generation technology. For example, the generation unit extracts important points and generates the news in a format that is easy for users to understand. Step 4: The providing unit provides the news generated by the generating unit to the user. The providing unit provides the generated news to the user in a text format or a multimedia format, and provides the news through a website, a mobile app, email distribution, etc. For example, the providing unit provides the generated news to the user through a website or a mobile app.

[0068] (Example 2) A news delivery system according to an embodiment of the present invention uses a generation AI to collect the latest information from social media and other online platforms, scrutinize the authenticity of the collected information, and provide real-time news to users. The news delivery system collects the latest information from social media and other online platforms, uses a generation AI to scrutinize the authenticity of the collected information, and generates real-time news based on the scrutinized information and provides it to users. For example, the news delivery system collects information based on specific keywords or topics. For example, it can collect information about specific incidents or disasters. The news delivery system then scrutinizes the authenticity of the collected information using a generation AI. The generation AI analyzes the collected information and distinguishes between highly reliable and less reliable information. For example, it can evaluate the reliability of the information by checking the source and consistency of the content. The news delivery system then generates real-time news based on the scrutinized information and provides it to users. The generation AI organizes the scrutinized information and generates news in a format that is easy for users to understand. For example, it can generate news articles or breaking news summarizing key points. This allows the news delivery system to receive reliable news in real time from the latest information overflowing on social media and other platforms. This improves the reliability of information and makes information gathering more efficient for users. For example, users can receive reliable real-time news, improving the accuracy and reliability of information. Also, users can quickly obtain the latest information, improving the efficiency of information gathering.

[0069] A news provision system according to an embodiment includes a collection unit, a review unit, a generation unit, and a provision unit. The collection unit collects information from social media and websites. The collection unit can collect information based on, for example, specific keywords or topics. The collection unit can acquire data using scraping technology or an API. For example, the collection unit can collect information related to specific incidents or disasters. The review unit reviews the authenticity of the information collected by the collection unit. The review unit can review the authenticity of the information based on, for example, the source of the information and the consistency of its content as evaluation criteria. The review unit can evaluate the reliability of the information based on reliable media sources or official announcements. For example, the review unit can evaluate whether the source of the information is reliable media. The generation unit generates news based on the information reviewed by the review unit. For example, the generation unit can generate news articles or breaking news summarizing important points based on the reviewed information. The generation unit can generate news using natural language generation technology or template-based generation technology. For example, the generation unit can extract important points and generate news in a format that is easy for users to understand. The providing unit provides the news generated by the generating unit to the user. The providing unit can provide the generated news to the user in, for example, a text format or a multimedia format. The providing unit can provide the news to the user via a website, a mobile app, email distribution, or other methods. For example, the providing unit can provide the generated news to the user via a website or a mobile app. As a result, the news providing system according to the embodiment can provide the user with reliable real-time news by collecting information from social media and online platforms, examining the authenticity of the information, and generating and providing the news.

[0070] The collection unit can collect information based on specific keywords or topics. For example, the collection unit can collect information based on specific keywords or topics. The collection unit can collect information related to specific incidents or disasters. For example, the collection unit can collect information based on specific keywords such as politics, economics, and sports. The collection unit can efficiently collect information related to specific topics. As a result, highly relevant information can be efficiently collected by collecting information based on specific keywords or topics. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a prompt to collect information based on specific keywords or topics to the generation AI, and cause the generation AI to collect information.

[0071] The scrutiny unit can scrutinize the authenticity of information using the source of the information or the consistency of its content as evaluation criteria. For example, the scrutiny unit can scrutinize the authenticity of information using the source of the information or the consistency of its content as evaluation criteria. The scrutiny unit can evaluate the reliability of information based on reliable media or official announcements. For example, the scrutiny unit can evaluate whether the source of the information is a reliable media. The scrutiny unit can also evaluate the consistency of the content of the information. For example, the scrutiny unit can check whether there are matches or contradictions with multiple information sources. This makes it possible to provide highly reliable information by scrutinizing the authenticity of information using the consistency of the source of the information or the content as evaluation criteria. Some or all of the above-mentioned processing in the scrutiny unit may be performed using, for example, AI, or may be performed without using AI. For example, the scrutiny unit can input a prompt to the generation AI to evaluate the source of the information or the consistency of its content, and cause the generation AI to scrutinize the authenticity of the information.

