System

Abstract

The system according to the embodiment aims to efficiently provide news valuable to a user and to find a reliable information source.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a selection unit, a provision unit, an agreement acquisition unit, and an evaluation unit. The collection unit collects a user's past browsing history or search history. The analyzer analyzes the data collected by the collector to determine a user's interest or concern. The selection unit selects a news article based on the interest or the concern determined by the analysis unit. The providing unit provides the news article selected by the selecting unit. The consent obtaining unit obtains the consent of the user. The evaluation unit evaluates reliability of the news article.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to efficiently find news that is valuable to them from a large amount of information, and it is also difficult to find reliable information sources.

[0005] The system according to the embodiment aims to efficiently provide valuable news to users and to help them find reliable sources of information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a selection unit, a provision unit, a consent acquisition unit, and an evaluation unit. The collection unit collects a user's past browsing history or search history. The analysis unit analyzes the data collected by the collection unit and determines the user's interests or concerns. The selection unit selects news articles based on the interests or concerns determined by the analysis unit. The provision unit provides the news articles selected by the selection unit. The consent acquisition unit obtains the user's consent. The evaluation unit evaluates the reliability of the news articles. [Effects of the Invention]

[0007] The system according to the embodiment efficiently provides valuable news to users and enables them to find reliable sources of information. [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 curation system according to an embodiment of the present invention collects a user's past browsing history and search history, analyzes them using AI, and provides personalized news articles. The news curation system collects past browsing history and search history to understand the user's interests and concerns, analyzes them using AI, and selects and provides the most appropriate news articles to the user. This mechanism allows users to easily select content that is valuable to them from the vast amount of information disseminated daily. Even users who are not good at effectively searching to find accurate and reliable sources can easily obtain reliable news articles. For example, the news curation system collects a user's past browsing history and search history. For example, it can identify the categories of news articles the user has previously viewed and trends in search keywords. Next, the news curation system uses AI to analyze the collected data. The AI ​​determines the user's interests and concerns based on the collected data and selects the most appropriate news articles for the user. For example, if the user has viewed many sports-related news articles in the past, the AI ​​can prioritize sports-related news articles. The news curation system then provides the selected news articles in a personalized format tailored to the user's preferences. For example, if a user prefers sports-related news articles, sports-related news articles will be displayed preferentially. Also, if a user trusts a particular news source, news articles from that news source will be displayed preferentially. This allows the news curation system to quickly grasp important news during busy morning hours. Furthermore, even users who are not good at effectively searching to find accurate and reliable sources can easily obtain reliable news articles. For example, AI can automatically select and provide reliable news sources to users, allowing them to browse the news with peace of mind. This allows the news curation system to provide personalized news articles based on the user's past browsing and search history.For example, users can quickly find important news during busy morning hours, and even users who are not good at effectively searching to find accurate and reliable information sources can easily obtain reliable news articles.

[0029] A news curation system according to an embodiment includes a collection unit, an analysis unit, a selection unit, a provision unit, a consent acquisition unit, and an evaluation unit. The collection unit collects a user's past browsing history or search history. The user's past browsing history or search history may include, but is not limited to, website URLs, search keywords, and browsing time. The collection unit may, for example, identify the categories of news articles the user has previously viewed and trends in keywords searched. The analysis unit analyzes the data collected by the collection unit to determine the user's interests or concerns. The analysis may be performed using, for example, a machine learning algorithm or a statistical analysis method, but is not limited to, examples. For example, if the user has viewed many sports-related news articles in the past, the analysis unit may preferentially select sports-related news articles. The selection unit selects news articles based on the interests or concerns determined by the analysis unit. The selection may be performed based on, for example, the category or reliability of the news articles, but is not limited to, examples. For example, if the user prefers sports-related news articles, the selection unit may preferentially select sports-related news articles. The providing unit provides the news articles selected by the selecting unit. The providing unit may provide the news articles by, for example, email delivery, app notification, website display, or the like, but is not limited to these examples. For example, if a user trusts a particular news source, the providing unit may preferentially display news articles from that news source. The consent acquisition unit obtains consent from the user during the data collection and analysis process. The consent may be obtained by, for example, a pop-up notification, email confirmation, a checkbox, or the like, but is not limited to these examples. For example, the consent acquisition unit may display a pop-up notification to confirm whether the user agrees to the data collection and analysis. The evaluation unit evaluates the reliability of the news articles. The evaluation may be performed by, for example, a method of source verification, cross-checking, evaluation score, or the like, but is not limited to these examples. For example, the evaluation unit may check the source of the news articles and preferentially display news articles from highly reliable news sources.As a result, the news curation system according to the embodiment can provide personalized news articles based on the user's past browsing history and search history. For example, the user can quickly grasp important news during busy morning hours. Furthermore, even users who are not good at effectively searching to find accurate and reliable information sources can easily obtain reliable news articles.

[0030] The collection unit can analyze the user's past browsing history and search history and select a collection method. For example, the collection unit prioritizes collection of categories that the user has frequently browsed in the past. The collection unit can also collect related data based on search keywords used by the user in the past. The collection unit can also analyze the user's past browsing patterns and select the most efficient collection method. This enables efficient data collection by analyzing past history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past browsing history and search history into the generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter the browsing history and search history based on the user's current areas of interest when collecting the browsing history and search history. For example, the collection unit prioritizes collecting data related to categories in which the user is currently interested. The collection unit can also filter related data based on keywords recently searched by the user. The collection unit can also grasp the user's current areas of interest in real time and filter the collected data. This makes it possible to collect highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest to the generation AI and have the generation AI perform the filtering.

