system

Abstract

The system according to the embodiment aims to analyze patterns of past articles and content and predict reader preferences. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a suggestion unit. The collection unit collects past articles or content. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts reader preferences based on the analysis results obtained by the analysis unit. The suggestion unit suggests article creation, advertising development, and posting timing based on the results predicted by the prediction unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not effectively analyze patterns of past articles and content to predict reader preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze patterns of past articles and content and predict reader preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a suggestion unit. The collection unit collects past articles or content. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts reader preferences based on the analysis results obtained by the analysis unit. The suggestion unit suggests article creation, advertising development, and posting timing based on the results predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze patterns of past articles and content and predict reader preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a generative AI to analyze patterns in past articles and content and predict reader preferences. This system collects past articles and content, analyzes them using a generative AI, and predicts reader preferences. Based on the prediction results, the system then proposes ways to create articles tailored to the reader, develop advertising strategies, and provide appropriate posting timing. For example, the system collects data from previously posted articles, such as the titles, text, posting dates, and number of reader comments and likes. The generative AI then analyzes the collected data to extract patterns in articles and content. The generative AI then predicts reader preferences based on the past data. Finally, based on the prediction results, the system creates articles on specific topics and posts them at appropriate times. Furthermore, by providing advertisements tailored to reader preferences, advertising effectiveness can be improved. This enables the system to propose ways to create articles tailored to the reader, develop advertising strategies, and provide appropriate posting timing. For example, a news site can maintain reader interest by providing articles of interest in a timely manner. In addition, improving advertising effectiveness can also be expected to increase advertising revenue.

[0029] An information processing system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a suggestion unit. The collection unit collects past articles or content. The collection unit can collect data such as blog articles, news articles, and social media posts. The collection unit collects data using, for example, web scraping technology. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using the API of a news site. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, text mining technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can extract frequently occurring keywords from the collected article data. The prediction unit predicts reader preferences based on the analysis results obtained by the analysis unit. The prediction unit predicts preferences using, for example, a machine learning algorithm. The prediction unit can also predict preferences using a statistical model. For example, the prediction unit can predict article themes preferred by readers based on past data. The suggestion unit makes suggestions for article creation, advertising deployment, posting timing, etc. based on the results predicted by the prediction unit. The suggestion unit, for example, uses a generation AI to suggest article themes. The suggestion unit can also suggest advertisements tailored to the reader's preferences. For example, the suggestion unit suggests creating an article on a specific theme and posting it at an appropriate time. As a result, the information processing system according to the embodiment analyzes patterns of past articles and content and predicts reader preferences, thereby enabling suggestions for article creation, advertising deployment, appropriate posting timing, etc. that are tailored to the reader.

[0030] The collection unit can collect data such as the article title, text, posting date and time, reader comments, or number of likes. The collection unit collects data such as the article title, text, posting date and time, reader comments, and number of likes. For example, the collection unit can collect data such as the article title, text, posting date and time, reader comments, and number of likes for articles posted in the past on a news site. The collection unit can also collect data on social media posts. For example, the collection unit can acquire posting data using a social media API. By collecting detailed data, patterns of articles and content can be more accurately identified. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data acquired using a news site's API into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and extract patterns in articles and content. For example, the analysis unit can analyze the collected data and extract patterns in articles and content. For example, the analysis unit can analyze the data using text mining technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can extract frequently occurring keywords from the collected article data. The analysis unit can also analyze the data using topic modeling technology. For example, the analysis unit can extract article topics using LDA (Latent Dirichlet Allocation). By analyzing the data, it is possible to extract patterns in articles and content and provide a basis for predicting reader preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The prediction unit can predict reader preferences based on past data. The prediction unit can predict reader preferences based on past data, for example. The prediction unit can predict preferences using a machine learning algorithm, for example. The prediction unit can also predict preferences using a statistical model. For example, the prediction unit can predict themes of articles that readers will like based on past data. The prediction unit can also predict the popularity of articles on specific themes based on past data. This makes it possible to create articles and develop advertisements that are tailored to the reader by predicting reader preferences based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input past data into the generation AI and have the generation AI perform preference prediction.

[0033] The suggestion unit can make suggestions on article creation, advertising deployment, posting timing, etc. based on the prediction results. The suggestion unit can make suggestions on article creation, advertising deployment, posting timing, etc. based on the prediction results, for example. The suggestion unit can suggest article themes using a generation AI, for example. The suggestion unit can also suggest advertisements tailored to reader preferences. For example, the suggestion unit can suggest creating an article on a specific theme and posting it at an appropriate time. The suggestion unit can also deploy advertisements tailored to reader preferences. For example, the suggestion unit can suggest targeted advertisements based on reader preferences. As a result, making suggestions based on the prediction results can improve reader satisfaction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input the prediction results to the generation AI and cause the generation AI to generate proposals.