[0072] The generation unit can generate news articles and breaking news summarizing important points based on the scrutinized information. The generation unit can generate news articles and breaking news summarizing important points based on the scrutinized information, for example. The generation unit can generate news using natural language generation technology or template-based generation technology. For example, the generation unit can extract important points and generate news in a format that is easy for users to understand. The generation unit can also generate news using a generation AI. For example, the generation unit can input scrutinized information to the generation AI and cause the generation AI to generate news articles and breaking news. In this way, by generating news articles and breaking news summarizing important points based on the scrutinized information, it is possible to provide news that is easy for users to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The providing unit can provide the generated news to the user in text format or multimedia format. For example, the providing unit can provide the generated news to the user in text format or multimedia format. The providing unit can provide the news via a website, a mobile app, email distribution, or other methods. For example, the providing unit can provide the generated news to the user through a website or a mobile app. The providing unit can also provide the generated news in text format or multimedia format such as images, videos, or audio. In this way, by providing the generated news in text format or multimedia format, news can be provided in a format that meets the user's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt to the generation AI to provide the generated news to the user, and cause the generation AI to provide the news.

[0074] The providing unit can collect user feedback and feed it back to the scrutiny unit or the generation unit. The providing unit can, for example, collect user feedback and feed it back to the scrutiny unit or the generation unit. The providing unit can collect user feedback by methods such as questionnaires, comments, and ratings. For example, the providing unit can collect ratings and comments that users have given to news and feed them back to the scrutiny unit or the generation unit. In this way, by collecting user feedback and feeding it back to the scrutiny unit or the generation unit, the accuracy of the system can be improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input a prompt to collect user feedback to the generation AI and cause the generation AI to collect feedback.

[0075] The collection unit can estimate a user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit can estimate a user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit can estimate a user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is stressed, the collection unit can reduce the frequency of information collection and collect only important information. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection and cover a wide range of topics. Furthermore, if the user is excited, the collection unit can collect information in real time and provide it immediately. This allows adjusting the timing of information collection based on the user's emotions to reduce the user's stress and collect information at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input a prompt to estimate the user's emotion to the generation AI and cause the generation AI to perform emotion estimation.

[0076] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit can analyze the user's past information collection history and select the optimal collection method. The collection unit can analyze the type, frequency, time period, etc. of information collected in the past. For example, the collection unit can prioritize collecting topics that the user frequently collected in the past. The collection unit can also prioritize collection methods (RSS feeds, social media, etc.) that the user has used in the past. Furthermore, the collection unit can predict the information to be collected in a specific time period from the user's past information collection history and select the optimal collection method. This allows the optimal collection method to be selected and information to be collected efficiently by analyzing the user's past information collection history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input a prompt to analyze the user's past information collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0077] The collection unit may filter information based on the user's current areas of interest or topics when collecting information. For example, the collection unit may filter information based on the user's current areas of interest or topics when collecting information. The collection unit may identify the user's current areas of interest or topics based on the user's search history, browsing history, social media activity, etc. For example, the collection unit may collect only information related to topics in which the user is currently interested. The collection unit may also filter information using related keywords based on the user's areas of interest. Furthermore, the collection unit may collect information only from specific sources based on the user's current areas of interest. This allows highly relevant information to be collected by filtering information based on the user's current areas of interest or topics. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input a prompt identifying the user's current areas of interest or topics to the generation AI and cause the generation AI to filter the information.