[0032] When collecting browsing history or search history, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uses image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting a collection means according to the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting browsing history and search history, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting news articles related to the user's current location. The collection unit can also prioritize collecting local news based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize collecting news related to the user's travel destination. In this way, highly relevant data can be collected by taking the geographical location information into consideration. 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 the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0034] The collection unit can analyze the user's social media activities and collect related data when collecting browsing history and search history. The collection unit can collect related data based on, for example, news articles shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related news articles. The collection unit can also collect related news articles by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0035] When collecting browsing history or search history, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting categories of news articles that the user has previously rated highly. The collection unit can also collect news articles excluding categories of news articles that the user has previously rated poorly. The collection unit can also customize the optimal collection method based on the user's past feedback. In this way, the optimal collection method can be customized by reflecting past feedback. 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 the user's past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related data. The analysis unit can also apply an economics-specific analysis algorithm to economics-related data. The analysis unit can also apply an entertainment-specific analysis algorithm to entertainment-related data. This enables highly accurate analysis by applying an analysis algorithm according to the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0042] The selection unit can adjust the level of detail of the selection based on the importance of the news article during selection. For example, the selection unit performs a detailed selection for important news articles. The selection unit can also perform a concise selection for news articles with low importance. The selection unit can also dynamically adjust the level of detail of the selection according to the importance of the news article. This enables efficient selection by adjusting the level of detail of the selection according to the importance of the news article. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the importance of the news article to the generation AI and cause the generation AI to adjust the level of detail of the selection.

[0043] When making a selection, the selection unit can apply different selection algorithms depending on the category of the news article. For example, the selection unit can apply a sports-specific selection algorithm to sports-related news articles. The selection unit can also apply an economics-specific selection algorithm to economics-related news articles. The selection unit can also apply an entertainment-specific selection algorithm to entertainment-related news articles. This enables highly accurate selection by applying a selection algorithm according to the category of the news article. Some or all of the above-mentioned processing by the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input the category of the news article to the generation AI and cause the generation AI to apply the selection algorithm.

[0044] When making a selection, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. The selection unit, for example, optimizes the selection algorithm based on the user's past selection results. The selection unit can also improve the accuracy of the selection by referring to the user's past selection results. The selection unit can also analyze the user's past selection results and identify areas for improvement in the selection. In this way, the accuracy of the selection is improved by referring to the past selection results. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past selection results into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0045] The selection unit can determine the selection priority based on the time when the news articles were collected during selection. For example, the selection unit preferentially selects the most recent news articles. The selection unit can also postpone the selection of older news articles. The selection unit can also dynamically adjust the selection priority according to the time when the news articles were collected. This enables efficient selection by determining the selection priority based on the time when the news articles were collected. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the time when the news articles were collected into the generation AI and have the generation AI determine the selection priority.

[0046] The selection unit can adjust the selection order based on the relevance of the news articles during selection. For example, the selection unit prioritizes the selection of highly relevant news articles. The selection unit can also postpone the selection of less relevant news articles. The selection unit can also dynamically adjust the selection order according to the relevance of the news articles. This enables efficient selection by adjusting the selection order based on the relevance of the news articles. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the relevance of the news articles to the generation AI and cause the generation AI to adjust the selection order.

[0047] During selection, the selection unit can adjust the use of selected terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the selection unit selects news articles that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the selection unit can also select news articles that avoid technical terminology. Furthermore, the selection unit can dynamically adjust the use of selected terminology according to the user's level of expertise. This allows for more appropriate news articles to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

[0048] The providing unit can adjust the level of detail of the provision based on the importance of the news article when providing the news. For example, the providing unit provides detailed information for important news articles. The providing unit can also provide concise information for news articles with low importance. The providing unit can also dynamically adjust the level of detail of the provision according to the importance of the news article. This enables efficient provision by adjusting the level of detail of the provision according to the importance of the news article. Some or all of the above-mentioned 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 the importance of the news article to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0049] The providing unit can apply different providing algorithms depending on the category of the news article when providing the news. For example, the providing unit applies a sports-specific providing algorithm to sports-related news articles. The providing unit can also apply an economics-specific providing algorithm to economics-related news articles. The providing unit can also apply an entertainment-specific providing algorithm to entertainment-related news articles. This enables highly accurate provision by applying a providing algorithm according to the category of the news article. Some or all of the above-mentioned 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 the category of the news article to the generation AI and cause the generation AI to apply the providing algorithm.

[0050] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit optimizes the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of the provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and identify areas for improvement in the provision. In this way, the accuracy of the provision is improved by referring to the past provision results. 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 the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0051] The providing unit can determine the priority of provision based on the time when the news articles were collected at the time of provision. For example, the providing unit provides the latest news articles with priority. The providing unit can also provide older news articles later. The providing unit can also dynamically adjust the priority of provision according to the time when the news articles were collected. This enables efficient provision by determining the priority of provision based on the time when the news articles were collected. Some or all of the above-mentioned 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 the time when the news articles were collected into the generation AI and cause the generation AI to determine the priority of provision.

[0052] The providing unit can adjust the order of provision based on the relevance of the news articles when providing them. For example, the providing unit can provide highly relevant news articles preferentially. The providing unit can also provide less relevant news articles later. The providing unit can also dynamically adjust the order of provision according to the relevance of the news articles. This enables efficient provision by adjusting the order of provision based on the relevance of the news articles. Some or all of the above-mentioned 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 the relevance of the news articles to the generation AI and cause the generation AI to adjust the order of provision.

[0053] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide news articles that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide news articles that avoid technical terminology. Furthermore, the providing unit can dynamically adjust the use of technical terminology provided according to the user's level of expertise. This allows more appropriate news articles to be provided by adjusting the use of technical terminology according to the user's level of expertise. 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 the user's level of expertise to the generating AI and cause the generating AI to use technical terminology.