[0034] The suggestion unit can display advertisements tailored to the reader's preferences. For example, the suggestion unit displays advertisements tailored to the reader's preferences. For example, the suggestion unit can display targeted advertisements. The suggestion unit can also display personalized advertisements. For example, the suggestion unit can display specific advertisements based on the reader's preferences. This can enhance advertising effectiveness by displaying advertisements tailored to the reader's preferences. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input reader preference data into the generation AI and have the generation AI select an advertisement.

[0035] The collection unit can filter articles based on specific keywords or topics when collecting them. For example, the collection unit can filter articles based on specific keywords or topics when collecting them. For example, the collection unit can perform filtering using keyword matching technology. The collection unit can also perform filtering using topic modeling technology. For example, the collection unit can collect related articles based on keywords such as "technology" or "AI." The collection unit can also collect related articles based on topics such as "environmental protection" or "sustainability." The collection unit can also collect related articles based on keywords such as "economy" or "market trends." This allows highly relevant articles to be collected by filtering based on specific keywords or topics. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the collected article data into a generation AI and have the generation AI perform filtering.

[0036] When collecting articles, the collection unit can refer to the user's past browsing history to preferentially collect highly relevant articles. For example, when collecting articles, the collection unit can refer to the user's past browsing history to preferentially collect highly relevant articles. For example, the collection unit can refer to the user's past browsing history using cookie information. The collection unit can also refer to the user's past browsing history using browser history. For example, the collection unit preferentially collects articles in a category that the user has frequently viewed in the past. The collection unit can also preferentially collect articles with content similar to articles that the user has previously given high ratings to. Furthermore, the collection unit can preferentially collect articles with content related to articles on which the user has previously left comments. In this way, by referring to the user's past browsing history, highly relevant articles can be preferentially collected. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past browsing history data into the generation AI and cause the generation AI to collect highly relevant articles.

[0037] The collection unit can integrate information from different data sources to enrich the collected data when collecting articles. For example, when collecting articles, the collection unit can integrate information from different data sources to enrich the collected data. The collection unit can collect articles from multiple data sources, such as news sites, blogs, and social media. The collection unit can also integrate information from academic papers and professional journals to enrich the collected data. For example, the collection unit can acquire article data using a news site's API and simultaneously acquire related post data using a social media API. Furthermore, the collection unit can collect the posts of influencers followed by the user. This allows the collected data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data acquired from different data sources into a generation AI and have the generation AI integrate the data.

[0038] When collecting articles, the collection unit can prioritize collecting highly relevant articles by taking into account the user's geographical location information. For example, when collecting articles, the collection unit prioritizes collecting highly relevant articles by taking into account the user's geographical location information. The collection unit can, for example, acquire the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using an IP address. For example, the collection unit prioritizes collecting news and event information in the area where the user is currently located. Furthermore, if the user is traveling, the collection unit can collect information on tourist spots and restaurants in the travel destination. Furthermore, if the user is interested in a particular city, the collection unit can prioritize collecting articles related to that city. In this way, highly relevant articles can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant articles.