[0078] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. The collection unit can collect information using voice recognition technology, text analysis technology, image recognition technology, etc. For example, if the user is using voice input, the collection unit can collect information using voice recognition technology. Also, if the user is using text input, the collection unit can collect information using text analysis technology. Furthermore, if the user is using image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input a prompt to the generation AI to select a collection means depending on the user's input method, and cause the generation AI to select the collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, when the user is stressed, the collection unit can prioritize collecting only important information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting information covering a wide range of topics. Furthermore, when the user is excited, the collection unit can prioritize collecting information updated in real time. Thus, by determining the priority of information to be collected based on the user's emotions, information important to the user can be prioritized and collected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input a prompt to estimate the user's emotion to the generation AI and cause the generation AI to perform emotion estimation.

[0080] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. The collection unit can acquire the user's geographical location information using GPS data, an IP address, a location information service, etc. For example, the collection unit can prioritize collecting news related to the area where the user is currently located. The collection unit can also prioritize collecting information related to places the user has visited in the past. Furthermore, the collection unit can prioritize collecting information related to travel destinations the user is planning. This makes it possible to provide important information related to the area by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input an information collection prompt that takes into account the user's geographical location information to the generation AI, causing the generation AI to collect information.

[0081] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can analyze the social media activity based on the user's posted content, the number of likes, the number of followers, etc. For example, the collection unit can preferentially collect information from accounts the user follows on social media. The collection unit can also collect information related to posts the user has "liked" or shared on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect related information. This allows for efficient collection of related information by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input a prompt to analyze the user's social media activity to the generation AI and cause the generation AI to collect information.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit can reflect past feedback based on the user's evaluation comments, survey results, usage history, etc. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also exclude information from information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This enables optimal information collection for the user by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input prompts that reflect the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0083] The scrutiny unit can estimate the user's emotions and adjust the criteria for scrutinizing the authenticity of information based on the estimated user emotions. For example, the scrutiny unit can estimate the user's emotions and adjust the criteria for scrutinizing the authenticity of information based on the estimated user emotions. The scrutiny unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is stressed, the scrutiny unit can scrutinize the authenticity of information using strict criteria. Furthermore, if the user is relaxed, the scrutiny unit can scrutinize the authenticity of information using flexible criteria. Furthermore, if the user is excited, the scrutiny unit can quickly scrutinize the authenticity of information. This allows for scrutiny according to the user's situation by adjusting the criteria for scrutinizing the authenticity of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the refining unit may be performed using, for example, AI, or may be performed without using AI. For example, the refining unit may input a prompt to estimate the user's emotion to the generation AI, causing the generation AI to perform emotion estimation.

[0084] The review unit can refer to past data to evaluate the reliability of the information source during review. For example, the review unit can refer to past data to evaluate the reliability of the information source during review. The review unit can evaluate the reliability of the information source based on past news articles, reliability evaluation data, etc. For example, the review unit can evaluate whether the information source has provided reliable information in the past. The review unit can also evaluate whether the information source has provided false information in the past. Furthermore, the review unit can evaluate what topics the information source has provided in the past. In this way, by referring to past data to evaluate the reliability of the information source, reliable information can be provided. Some or all of the above-mentioned processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input a prompt to evaluate the reliability of the information source to the generation AI and have the generation AI refer to past data.

[0085] The review unit may cross-check multiple sources to evaluate the consistency of the information content during review. For example, the review unit may cross-check multiple sources to evaluate the consistency of the information content during review. The review unit may evaluate the consistency of the information content based on different media, official announcements, third-party reports, etc. For example, the review unit may evaluate whether the content of the information provided by multiple sources is consistent. The review unit may also evaluate whether the timing of the information provided by multiple sources is consistent. Furthermore, the review unit may evaluate whether the details of the information provided by multiple sources are consistent. This allows for cross-checking multiple sources to evaluate the consistency of the information content, thereby providing more reliable information. Some or all of the above-described processing in the review unit may be performed using, or without, AI. For example, the review unit may input a prompt to the generation AI to evaluate the consistency of the information content and cause the generation AI to perform the cross-check.