[0054] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously consented. The consent acquisition unit can also select the optimal consent acquisition method based on the user's past consent history. The consent acquisition unit can also analyze the user's past consent history and identify areas for improvement in consent acquisition. In this way, the optimal consent acquisition method can be selected by referring to the past consent history. Some or all of the above-mentioned processing in the consent acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent acquisition unit can input the user's past consent history into a generation AI and cause the generation AI to select a consent acquisition method.

[0055] The consent acquisition unit can customize the content of the consent acquisition based on the user's current areas of interest when acquiring consent. The consent acquisition unit, for example, provides consent acquisition content related to categories in which the user is currently interested. The consent acquisition unit can also customize the content of the consent acquisition based on the user's current areas of interest. The consent acquisition unit can also grasp the user's current areas of interest in real time and customize the content of the consent acquisition. This makes it possible to acquire more appropriate consent by customizing the content of the consent acquisition based on the user's current areas of interest. Some or all of the above-described processing in the consent acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the consent acquisition unit can input the user's current areas of interest to the generation AI and cause the generation AI to customize the content of the consent acquisition.

[0056] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by taking into account the user's geographical location information. For example, the consent acquisition unit provides a consent acquisition method related to the user's current location. The consent acquisition unit can also select the optimal consent acquisition method based on the user's geographical location information. If the user is traveling, the consent acquisition unit can also provide a consent acquisition method related to the user's travel destination. This makes it possible to select the optimal consent acquisition method by taking into account the geographical location information. Some or all of the above-described processing in the consent acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the consent acquisition unit can input the user's geographical location information into the generation AI and cause the generation AI to select the consent acquisition method.

[0057] When obtaining consent, the consent acquisition unit can analyze the user's social media activity and suggest a relevant consent acquisition method. For example, the consent acquisition unit provides a consent acquisition method related to content shared by the user on social media. The consent acquisition unit can also analyze the content posted by the user on social media and suggest a relevant consent acquisition method. The consent acquisition unit can also suggest a relevant consent acquisition method by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest a highly relevant consent acquisition method. Some or all of the above-described processing in the consent acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent acquisition unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of a consent acquisition method.

[0058] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the news article during evaluation. For example, the evaluation unit provides a detailed evaluation for an important news article. The evaluation unit can also provide a brief evaluation for a news article with low importance. The evaluation unit can also dynamically adjust the level of detail of the evaluation according to the importance of the news article. This enables efficient evaluation by adjusting the level of detail of the evaluation according to the importance of the news article. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the importance of the news article to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0059] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the news article. For example, the evaluation unit can apply a sports-specific evaluation algorithm to a sports-related news article. The evaluation unit can also apply an economics-specific evaluation algorithm to an economics-related news article. The evaluation unit can also apply an entertainment-specific evaluation algorithm to an entertainment-related news article. This enables highly accurate evaluation by applying an evaluation algorithm according to the category of the news article. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the category of the news article into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0060] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when performing the evaluation. The evaluation unit, for example, optimizes the evaluation algorithm based on the user's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past evaluation results. The evaluation unit can also analyze the user's past evaluation results and identify areas for improvement in the evaluation. In this way, the accuracy of the evaluation is improved by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation results into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0061] During evaluation, the evaluation unit can determine the priority of evaluation based on the time when the news articles were collected. For example, the evaluation unit prioritizes evaluation of the most recent news articles. The evaluation unit can also postpone evaluation of older news articles. The evaluation unit can also dynamically adjust the priority of evaluation according to the time when the news articles were collected. This enables efficient evaluation by determining the priority of evaluation based on the time when the news articles were collected. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the time when the news articles were collected into the generation AI and have the generation AI determine the priority of evaluation.

[0062] The evaluation unit can adjust the order of evaluation based on the relevance of the news articles during evaluation. For example, the evaluation unit prioritizes evaluation of highly relevant news articles. The evaluation unit can also postpone evaluation of less relevant news articles. The evaluation unit can also dynamically adjust the order of evaluation according to the relevance of the news articles. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the news articles. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the relevance of the news articles to the generation AI and cause the generation AI to adjust the order of evaluation.

[0063] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can perform an evaluation that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the evaluation unit can also perform an evaluation that avoids technical terminology. The evaluation unit can also dynamically adjust the use of technical terminology in the evaluation according to the user's level of expertise. This allows for more appropriate evaluation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI, for example. For example, the evaluation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The news curation system can also collect users' purchasing history, which the analysis unit analyzes and reflects in the selection of news articles. For example, news articles related to products the user has previously purchased can be provided preferentially. Also, if a user is interested in a particular brand, news articles related to that brand can be selected. Furthermore, it is possible to estimate seasonal interests from the user's purchasing history and provide news articles appropriate for the season. This makes it possible to provide personalized news based on the user's purchasing behavior.

[0066] The news curation system can also collect users' "likes" and comments on social media, and the analysis unit can analyze these and reflect them in the selection of news articles. For example, news articles related to posts that the user has "liked" can be provided preferentially. It can also identify topics of interest based on the content of the user's comments and select news articles related to those topics. It can also analyze the time periods when the user is active on social media and provide news articles tailored to those times. This makes it possible to provide personalized news based on the user's social media activity.

[0067] The news curation system can also collect the user's calendar data, which the analysis unit analyzes and reflects in the selection of news articles. For example, relevant news articles can be provided based on the user's schedule. Also, if the user plans to attend a specific event, news articles related to that event can be preferentially selected. Furthermore, based on the user's calendar, it is possible to provide news articles that cover the main points during busy times and more detailed news articles during times when the user has less time. This makes it possible to provide personalized news based on the user's schedule.