[0039] The collection unit can analyze the user's social media activity when collecting articles and collect related articles. For example, the collection unit can analyze the user's social media activity when collecting articles and collect related articles. For example, the collection unit can analyze the content of social media posts. The collection unit can also analyze the number of likes and comments on social media. For example, the collection unit can collect articles with content related to articles shared by the user on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related articles. Furthermore, the collection unit can collect articles with content similar to articles that the user has "liked" on social media. In this way, related articles can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related articles.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting articles. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting articles. For example, the collection unit can customize the collection method by analyzing user reviews. The collection unit can also customize the collection method by analyzing survey results. For example, the collection unit analyzes the characteristics of articles that users have rated highly and preferentially collects similar articles. The collection unit can also adjust the collection method to avoid the characteristics of articles that users have rated poorly. Furthermore, the collection unit can customize the categories and topics of articles to be collected based on feedback provided by the user. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input user feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can determine the analysis priority based on the importance of the article during analysis. For example, the analysis unit can determine the analysis priority based on the importance of the article during analysis. For example, the analysis unit can evaluate the importance of the article based on the number of views or shares. The analysis unit can also evaluate the importance of the article based on the number of comments. For example, the analysis unit can prioritize analyzing important news articles. The analysis unit can also prioritize analyzing articles that have received high reader responses. Furthermore, the analysis unit can prioritize analyzing articles related to a specific theme. In this way, by determining the analysis priority based on the importance of the article, important articles can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article importance data into a generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can apply different analysis algorithms depending on the article category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the article category during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news articles. The analysis unit can also apply an entertainment-specific analysis algorithm to entertainment articles. For example, the analysis unit can apply an academic-specific analysis algorithm to academic papers. In this way, by applying different analysis algorithms depending on the article category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input article category data into the generation AI and have the generation AI apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past reports. The analysis unit can also improve the accuracy of the analysis by referring to a database. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also adjust the analysis method to avoid analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the posting time of the article during analysis. The analysis unit, for example, determines the analysis priority based on the posting time of the article during analysis. The analysis unit can evaluate the posting time of the article based on the posting date and time, for example. The analysis unit can also evaluate the posting time of the article based on the season or an event. For example, the analysis unit prioritizes analyzing the most recent article. The analysis unit can also prioritize analyzing articles posted within a specific period. Furthermore, the analysis unit can prioritize analyzing articles posted at a specific time in the past. In this way, by determining the analysis priority based on the posting time of the article, the most recent information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article posting time data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the articles during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the articles during analysis. For example, the analysis unit can evaluate the relevance of the articles using keyword matching technology. The analysis unit can also evaluate the relevance of the articles using topic modeling technology. For example, the analysis unit can prioritize analyzing articles related to a specific theme. The analysis unit can also prioritize analyzing articles related to user interests. Furthermore, the analysis unit can prioritize analyzing articles that have received a high reader response. As a result, by adjusting the order of analysis based on the relevance of the articles, highly relevant articles can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can, for example, evaluate the user's level of expertise based on survey results. The analysis unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user is a general reader, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the use of technical terms 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 in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The prediction unit can improve the accuracy of the prediction based on past data when making a prediction. For example, the prediction unit can improve the accuracy of the prediction based on past data when making a prediction. For example, the prediction unit can improve the accuracy of the prediction by referring to past reports. The prediction unit can also improve the accuracy of the prediction by referring to a database. For example, the prediction unit can predict reader preferences based on past data. The prediction unit can also predict the popularity of articles on a specific topic based on past data. Furthermore, the prediction unit can predict the effectiveness of advertisements based on past data. This allows for improving the accuracy of the prediction based on past data, thereby providing more accurate prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input past data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0048] The prediction unit can apply different prediction algorithms to each article category during prediction. For example, the prediction unit can apply different prediction algorithms to each article category during prediction. For example, the prediction unit can apply a news-specific prediction algorithm to news articles. The prediction unit can also apply an entertainment-specific prediction algorithm to entertainment articles. For example, the prediction unit can apply an academic-specific prediction algorithm to academic papers. In this way, by applying different prediction algorithms to each article category, more appropriate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit can be performed using, or without, a generation AI. For example, the prediction unit can input article category data into the generation AI and cause the generation AI to apply the prediction algorithm.

[0049] The prediction unit can improve the accuracy of predictions by referring to the user's past prediction results when making predictions. For example, the prediction unit can improve the accuracy of predictions by referring to the user's past prediction results when making predictions. The prediction unit can improve the accuracy of predictions by referring to past reports, for example. The prediction unit can also improve the accuracy of predictions by referring to a database. For example, the prediction unit can improve the accuracy of predictions by referring to prediction results that the user has previously rated highly. The prediction unit can also adjust the prediction method to avoid prediction results that the user has previously rated poorly. Furthermore, the prediction unit can optimize the prediction algorithm based on the user's past prediction results. This can improve the accuracy of predictions by referring to the user's past prediction results. Some or all of the above-described processing in the prediction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the prediction unit can input the user's past prediction result data into the generation AI and cause the generation AI to improve the accuracy of predictions.

[0050] The prediction unit can determine the priority of predictions based on the posting time of the article at the time of prediction. For example, the prediction unit can determine the priority of predictions based on the posting time of the article at the time of prediction. The prediction unit can evaluate the posting time of the article based on, for example, the posting date and time. The prediction unit can also evaluate the posting time of the article based on the season or an event. For example, the prediction unit can predict the most recent article with priority. The prediction unit can also predict articles posted within a specific period with priority. Furthermore, the prediction unit can predict articles posted at a specific time in the past with priority. As a result, by determining the priority of predictions based on the posting time of the article, the most recent information can be predicted with priority. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input article posting time data to the generation AI and cause the generation AI to determine the priority of predictions.