[0086] The scrutiny unit can take into account attribute information of the sender of the information to evaluate the reliability of the information during scrutiny. For example, the scrutiny unit can take into account attribute information of the sender of the information to evaluate the reliability of the information during scrutiny. The scrutiny unit can evaluate the reliability of the information based on the sender's occupation, past reliability, expertise, etc. For example, the scrutiny unit can evaluate whether the sender of the information is an expert. The scrutiny unit can also evaluate whether the sender of the information has provided reliable information in the past. Furthermore, the scrutiny unit can evaluate whether the sender of the information belongs to a specific organization or group. In this way, by taking into account the attribute information of the sender of the information, reliable information can be provided. Some or all of the above-mentioned processing in the scrutiny unit may be performed using, for example, AI, or may be performed without using AI. For example, the scrutiny unit can input a prompt to evaluate the attribute information of the sender of the information to the generation AI and cause the generation AI to perform the evaluation.

[0087] The reconciliation unit can estimate the user's emotions and adjust the display method of the reconciliation results based on the estimated user emotions. For example, the reconciliation unit can estimate the user's emotions and adjust the display method of the reconciliation results based on the estimated user emotions. The reconciliation unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the reconciliation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the reconciliation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reconciliation unit can provide a display method that focuses on the main points. By adjusting the display method of the reconciliation results based on the user's emotions, information can be provided in a format that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reconciliation unit can be performed, for example, using AI, or without AI. For example, the scrutiny unit can input a prompt to the generation AI to estimate the user's emotions, causing the generation AI to perform emotion estimation.

[0088] The reconciliation unit can evaluate the reliability of information taking into account the geographical distribution of the information during reconciliation. For example, the reconciliation unit can evaluate the reliability of information taking into account the geographical distribution of the information during reconciliation. The reconciliation unit can evaluate the reliability of information based on the frequency of occurrence in each region, geographical relevance, etc. For example, if the information is concentrated in a specific region, the reconciliation unit can evaluate the reliability of the region. Furthermore, if the information is distributed over a wide area, the reconciliation unit can evaluate the reliability of the region. Furthermore, if the information is only transmitted from a specific region, the reconciliation unit can evaluate the reliability of the region. In this way, by evaluating the reliability taking into account the geographical distribution of the information, it is possible to provide reliable information related to the region. Some or all of the above-described processing in the reconciliation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconciliation unit can input a prompt to evaluate the geographical distribution of the information to the generation AI and cause the generation AI to perform the evaluation.

[0089] The review unit can improve the reliability of information by referring to related past news articles during review. For example, the review unit can improve the reliability of information by referring to related past news articles during review. The review unit can evaluate the reliability of information based on the reliability, relevance, publication date, etc. of past news articles. For example, the review unit can preferentially trust information that matches past news articles. The review unit can also review information that contradicts past news articles. Furthermore, the review unit can understand the background of the information by referring to past news articles. This makes it possible to improve the reliability of information by referring to related past news articles. Some or all of the above-mentioned processing in the review unit may be performed, for example, using AI or without AI. For example, the review unit can input a prompt to refer to past news articles to the generation AI and cause the generation AI to perform the reference.

[0090] The scrutiny unit can evaluate the reliability of the information by taking into account its market value during the scrutiny. For example, the scrutiny unit can evaluate the reliability of the information by taking into account its market value during the scrutiny. The scrutiny unit can evaluate the reliability of the information based on factors such as economic impact and the balance between supply and demand. For example, the scrutiny unit can evaluate the impact of the information on the market. Furthermore, if the information is related to a specific market, the scrutiny unit can evaluate the reliability of that market. Furthermore, if the information has market value, the scrutiny unit can evaluate its reliability. This makes it possible to provide highly reliable information by taking into account the market value of the information. Some or all of the above-described processing in the scrutiny unit may be performed using, or without, AI. For example, the scrutiny unit can input a prompt to evaluate the market value of the information to the generation AI and cause the generation AI to perform the evaluation.

[0091] The generation unit can estimate the user's emotions and adjust the way the news is presented based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the way the news is presented based on the estimated user emotions. The generation unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the generation unit can generate simple, highly readable news. Furthermore, if the user is relaxed, the generation unit can generate news that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can generate news that focuses on the main points. By adjusting the way the news is presented based on the user's emotions, news can be provided in an optimal format for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input a prompt to the generation AI to estimate the user's emotion, and cause the generation AI to perform emotion estimation.