[0068] The news curation system can also collect a user's reading history, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can provide news articles related to the genre of books the user has read in the past. Also, if a user is interested in a particular author, it can prioritize the selection of news articles related to that author. It can also identify topics of interest from the user's reading history and provide news articles related to those topics. This makes it possible to provide personalized news based on the user's reading history.

[0069] The news curation system can also collect the user's travel history, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can provide news articles related to places the user has visited in the past. Also, if the user is interested in a particular region, it can prioritize news articles related to that region. It can also estimate seasonal interests from the user's travel history and provide news articles appropriate for the season. This makes it possible to provide personalized news based on the user's travel history.

[0070] The news curation system can also collect a user's gameplay history, which the analysis unit analyzes and reflects in the selection of news articles. For example, news articles related to the genre of games played by the user can be provided. Also, if a user is interested in a particular game, news articles related to that game can be preferentially selected. Furthermore, it is possible to identify topics of interest from the user's gameplay history and provide news articles related to those topics. This allows for personalized news provision based on the user's gameplay history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects the user's past browsing or search history, including website URLs, search keywords, and browsing time. For example, it can identify the categories of news articles the user has viewed in the past and trends in search keywords. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's interests. The analysis is performed using machine learning algorithms and statistical analysis methods. For example, if the user has viewed many sports-related news articles in the past, sports-related news articles can be prioritized. Step 3: The selection unit selects news articles based on the interests or concerns determined by the analysis unit. The selection is based on the category and reliability of the news articles. For example, if the user prefers sports-related news articles, sports-related news articles can be preferentially selected. Step 4: The provider provides the news articles selected by the selector. The news articles are provided via email, app notifications, website display, etc. For example, if a user trusts a particular news source, news articles from that news source can be displayed preferentially. Step 5: The consent acquisition unit obtains the user's consent during the data collection and analysis process. Consent can be obtained through methods such as a pop-up notification, email confirmation, or a checkbox. For example, a pop-up notification can be displayed to confirm whether the user agrees to the data collection and analysis. Step 6: The evaluation unit evaluates the reliability of the news article. The evaluation is performed by methods such as checking the source of the news, cross-checking, and scoring. For example, the source of the news article can be checked, and news articles from reliable news sources can be displayed preferentially.

[0073] (Example 2) A news curation system according to an embodiment of the present invention collects a user's past browsing history and search history, analyzes them using AI, and provides personalized news articles. The news curation system collects past browsing history and search history to understand the user's interests and concerns, analyzes them using AI, and selects and provides the most appropriate news articles to the user. This mechanism allows users to easily select content that is valuable to them from the vast amount of information disseminated daily. Even users who are not good at effectively searching to find accurate and reliable sources can easily obtain reliable news articles. For example, the news curation system collects a user's past browsing history and search history. For example, it can identify the categories of news articles the user has previously viewed and trends in search keywords. Next, the news curation system uses AI to analyze the collected data. The AI ​​determines the user's interests and concerns based on the collected data and selects the most appropriate news articles for the user. For example, if the user has viewed many sports-related news articles in the past, the AI ​​can prioritize sports-related news articles. The news curation system then provides the selected news articles in a personalized format tailored to the user's preferences. For example, if a user prefers sports-related news articles, sports-related news articles will be displayed preferentially. Also, if a user trusts a particular news source, news articles from that news source will be displayed preferentially. This allows the news curation system to quickly grasp important news during busy morning hours. Furthermore, even users who are not good at effectively searching to find accurate and reliable sources can easily obtain reliable news articles. For example, AI can automatically select and provide reliable news sources to users, allowing them to browse the news with peace of mind. This allows the news curation system to provide personalized news articles based on the user's past browsing and search history.For example, users can quickly find important news during busy morning hours, and even users who are not good at effectively searching to find accurate and reliable information sources can easily obtain reliable news articles.

[0074] A news curation system according to an embodiment includes a collection unit, an analysis unit, a selection unit, a provision unit, a consent acquisition unit, and an evaluation unit. The collection unit collects a user's past browsing history or search history. The user's past browsing history or search history may include, but is not limited to, website URLs, search keywords, and browsing time. The collection unit may, for example, identify the categories of news articles the user has previously viewed and trends in keywords searched. The analysis unit analyzes the data collected by the collection unit to determine the user's interests or concerns. The analysis may be performed using, for example, a machine learning algorithm or a statistical analysis method, but is not limited to, examples. For example, if the user has viewed many sports-related news articles in the past, the analysis unit may preferentially select sports-related news articles. The selection unit selects news articles based on the interests or concerns determined by the analysis unit. The selection may be performed based on, for example, the category or reliability of the news articles, but is not limited to, examples. For example, if the user prefers sports-related news articles, the selection unit may preferentially select sports-related news articles. The providing unit provides the news articles selected by the selecting unit. The providing unit may provide the news articles by, for example, email delivery, app notification, website display, or the like, but is not limited to these examples. For example, if a user trusts a particular news source, the providing unit may preferentially display news articles from that news source. The consent acquisition unit obtains consent from the user during the data collection and analysis process. The consent may be obtained by, for example, a pop-up notification, email confirmation, a checkbox, or the like, but is not limited to these examples. For example, the consent acquisition unit may display a pop-up notification to confirm whether the user agrees to the data collection and analysis. The evaluation unit evaluates the reliability of the news articles. The evaluation may be performed by, for example, a method of source verification, cross-checking, evaluation score, or the like, but is not limited to these examples. For example, the evaluation unit may check the source of the news articles and preferentially display news articles from highly reliable news sources.As a result, the news curation system according to the embodiment can provide personalized news articles based on the user's past browsing history and search history. For example, the user can quickly grasp important news during busy morning hours. Furthermore, even users who are not good at effectively searching to find accurate and reliable information sources can easily obtain reliable news articles.