[0051] The prediction unit can adjust the order of predictions based on the relevance of the articles during prediction. For example, the prediction unit can adjust the order of predictions based on the relevance of the articles during prediction. The prediction unit can evaluate the relevance of the articles using keyword matching technology, for example. The prediction unit can also evaluate the relevance of the articles using topic modeling technology. For example, the prediction unit can preferentially predict articles related to a specific theme. The prediction unit can also preferentially predict articles related to user interests. Furthermore, the prediction unit can preferentially predict articles that have received a high reader response. As a result, by adjusting the order of predictions based on the relevance of the articles, highly relevant articles can be preferentially predicted. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input article relevance data into the generation AI and cause the generation AI to adjust the order of predictions.

[0052] The prediction unit can adjust the use of technical terms in the prediction according to the user's level of expertise during prediction. For example, the prediction unit can adjust the use of technical terms in the prediction according to the user's level of expertise during prediction. The prediction unit can evaluate the user's level of expertise based on, for example, survey results. The prediction unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the prediction unit can provide a prediction result that uses a lot of technical terms. Furthermore, if the user is a general reader, the prediction unit can provide a prediction result that avoids technical terms. Furthermore, the prediction unit can adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate prediction results to be provided by adjusting the use of technical terms in the prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The suggestion unit can adjust the level of detail of the proposal based on the prediction result when making the proposal. The suggestion unit, for example, adjusts the level of detail of the proposal based on the prediction result when making the proposal. For example, if the prediction result is detailed, the suggestion unit can make a detailed proposal. Furthermore, if the prediction result is concise, the suggestion unit can also make a concise proposal. For example, the suggestion unit can adjust the level of detail of the proposal based on the prediction result. As a result, by adjusting the level of detail of the proposal based on the prediction result, it is possible to provide a more appropriate proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input prediction result data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0054] The suggestion unit can apply different suggestion algorithms depending on the article category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the article category when making a suggestion. For example, the suggestion unit can apply a news-specific suggestion algorithm to news articles. The suggestion unit can also apply an entertainment-specific suggestion algorithm to entertainment articles. For example, the suggestion unit can apply an academic-specific suggestion algorithm to academic papers. This makes it possible to provide more appropriate suggestions by applying different suggestion algorithms depending on the article category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input article category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0055] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to past reports, for example. The suggestion unit can also improve the accuracy of the suggestion by referring to a database. For example, the suggestion unit can improve the accuracy of the suggestion by referring to suggestion results that the user has previously rated highly. The suggestion unit can also adjust the suggestion method to avoid suggestion results that the user has previously rated poorly. Furthermore, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. This can improve the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0056] The suggestion unit can determine the priority of the suggestion based on the prediction result when making a suggestion. The suggestion unit, for example, determines the priority of the suggestion based on the prediction result when making a suggestion. For example, if the prediction result has high accuracy, the suggestion unit can prioritize the suggestion. Furthermore, if the prediction result has low accuracy, the suggestion unit can postpone the suggestion. For example, the suggestion unit can adjust the priority of the suggestion based on the prediction result. This makes it possible to provide more appropriate suggestions by determining the priority of the suggestion based on the prediction result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input prediction result data to the generation AI and cause the generation AI to determine the priority of the suggestion.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the prediction results when making suggestions. The suggestion unit, for example, can adjust the order of suggestions based on the relevance of the prediction results when making suggestions. The suggestion unit can evaluate the relevance of the prediction results using keyword matching technology, for example. The suggestion unit can also evaluate the relevance of the prediction results using topic modeling technology. For example, if a prediction result has high relevance, the suggestion unit can prioritize that suggestion. Also, if a prediction result has low relevance, the suggestion unit can postpone that suggestion. Furthermore, the suggestion unit can adjust the order of suggestions based on the prediction results. As a result, by adjusting the order of suggestions based on the relevance of the prediction results, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input relevance data of the prediction results to the generation AI and cause the generation AI to adjust the order of suggestions.