[0092] The generation unit can adjust the level of detail of the news based on the importance of the information when generating news. For example, the generation unit can adjust the level of detail of the news based on the importance of the information when generating news. The generation unit can adjust the level of detail of the news based on the importance of the information, the user's level of interest, etc. For example, the generation unit can generate detailed news for important information. The generation unit can also generate concise news for less important information. Furthermore, the generation unit can adjust the level of detail of the news according to the importance of the information. As a result, by adjusting the level of detail of the news based on the importance of the information, important information for the user can be provided in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to adjust the level of detail of the news based on the importance of the information, and cause the generation AI to generate news.

[0093] The generation unit can apply different generation algorithms depending on the category of information when generating news. For example, the generation unit can apply different generation algorithms depending on the category of information when generating news. The generation unit can generate news using generation algorithms such as natural language generation and template-based generation. For example, the generation unit can apply a generation algorithm including detailed analysis to political news. The generation unit can apply a visually appealing generation algorithm to entertainment news. The generation unit can also apply a generation algorithm including real-time scores and highlights to sports news. In this way, by applying different generation algorithms depending on the category of information, it is possible to provide news that is optimal for the category. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input a prompt to the generation AI to apply a generation algorithm depending on the category of information, and cause the generation AI to generate news.

[0094] The generation unit can improve the accuracy of news generation by referring to the user's past news browsing history when generating news. For example, the generation unit can improve the accuracy of news generation by referring to the user's past news browsing history when generating news. The generation unit can improve the accuracy of news generation based on the type of article viewed by the user, the viewing time, frequency, etc. For example, the generation unit can analyze trends in news viewed by the user in the past and generate related news. The generation unit can also generate news by referring to news styles that the user has previously rated highly. Furthermore, the generation unit can generate optimal news based on the user's past news browsing history. In this way, by referring to the user's past news browsing history, it is possible to provide news that is highly relevant to the user. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input a prompt that analyzes the user's past news browsing history to the generation AI and cause the generation AI to generate news.

[0095] The generation unit can estimate the user's emotions and adjust the length of the news based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the news based on the estimated user emotions. The generation unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, the generation unit can generate short, to-the-point news when the user is in a hurry. The generation unit can generate longer news with detailed explanations when the user is relaxed. Furthermore, the generation unit can generate news with visually stimulating effects when the user is excited. This allows the length of the news to be adjusted based on the user's emotions, thereby providing news of an optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input a prompt to the generation AI to estimate the user's emotion, and cause the generation AI to perform emotion estimation.

[0096] The generation unit can determine the priority of news based on the time of submission of information when generating news. For example, the generation unit can determine the priority of news based on the time of submission of information when generating news. The generation unit can determine the priority of news based on the latest information, past information, the time period in which the information was submitted, etc. For example, the generation unit can generate news with priority given to the latest information. The generation unit can also lower the priority of older information based on its importance. Furthermore, the generation unit can determine the priority of news by grouping together information that was recently submitted. In this way, by determining the priority of news based on the time of submission of the information, the latest information can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to determine the priority of news based on the time of submission of information, and cause the generation AI to generate news.

[0097] The generation unit can adjust the order of news based on the relevance of the information when generating news. For example, the generation unit can adjust the order of news based on the relevance of the information when generating news. The generation unit can adjust the order of news based on topic matching, user interest, etc. For example, the generation unit can prioritize highly relevant information when generating news. The generation unit can also postpone the order of less relevant information in the news. Furthermore, the generation unit can optimize the order of news based on the relevance of the information. As a result, by adjusting the order of news based on the relevance of the information, it is possible to provide information that is highly relevant to the user preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a prompt to the generation AI to adjust the order of news based on the relevance of the information, and cause the generation AI to generate news.