[0075] The collection unit can estimate the user's emotions and adjust the timing of collecting browsing history and search history based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to collect data in real time. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to collect necessary data in a short time. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions. 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. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The collection unit can analyze the user's past browsing history and search history and select a collection method. For example, the collection unit prioritizes collection of categories that the user has frequently browsed in the past. The collection unit can also collect related data based on search keywords used by the user in the past. The collection unit can also analyze the user's past browsing patterns and select the most efficient collection method. This enables efficient data collection by analyzing past history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past browsing history and search history into the generation AI and have the generation AI select the optimal collection method.

[0077] The collection unit can filter the browsing history and search history based on the user's current areas of interest when collecting the browsing history and search history. For example, the collection unit prioritizes collecting data related to categories in which the user is currently interested. The collection unit can also filter related data based on keywords recently searched by the user. The collection unit can also grasp the user's current areas of interest in real time and filter the collected data. This makes it possible to collect highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest to the generation AI and have the generation AI perform the filtering.

[0078] When collecting browsing history or search history, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uses image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting a collection means according to the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data with relaxing content. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data with interesting content. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting data with important content. This enables more appropriate data collection by prioritizing data according to the user's emotions. 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 can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priority.

[0080] When collecting browsing history and search history, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting news articles related to the user's current location. The collection unit can also prioritize collecting local news based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize collecting news related to the user's travel destination. In this way, highly relevant data can be collected by taking the geographical location information into consideration. 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 the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0081] The collection unit can analyze the user's social media activities and collect related data when collecting browsing history and search history. The collection unit can collect related data based on, for example, news articles shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related news articles. The collection unit can also collect related news articles by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0082] When collecting browsing history or search history, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting categories of news articles that the user has previously rated highly. The collection unit can also collect news articles excluding categories of news articles that the user has previously rated poorly. The collection unit can also customize the optimal collection method based on the user's past feedback. In this way, the optimal collection method can be customized by reflecting past feedback. 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 the user's past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to 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 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 analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related data. The analysis unit can also apply an economics-specific analysis algorithm to economics-related data. The analysis unit can also apply an entertainment-specific analysis algorithm to entertainment-related data. This enables highly accurate analysis by applying an analysis algorithm according to the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a short analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0091] The selection unit can estimate the user's emotions and adjust the selection criteria for news articles based on the estimated user emotions. For example, when the user is relaxed, the selection unit can prioritize selecting interesting news articles. Furthermore, when the user is in a hurry, the selection unit can prioritize selecting important news articles. Furthermore, when the user is excited, the selection unit can prioritize selecting visually stimulating news articles. By adjusting the selection criteria according to the user's emotions, more appropriate news articles can be selected. 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 selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the selection criteria for news articles.

[0092] The selection unit can adjust the level of detail of the selection based on the importance of the news article during selection. For example, the selection unit performs a detailed selection for important news articles. The selection unit can also perform a concise selection for news articles with low importance. The selection unit can also dynamically adjust the level of detail of the selection according to the importance of the news article. This enables efficient selection by adjusting the level of detail of the selection according to the importance of the news article. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the importance of the news article to the generation AI and cause the generation AI to adjust the level of detail of the selection.

[0093] When making a selection, the selection unit can apply different selection algorithms depending on the category of the news article. For example, the selection unit can apply a sports-specific selection algorithm to sports-related news articles. The selection unit can also apply an economics-specific selection algorithm to economics-related news articles. The selection unit can also apply an entertainment-specific selection algorithm to entertainment-related news articles. This enables highly accurate selection by applying a selection algorithm according to the category of the news article. Some or all of the above-mentioned processing by the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input the category of the news article to the generation AI and cause the generation AI to apply the selection algorithm.

[0094] When making a selection, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. The selection unit, for example, optimizes the selection algorithm based on the user's past selection results. The selection unit can also improve the accuracy of the selection by referring to the user's past selection results. The selection unit can also analyze the user's past selection results and identify areas for improvement in the selection. In this way, the accuracy of the selection is improved by referring to the past selection results. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past selection results into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0095] The selection unit can estimate the user's emotions and determine the priority of news articles to be selected based on the estimated user emotions. For example, when the user is relaxed, the selection unit can prioritize interesting news articles. Furthermore, when the user is in a hurry, the selection unit can prioritize important news articles. Furthermore, when the user is excited, the selection unit can prioritize visually stimulating news articles. This allows for more appropriate news articles to be provided by prioritizing news articles according to the user's emotions. 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 selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the news articles.

[0096] The selection unit can determine the selection priority based on the time when the news articles were collected during selection. For example, the selection unit preferentially selects the most recent news articles. The selection unit can also postpone the selection of older news articles. The selection unit can also dynamically adjust the selection priority according to the time when the news articles were collected. This enables efficient selection by determining the selection priority based on the time when the news articles were collected. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the time when the news articles were collected into the generation AI and have the generation AI determine the selection priority.

[0097] The selection unit can adjust the selection order based on the relevance of the news articles during selection. For example, the selection unit prioritizes the selection of highly relevant news articles. The selection unit can also postpone the selection of less relevant news articles. The selection unit can also dynamically adjust the selection order according to the relevance of the news articles. This enables efficient selection by adjusting the selection order based on the relevance of the news articles. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the relevance of the news articles to the generation AI and cause the generation AI to adjust the selection order.

[0098] During selection, the selection unit can adjust the use of selected terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the selection unit selects news articles that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the selection unit can also select news articles that avoid technical terminology. Furthermore, the selection unit can dynamically adjust the use of selected terminology according to the user's level of expertise. This allows for more appropriate news articles to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

[0099] The providing unit can estimate the user's emotions and adjust the news article presentation method based on the estimated user emotions. For example, when the user is relaxed, the providing unit can provide a detailed news article. When the user is in a hurry, the providing unit can also provide a news article that focuses on the main points. When the user is excited, the providing unit can also provide a visually stimulating news article. This allows for adjusting the presentation method according to the user's emotions to provide more appropriate news articles. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the news article presentation method.