[0058] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can, for example, evaluate the user's level of expertise based on survey results. The suggestion unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the suggestion unit can make a proposal that uses a lot of technical terms. For example, if the user is a general reader, the suggestion unit can make a proposal that avoids technical terms. Furthermore, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

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

[0060] The analysis unit can analyze browsing patterns during specific time periods based on the user's past browsing history. For example, if the user tends to prefer reading news articles during their morning commute, the analysis unit can prioritize analyzing news articles tailored to that time period. Also, if the user prefers to read entertainment articles at night, the analysis unit can prioritize analyzing entertainment articles tailored to that time period. Furthermore, the analysis unit can prioritize analyzing articles related to lifestyle and travel tailored to the time period when the user is relaxing on weekends. This allows for more appropriate articles to be provided by analyzing browsing patterns during specific time periods based on the user's past browsing history.

[0061] The collection unit can identify topics of interest to a user based on the user's social media activity and prioritize collecting articles related to those topics. For example, the collection unit can analyze the content of articles frequently shared by the user and collect articles related to similar topics. The collection unit can also collect related articles based on the content of posts by influencers the user follows. Furthermore, the collection unit can collect related articles based on the content of posts that the user has "liked." This allows the system to provide more appropriate content by prioritized collection of articles related to topics of interest to the user based on the user's social media activity.

[0062] The suggestion unit can customize the suggestion content based on the user's past feedback. For example, the suggestion unit can make similar suggestions based on suggestions that the user has given a high rating. The suggestion unit can also adjust the suggestion method to avoid suggestions that the user has given a low rating. Furthermore, the suggestion unit can customize the categories and topics of the suggestions based on the feedback provided by the user. This allows the suggestion content to be customized by reflecting the user's past feedback, thereby providing more appropriate suggestions.

[0063] The collection unit can prioritize collecting articles related to region-specific topics based on the user's geographical location information. For example, the collection unit prioritizes collecting news and event information for the region where the user is currently located. If the user is traveling, the collection unit can collect information about tourist attractions and restaurants at the travel destination. Furthermore, if the user is interested in a particular city, the collection unit can prioritize collecting articles related to that city. In this way, by taking the user's geographical location information into consideration, articles related to region-specific topics can be prioritized and more appropriate content can be provided.

[0064] The suggestion unit can adjust the use of technical terms in the proposed content according to the user's level of expertise. For example, if the user is an expert, the suggestion unit can make suggestions that use a lot of technical terms. Also, if the user is a general reader, the suggestion unit can make suggestions that avoid technical terms. Furthermore, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. As a result, by adjusting the use of technical terms in the proposed content according to the user's level of expertise, more appropriate suggestions can be provided.

[0065] The collection unit can integrate information from different data sources to enrich the collected data. For example, the collection unit can collect articles from multiple data sources, such as news sites, blogs, and social media. The collection unit can also integrate information from academic papers and professional journals to enrich the collected data. Furthermore, the collection unit can collect posts from influencers that the user follows. In this way, by integrating information from different data sources, the collected data can be enriched and more diverse content can be provided.

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

[0067] Step 1: The collection unit collects past articles or content. The collection unit can collect data such as blog articles, news articles, and social media posts. The collection unit can obtain data using web scraping technology or APIs. For example, the collection unit can obtain article data using the API of a news site. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using text mining technology or natural language processing technology. For example, it extracts frequently occurring keywords from the collected article data. Step 3: The prediction unit predicts the reader's preferences based on the analysis results obtained by the analysis unit. The prediction unit can predict preferences using machine learning algorithms or statistical models. For example, it can predict the topics of articles that readers will like based on past data. Step 4: The suggestion unit makes suggestions on article creation, advertising campaigns, posting timing, etc. based on the results predicted by the prediction unit. The suggestion unit can use generative AI to suggest article themes and advertisements tailored to the reader's preferences. For example, it suggests creating an article on a specific theme and posting it at the appropriate time.

[0068] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to analyze patterns in past articles and content and predict reader preferences. This system collects past articles and content, analyzes them using a generative AI, and predicts reader preferences. Based on the prediction results, the system then proposes ways to create articles tailored to the reader, develop advertising strategies, and provide appropriate posting timing. For example, the system collects data from previously posted articles, such as the titles, text, posting dates, and number of reader comments and likes. The generative AI then analyzes the collected data to extract patterns in articles and content. The generative AI then predicts reader preferences based on the past data. Finally, based on the prediction results, the system creates articles on specific topics and posts them at appropriate times. Furthermore, by providing advertisements tailored to reader preferences, advertising effectiveness can be improved. This enables the system to propose ways to create articles tailored to the reader, develop advertising strategies, and provide appropriate posting timing. For example, a news site can maintain reader interest by providing articles of interest in a timely manner. In addition, improving advertising effectiveness can also be expected to increase advertising revenue.