[0098] The generation unit can adjust the use of technical terms in the news according to the user's level of expertise when generating news. For example, the generation unit can adjust the use of technical terms in the news according to the user's level of expertise when generating news. The generation unit can evaluate the user's level of expertise based on the user's occupation, past browsing history, survey results, etc. For example, if the user has technical expertise, the generation unit can generate news that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate news that avoids technical terms. Furthermore, the generation unit can adjust the use of technical terms in the news according to the user's level of expertise. This makes it possible to provide news that is easy for the user to understand by adjusting the use of technical terms in the news according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input a prompt to the generation AI that adjusts the use of technical terms in the news according to the user's level of expertise, and cause the generation AI to generate news.

[0099] The providing unit can estimate the user's emotions and adjust the news provision method based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the news provision method based on the estimated user emotions. The providing unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the providing unit can provide news in a simple, highly visible format. Furthermore, if the user is relaxed, the providing unit can provide news in a format including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide news in a format that focuses on the main points. In this way, by adjusting the news provision method based on the user's emotions, it is possible to provide news in an optimal format for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input a prompt to the generation AI to estimate the user's emotion, and cause the generation AI to perform emotion estimation.

[0100] The providing unit can select the optimal delivery method by referring to the user's past operation history when providing news. For example, the providing unit can select the optimal delivery method by referring to the user's past operation history when providing news. The providing unit can analyze the operation history based on click history, viewing time, operation frequency, etc. For example, the providing unit can prioritize the use of a delivery method (text, video, etc.) that the user has previously preferred. The providing unit can also select the optimal delivery method for a specific time period based on the user's past operation history. Furthermore, the providing unit can suggest the optimal delivery method based on the user's past operation history. This allows news to be provided in the optimal format for the user by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt that analyzes the user's past operation history to the generation AI, causing the generation AI to select the optimal delivery method.

[0101] The providing unit can customize the news content provided according to the user's current task when providing news. For example, the providing unit can customize the news content provided according to the user's current task when providing news. The providing unit can identify the current task based on calendar information, data from a task management app, etc. For example, the providing unit can provide short, to-the-point news when the user is at work. The providing unit can provide news with detailed information when the user is on a break. Furthermore, the providing unit can provide news in audio format when the user is on the move. This allows the news to be provided in an optimal format for the user by customizing the news content according to the user's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a prompt identifying the user's current task to the generating AI and cause the generating AI to customize the news content provided.

[0102] The providing unit can improve the news providing method by reflecting user feedback when providing news. For example, the providing unit can improve the news providing method by reflecting user feedback when providing news. The providing unit can improve the news providing method based on user feedback, analysis of usage, and the like. For example, the providing unit can preferentially use a news providing method that the user has given a high rating. The providing unit can also exclude a news providing method that the user has given a low rating. Furthermore, the providing unit can optimize the news providing method based on user feedback. In this way, by improving the news providing method by reflecting user feedback, news can be provided in a format that is optimal for the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input a prompt that reflects user feedback to the generating AI and cause the generating AI to improve the news providing method.

[0103] The providing unit can estimate the user's emotions and adjust the news delivery procedure based on the estimated user emotions. The providing unit can, for example, estimate the user's emotions and adjust the news delivery procedure based on the estimated user emotions. The providing unit can estimate the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the providing unit can provide news in a simple, highly visible procedure. Furthermore, if the user is relaxed, the providing unit can provide news in a procedure including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide news in a procedure that focuses on the main points. In this way, by adjusting the news delivery procedure based on the user's emotions, it is possible to provide news in a procedure that is optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input a prompt to the generation AI to estimate the user's emotion, and cause the generation AI to perform emotion estimation.

[0104] The provision unit can select the optimal delivery method by taking into account the user's device information when providing news. For example, the provision unit can select the optimal delivery method by taking into account the user's device information when providing news. The provision unit can acquire device information based on the device type, OS, browser information, etc. For example, if the user is using a smartphone, the provision unit can provide a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the provision unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the provision unit can provide a delivery method that is concise and highly visible. This allows news to be provided in the optimal format for the user by taking into account the user's device information. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input a prompt to the generation AI to select a delivery method taking into account the user's device information, and cause the generation AI to select the delivery method.