[0100] The providing unit can adjust the level of detail of the provision based on the importance of the news article when providing the news. For example, the providing unit provides detailed information for important news articles. The providing unit can also provide concise information for news articles with low importance. The providing unit can also dynamically adjust the level of detail of the provision according to the importance of the news article. This enables efficient provision by adjusting the level of detail of the provision according to the importance of the news article. Some or all of the above-mentioned 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 the importance of the news article to the generating AI and cause the generating AI to adjust the level of detail of the provision.

[0101] The providing unit can apply different providing algorithms depending on the category of the news article when providing the news. For example, the providing unit applies a sports-specific providing algorithm to sports-related news articles. The providing unit can also apply an economics-specific providing algorithm to economics-related news articles. The providing unit can also apply an entertainment-specific providing algorithm to entertainment-related news articles. This enables highly accurate provision by applying a providing algorithm according to the category of the news article. Some or all of the above-mentioned 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 the category of the news article to the generation AI and cause the generation AI to apply the providing algorithm.

[0102] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit optimizes the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of the provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and identify areas for improvement in the provision. In this way, the accuracy of the provision is improved by referring to the past provision results. 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 the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0103] The providing unit can estimate the user's emotions and determine the priority of news articles to be provided based on the estimated user emotions. For example, when the user is relaxed, the providing unit can prioritize providing interesting news articles. Furthermore, when the user is in a hurry, the providing unit can prioritize providing important news articles. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating news articles. This allows more appropriate news articles to be provided by determining the priority of news articles according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of news articles.

[0104] The providing unit can determine the priority of provision based on the time when the news articles were collected at the time of provision. For example, the providing unit provides the latest news articles with priority. The providing unit can also provide older news articles later. The providing unit can also dynamically adjust the priority of provision according to the time when the news articles were collected. This enables efficient provision by determining the priority of provision based on the time when the news articles were collected. Some or all of the above-mentioned 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 the time when the news articles were collected into the generation AI and cause the generation AI to determine the priority of provision.

[0105] The providing unit can adjust the order of provision based on the relevance of the news articles when providing them. For example, the providing unit can provide highly relevant news articles preferentially. The providing unit can also provide less relevant news articles later. The providing unit can also dynamically adjust the order of provision according to the relevance of the news articles. This enables efficient provision by adjusting the order of provision based on the relevance of the news articles. Some or all of the above-mentioned 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 the relevance of the news articles to the generation AI and cause the generation AI to adjust the order of provision.

[0106] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide news articles that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide news articles that avoid technical terminology. Furthermore, the providing unit can dynamically adjust the use of technical terminology provided according to the user's level of expertise. This allows more appropriate news articles to be provided by adjusting the use of technical terminology according to the user's level of expertise. 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 the user's level of expertise to the generating AI and cause the generating AI to use technical terminology.

[0107] The consent acquisition unit can estimate the user's emotions and adjust the timing of consent acquisition based on the estimated user's emotions. For example, the consent acquisition unit can advance the timing of consent acquisition when the user is relaxed. The consent acquisition unit can also delay the timing of consent acquisition when the user is stressed. The consent acquisition unit can also optimize the timing of consent acquisition when the user is in a hurry. This allows consent to be acquired at a more appropriate time by adjusting the timing of consent acquisition according to 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 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 consent acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consent acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of consent acquisition.

[0108] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously consented. The consent acquisition unit can also select the optimal consent acquisition method based on the user's past consent history. The consent acquisition unit can also analyze the user's past consent history and identify areas for improvement in consent acquisition. In this way, the optimal consent acquisition method can be selected by referring to the past consent history. Some or all of the above-mentioned processing in the consent acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent acquisition unit can input the user's past consent history into a generation AI and cause the generation AI to select a consent acquisition method.

[0109] The consent acquisition unit can customize the content of the consent acquisition based on the user's current areas of interest when acquiring consent. The consent acquisition unit, for example, provides consent acquisition content related to categories in which the user is currently interested. The consent acquisition unit can also customize the content of the consent acquisition based on the user's current areas of interest. The consent acquisition unit can also grasp the user's current areas of interest in real time and customize the content of the consent acquisition. This makes it possible to acquire more appropriate consent by customizing the content of the consent acquisition based on the user's current areas of interest. Some or all of the above-described processing in the consent acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the consent acquisition unit can input the user's current areas of interest to the generation AI and cause the generation AI to customize the content of the consent acquisition.

[0110] The consent acquisition unit can estimate the user's emotions and determine the priority of consent acquisition based on the estimated user's emotions. For example, when the user is relaxed, the consent acquisition unit can prioritize important consent acquisition. Furthermore, when the user is stressed, the consent acquisition unit can postpone less important consent acquisition. Furthermore, when the user is in a hurry, the consent acquisition unit can prioritize important consent acquisition. This allows more appropriate consent to be acquired by determining the priority of consent acquisition according to the user's emotions. 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 consent acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the consent acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of consent acquisition.

[0111] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by taking into account the user's geographical location information. For example, the consent acquisition unit provides a consent acquisition method related to the user's current location. The consent acquisition unit can also select the optimal consent acquisition method based on the user's geographical location information. If the user is traveling, the consent acquisition unit can also provide a consent acquisition method related to the user's travel destination. This makes it possible to select the optimal consent acquisition method by taking into account the geographical location information. Some or all of the above-described processing in the consent acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the consent acquisition unit can input the user's geographical location information into the generation AI and cause the generation AI to select the consent acquisition method.