[0105] The providing unit can provide news in multiple languages ​​according to the user's language setting when providing news. For example, the providing unit can provide news in multiple languages ​​according to the user's language setting when providing news. The providing unit can acquire the language setting based on the browser's language setting, the user's profile information, etc. For example, the providing unit can automatically set the news language based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can provide news in that language. This makes the provided content multilingual according to the user's language setting, thereby providing news in a format that is easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input a prompt to the generation AI to make the provided content multilingual according to the user's language setting, and cause the generation AI to perform multilingualization of the provided content.

[0106] The providing unit can analyze the user's social media activity and provide related news when providing news. For example, the providing unit can analyze the user's social media activity and provide related news when providing news. The providing unit can analyze the user's social media activity based on the user's posted content, the number of likes, the number of followers, etc. For example, the providing unit can provide news about places the user has checked in on social media. The providing unit can also analyze the user's posted content on social media and provide related news. Furthermore, the providing unit can provide related news by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide news that is highly relevant to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input a prompt to analyze the user's social media activity to the generation AI and cause the generation AI to provide news. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, the scrutiny unit, the generation unit, and the provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and collects information from social media and online platforms. The scrutiny unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and scrutinizes the authenticity of the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates news based on the scrutinized information. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated news to a user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, the scrutiny unit, the generation unit, and the provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and collects information from social networking sites and online platforms. The scrutiny unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and scrutinizes the authenticity of the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates news based on the scrutinized information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated news to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, the scrutiny unit, the generation unit, and the provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and collects information from social media and online platforms. The scrutiny unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and scrutinizes the authenticity of the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates news based on the scrutinized information. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides the generated news to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, the scrutiny unit, the generation unit, and the provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and collects information from social media and online platforms. The scrutiny unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and scrutinizes the authenticity of the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates news based on the scrutinized information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated news to the user.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The news delivery system can also collect user health data and adjust the way news is delivered based on the user's health condition. For example, the collection unit can obtain heart rate and sleep data from the user's wearable device and deliver short, concise news if the user is tired. Alternatively, detailed news articles can be delivered if the user is relaxed. Furthermore, positive news can be prioritized if the user is stressed. This allows news to be delivered according to the user's health condition, reducing stress and improving the user's ease of information delivery.

[0109] The news delivery system can also collect the user's schedule information and adjust the timing of news delivery based on the user's schedule. For example, the collection unit can obtain the user's schedule from a calendar app and provide short, concise news before important meetings or events. It can also provide detailed news articles when the user has free time. Furthermore, it can provide news in audio format when the user is on the move. This allows news to be delivered according to the user's schedule, improving the ease of information delivery.

[0110] The news delivery system can further analyze a user's purchasing history and customize the news content based on the user's interests. For example, the collection unit can acquire a user's online shopping history and provide news related to products and services in which the user is interested. It can also provide news related to products that the user has previously purchased preferentially. Furthermore, if the user's purchasing history indicates an interest in a particular brand or category, it can provide news related to that brand or category. This makes it possible to provide news that is tailored to the user's interests, thereby improving the relevance of information.

[0111] The news delivery system can further analyze the user's music playback history and tailor the news delivery method based on the user's music preferences. For example, the collection unit can obtain the playback history from the user's music streaming service and provide detailed news articles if the user listens to relaxing music. Alternatively, the collection unit can provide short, concise news articles if the user listens to energetic music. Furthermore, if the user is interested in a particular artist or genre, news related to that artist or genre can be provided. This allows news to be delivered according to the user's music preferences, improving the user's ease of information intake.

[0112] The news delivery system can further collect the user's exercise data and adjust the news delivery method based on the user's exercise status. For example, the collection unit can obtain exercise data from the user's fitness tracker and provide news in audio format when the user is exercising. Furthermore, when the user is relaxing after exercising, detailed news articles can be provided. Furthermore, when the user is interested in a particular exercise, news related to that exercise can be provided. This allows news to be delivered according to the user's exercise status, improving the ease of information intake.