[0112] When obtaining consent, the consent acquisition unit can analyze the user's social media activity and suggest a relevant consent acquisition method. For example, the consent acquisition unit provides a consent acquisition method related to content shared by the user on social media. The consent acquisition unit can also analyze the content posted by the user on social media and suggest a relevant consent acquisition method. The consent acquisition unit can also suggest a relevant consent acquisition method by referring to the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest a highly relevant consent acquisition method. Some or all of the above-described processing in the consent acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent acquisition unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of a consent acquisition method.

[0113] The evaluation unit can estimate the user's emotions and adjust the credibility evaluation criteria of the news article based on the estimated user emotions. For example, the evaluation unit can perform a detailed credibility evaluation when the user is relaxed. The evaluation unit can also perform a concise credibility evaluation when the user is in a hurry. The evaluation unit can also perform a visually stimulating credibility evaluation when the user is excited. This enables more appropriate credibility evaluation by adjusting the credibility evaluation criteria according to the user's emotions. 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-mentioned processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the credibility evaluation criteria.

[0114] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the news article during evaluation. For example, the evaluation unit provides a detailed evaluation for an important news article. The evaluation unit can also provide a brief evaluation for a news article with low importance. The evaluation unit can also dynamically adjust the level of detail of the evaluation according to the importance of the news article. This enables efficient evaluation by adjusting the level of detail of the evaluation according to the importance of the news article. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the importance of the news article to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0115] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the news article. For example, the evaluation unit can apply a sports-specific evaluation algorithm to a sports-related news article. The evaluation unit can also apply an economics-specific evaluation algorithm to an economics-related news article. The evaluation unit can also apply an entertainment-specific evaluation algorithm to an entertainment-related news article. This enables highly accurate evaluation by applying an evaluation algorithm according to the category of the news article. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the category of the news article into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0116] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when performing the evaluation. The evaluation unit, for example, optimizes the evaluation algorithm based on the user's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past evaluation results. The evaluation unit can also analyze the user's past evaluation results and identify areas for improvement in the evaluation. In this way, the accuracy of the evaluation is improved by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation results into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0117] The evaluation unit can estimate the user's emotions and determine the priority of the credibility evaluation of news articles based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can prioritize detailed credibility evaluation. Furthermore, if the user is in a hurry, the evaluation unit can prioritize brief credibility evaluation. Furthermore, if the user is excited, the evaluation unit can prioritize visually stimulating credibility evaluation. This enables more appropriate credibility evaluation by determining the priority of the credibility evaluation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the credibility evaluation.

[0118] During evaluation, the evaluation unit can determine the priority of evaluation based on the time when the news articles were collected. For example, the evaluation unit prioritizes evaluation of the most recent news articles. The evaluation unit can also postpone evaluation of older news articles. The evaluation unit can also dynamically adjust the priority of evaluation according to the time when the news articles were collected. This enables efficient evaluation by determining the priority of evaluation based on the time when the news articles were collected. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the time when the news articles were collected into the generation AI and have the generation AI determine the priority of evaluation.

[0119] The evaluation unit can adjust the order of evaluation based on the relevance of the news articles during evaluation. For example, the evaluation unit prioritizes evaluation of highly relevant news articles. The evaluation unit can also postpone evaluation of less relevant news articles. The evaluation unit can also dynamically adjust the order of evaluation according to the relevance of the news articles. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the news articles. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the relevance of the news articles to the generation AI and cause the generation AI to adjust the order of evaluation.

[0120] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can perform an evaluation that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the evaluation unit can also perform an evaluation that avoids technical terminology. The evaluation unit can also dynamically adjust the use of technical terminology in the evaluation according to the user's level of expertise. This allows for more appropriate evaluation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI, for example. For example, the evaluation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, selection unit, provision unit, consent acquisition unit, and evaluation 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 can collect the user's past browsing history and search history via the control unit 46A of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's interests and concerns. The selection unit selects news articles based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit provides the news articles selected by the control unit 46A of the smart device 14 to the user. The consent acquisition unit acquires consent for data collection and analysis via the control unit 46A of the smart device 14. The evaluation unit evaluates the reliability of the news articles via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, selection unit, provision unit, consent acquisition unit, and evaluation 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 can collect the user's past browsing history and search history via the control unit 46A of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's interests and concerns. The selection unit selects news articles based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit provides the news articles selected by the control unit 46A of the smart glasses 214 to the user. The consent acquisition unit acquires consent for data collection and analysis via the control unit 46A of the smart glasses 214. The evaluation unit evaluates the reliability of the news articles via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, selection unit, provision unit, consent acquisition unit, and evaluation 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 can collect the user's past browsing history and search history via the control unit 46A of the headset type terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's interests and concerns. The selection unit selects news articles based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit provides the news articles selected by the control unit 46A of the headset type terminal 314 to the user. The consent acquisition unit acquires consent for data collection and analysis via the control unit 46A of the headset type terminal 314. The evaluation unit evaluates the reliability of the news articles via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, selection unit, provision unit, consent acquisition unit, and evaluation 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 can collect the user's past browsing history and search history via the control unit 46A of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's interests and concerns. The selection unit selects news articles based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit provides the news articles selected by the control unit 46A of the robot 414 to the user. The consent acquisition unit acquires consent for data collection and analysis via the control unit 46A of the robot 414. The evaluation unit evaluates the reliability of the news articles via the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The news curation system can also collect user health data, which the analysis unit analyzes and reflects in the selection of news articles. For example, the system can collect a user's heart rate and sleep data, and if the user's stress level is high, prioritize news articles that will help them relax. Also, if the user is interested in health, health-related news articles can be prioritized. Furthermore, the system can analyze the user's exercise data and provide news articles that will help them relax after exercising. This makes it possible to provide personalized news based on the user's health condition.