[0113] The news delivery system can further analyze the user's reading history and customize the news content based on the user's reading preferences. For example, the collection unit can obtain the reading history from the user's e-reader and provide news related to genres and authors in which the user is interested. It can also provide news related to books the user has read in the past with priority. Furthermore, if the user is interested in a particular topic, it can provide news related to that topic. This makes it possible to provide news according to the user's reading preferences and improve the relevance of information.

[0114] The news delivery system can further analyze the user's travel history and customize the news content based on the user's travel destinations. For example, the collection unit can obtain the user's travel history from a travel booking site and provide news related to places the user has visited. It can also provide news related to travel destinations the user is planning with priority. Furthermore, if the user is interested in a particular region, it can provide news related to that region. This makes it possible to provide news based on the user's travel history, improving the relevance of information.

[0115] The news delivery system can further analyze the activities of the user's social media followers and customize the news content based on the interests of the followers. For example, the collection unit can obtain the posts of the followers from the user's social media accounts and provide news related to topics that the followers are interested in. It can also provide information related to news shared by the followers preferentially. Furthermore, if a follower is participating in a specific event or campaign, it can provide news related to that event or campaign. This makes it possible to provide news based on the activities of the user's social media followers, thereby improving the relevance of the information.

[0116] The news delivery system can further estimate the user's emotions and adjust the tone of the news based on the estimated user emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition or voice analysis, and if the user is sad, it can provide positive news preferentially. If the user is excited, it can provide news in a calm tone. Furthermore, if the user is relaxed, it can provide detailed news articles. This makes it possible to adjust the tone of the news according to the user's emotions, thereby improving the ease with which the information is received.

[0117] The news provision system can further analyze the user's learning history and customize the news content based on the user's learning progress. For example, the collection unit can obtain the user's learning history from the user's online learning platform and provide news related to the topics the user is studying. It can also provide news related to content the user has previously learned preferentially. Furthermore, if the user is interested in a specific skill or knowledge, it can provide news related to that skill or knowledge. This makes it possible to provide news based on the user's learning history, thereby improving the relevance of information.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection department collects information from social media and websites. The collection department collects information based on specific keywords or topics and obtains data using scraping technology or APIs. For example, the collection department can collect information about a specific incident or disaster. Step 2: The Scrutiny Department scrutinizes the authenticity of the information collected by the Collection Department. The Scrutiny Department scrutinizes the authenticity of the information based on the source of the information and the consistency of its content, and evaluates the reliability of the information based on reliable media and official announcements. For example, the Scrutiny Department evaluates whether the source of the information is a reliable media. Step 3: The generation unit generates news based on the information reviewed by the review unit. The generation unit generates news articles or breaking news summarizing important points based on the reviewed information, and generates the news using natural language generation technology or template-based generation technology. For example, the generation unit extracts important points and generates the news in a format that is easy for users to understand. Step 4: The providing unit provides the news generated by the generating unit to the user. The providing unit provides the generated news to the user in a text format or a multimedia format, and provides the news through a website, a mobile app, email distribution, etc. For example, the providing unit provides the generated news to the user through a website or a mobile app.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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 AI 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 collection department that collects information from social media and websites; an inspection unit that inspects the authenticity of the information collected by the collection unit; a generating unit that generates news based on the information reviewed by the review unit; a providing unit that provides the news generated by the generating unit to a user; Equipped with A system characterized by:

2. The collecting unit Gather information based on specific keywords or topics 2. The system of claim 1.

3. The inspection unit Scrutinize the authenticity of information based on its source or consistency of content as evaluation criteria 2. The system of claim 1.

4. The generation unit Generate news articles and breaking news summaries based on vetted information 2. The system of claim 1.

5. The providing unit Providing generated news to users in text and multimedia formats 2. The system of claim 1.

6. The providing unit Collect user feedback and feed it back to the reviewers and generators 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past information collection history and select the optimal collection method 2. The system of claim 1.

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