[0123] The news curation system can also collect users' purchasing history, which the analysis unit analyzes and reflects in the selection of news articles. For example, news articles related to products the user has previously purchased can be provided preferentially. Also, if a user is interested in a particular brand, news articles related to that brand can be selected. Furthermore, it is possible to estimate seasonal interests from the user's purchasing history and provide news articles appropriate for the season. This makes it possible to provide personalized news based on the user's purchasing behavior.

[0124] The news curation system can also collect users' "likes" and comments on social media, and the analysis unit can analyze these and reflect them in the selection of news articles. For example, news articles related to posts that the user has "liked" can be provided preferentially. It can also identify topics of interest based on the content of the user's comments and select news articles related to those topics. It can also analyze the time periods when the user is active on social media and provide news articles tailored to those times. This makes it possible to provide personalized news based on the user's social media activity.

[0125] The news curation system can also collect user voice data, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can analyze what the user says to the voice assistant and provide news articles related to that content. It can also infer the user's emotions from the user's tone of voice and provide detailed news articles if the user is relaxed, or news articles that focus on the main points if the user is in a hurry. Furthermore, if the user frequently uses specific keywords, it can prioritize news articles related to those keywords. This makes it possible to provide personalized news based on the user's voice data.

[0126] The news curation system can also collect the user's calendar data, which the analysis unit analyzes and reflects in the selection of news articles. For example, relevant news articles can be provided based on the user's schedule. Also, if the user plans to attend a specific event, news articles related to that event can be preferentially selected. Furthermore, based on the user's calendar, it is possible to provide news articles that cover the main points during busy times and more detailed news articles during times when the user has less time. This makes it possible to provide personalized news based on the user's schedule.

[0127] The news curation system can also collect the user's music playback history, which the analysis unit analyzes and reflects in the selection of news articles. For example, if the user is listening to relaxing music, relaxing news articles can be provided. On the other hand, if the user is listening to energetic music, stimulating news articles can be provided. Furthermore, if the user is interested in a particular artist or genre, news articles related to that artist or genre can be preferentially selected. This makes it possible to provide personalized news based on the user's music preferences.

[0128] The news curation system can also collect a user's reading history, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can provide news articles related to the genre of books the user has read in the past. Also, if a user is interested in a particular author, it can prioritize the selection of news articles related to that author. It can also identify topics of interest from the user's reading history and provide news articles related to those topics. This makes it possible to provide personalized news based on the user's reading history.

[0129] The news curation system can also collect exercise data from users, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can provide news articles that help users relax after exercising. If a user is interested in a particular sport, it can prioritize news articles related to that sport. Furthermore, if it is inferred from the user's exercise data that they are interested in health, it can also provide health-related news articles. This makes it possible to provide personalized news based on the user's exercise data.

[0130] The news curation system can also collect the user's travel history, which the analysis unit analyzes and reflects in the selection of news articles. For example, it can provide news articles related to places the user has visited in the past. Also, if the user is interested in a particular region, it can prioritize news articles related to that region. It can also estimate seasonal interests from the user's travel history and provide news articles appropriate for the season. This makes it possible to provide personalized news based on the user's travel history.

[0131] The news curation system can also collect a user's gameplay history, which the analysis unit analyzes and reflects in the selection of news articles. For example, news articles related to the genre of games played by the user can be provided. Also, if a user is interested in a particular game, news articles related to that game can be preferentially selected. Furthermore, it is possible to identify topics of interest from the user's gameplay history and provide news articles related to those topics. This allows for personalized news provision based on the user's gameplay history.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects the user's past browsing or search history, including website URLs, search keywords, and browsing time. For example, it can identify the categories of news articles the user has viewed in the past and trends in search keywords. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's interests. The analysis is performed using machine learning algorithms and statistical analysis methods. For example, if the user has viewed many sports-related news articles in the past, sports-related news articles can be prioritized. Step 3: The selection unit selects news articles based on the interests or concerns determined by the analysis unit. The selection is based on the category and reliability of the news articles. For example, if the user prefers sports-related news articles, sports-related news articles can be preferentially selected. Step 4: The provider provides the news articles selected by the selector. The news articles are provided via email, app notifications, website display, etc. For example, if a user trusts a particular news source, news articles from that news source can be displayed preferentially. Step 5: The consent acquisition unit obtains the user's consent during the data collection and analysis process. Consent can be obtained through methods such as a pop-up notification, email confirmation, or a checkbox. For example, a pop-up notification can be displayed to confirm whether the user agrees to the data collection and analysis. Step 6: The evaluation unit evaluates the reliability of the news article. The evaluation is performed by methods such as checking the source of the news, cross-checking, and scoring. For example, the source of the news article can be checked, and news articles from reliable news sources can be displayed preferentially.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 unit that collects a user's past browsing history or search history; an analysis unit that analyzes the data collected by the collection unit and determines the user's interests or concerns; a selection unit that selects news articles based on the interests or concerns determined by the analysis unit; a providing unit that provides the news articles selected by the selecting unit; a consent acquisition unit that acquires consent from a user; Equipped with an evaluation section that evaluates the reliability of news articles A system characterized by:

2. The collecting unit Estimates user emotions and adjusts the timing of collecting browsing and search histories based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past browsing and search history and select collection methods 2. The system of claim 1.

4. The collecting unit When collecting browsing and search history, filter it based on the user's current interests.

2. The system of claim 1.

5. The collecting unit When collecting browsing history or search history, the collection method is selected according to the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting browsing and search history, prioritize collection of relevant data based on the user's geographic location.

2. The system of claim 1.

8. The collecting unit Analyze your social media activity and collect related data when collecting your browsing and search history 2. The system of claim 1.

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