[0069] An information processing system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a suggestion unit. The collection unit collects past articles or content. The collection unit can collect data such as blog articles, news articles, and social media posts. The collection unit collects data using, for example, web scraping technology. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using the API of a news site. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, text mining technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can extract frequently occurring keywords from the collected article data. The prediction unit predicts reader preferences based on the analysis results obtained by the analysis unit. The prediction unit predicts preferences using, for example, a machine learning algorithm. The prediction unit can also predict preferences using a statistical model. For example, the prediction unit can predict article themes preferred by readers based on past data. The suggestion unit makes suggestions for article creation, advertising deployment, posting timing, etc. based on the results predicted by the prediction unit. The suggestion unit, for example, uses a generation AI to suggest article themes. The suggestion unit can also suggest advertisements tailored to the reader's preferences. For example, the suggestion unit suggests creating an article on a specific theme and posting it at an appropriate time. As a result, the information processing system according to the embodiment analyzes patterns of past articles and content and predicts reader preferences, thereby enabling suggestions for article creation, advertising deployment, appropriate posting timing, etc. that are tailored to the reader.

[0070] The collection unit can collect data such as the article title, text, posting date and time, reader comments, or number of likes. The collection unit collects data such as the article title, text, posting date and time, reader comments, and number of likes. For example, the collection unit can collect data such as the article title, text, posting date and time, reader comments, and number of likes for articles posted in the past on a news site. The collection unit can also collect data on social media posts. For example, the collection unit can acquire posting data using a social media API. By collecting detailed data, patterns of articles and content can be more accurately identified. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data acquired using a news site's API into the generation AI and have the generation AI collect the data.

[0071] The analysis unit can analyze the collected data and extract patterns in articles and content. For example, the analysis unit can analyze the collected data and extract patterns in articles and content. For example, the analysis unit can analyze the data using text mining technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can extract frequently occurring keywords from the collected article data. The analysis unit can also analyze the data using topic modeling technology. For example, the analysis unit can extract article topics using LDA (Latent Dirichlet Allocation). By analyzing the data, it is possible to extract patterns in articles and content and provide a basis for predicting reader preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0072] The prediction unit can predict reader preferences based on past data. The prediction unit can predict reader preferences based on past data, for example. The prediction unit can predict preferences using a machine learning algorithm, for example. The prediction unit can also predict preferences using a statistical model. For example, the prediction unit can predict themes of articles that readers will like based on past data. The prediction unit can also predict the popularity of articles on specific themes based on past data. This makes it possible to create articles and develop advertisements that are tailored to the reader by predicting reader preferences based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input past data into the generation AI and have the generation AI perform preference prediction.

[0073] The suggestion unit can make suggestions on article creation, advertising deployment, posting timing, etc. based on the prediction results. The suggestion unit can make suggestions on article creation, advertising deployment, posting timing, etc. based on the prediction results, for example. The suggestion unit can suggest article themes using a generation AI, for example. The suggestion unit can also suggest advertisements tailored to reader preferences. For example, the suggestion unit can suggest creating an article on a specific theme and posting it at an appropriate time. The suggestion unit can also deploy advertisements tailored to reader preferences. For example, the suggestion unit can suggest targeted advertisements based on reader preferences. As a result, making suggestions based on the prediction results can improve reader satisfaction. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input the prediction results to the generation AI and cause the generation AI to generate proposals.

[0074] The suggestion unit can display advertisements tailored to the reader's preferences. For example, the suggestion unit displays advertisements tailored to the reader's preferences. For example, the suggestion unit can display targeted advertisements. The suggestion unit can also display personalized advertisements. For example, the suggestion unit can display specific advertisements based on the reader's preferences. This can enhance advertising effectiveness by displaying advertisements tailored to the reader's preferences. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input reader preference data into the generation AI and have the generation AI select an advertisement.

[0075] The collection unit can estimate a user's emotions and prioritize articles and content to be collected based on the estimated user emotions. For example, the collection unit can estimate a user's emotions and prioritize articles and content to be collected based on the estimated user emotions. For example, the collection unit can estimate a user's emotions using a sentiment analysis algorithm. The collection unit can also estimate a user's emotions using natural language processing technology. For example, the collection unit can analyze a user's comments and posts to estimate emotions. Furthermore, the collection unit can prioritize articles and content to be collected based on the estimated user emotions. For example, if a user is excited, the collection unit can prioritize articles related to entertainment and sports. Furthermore, if a user is relaxed, the collection unit can prioritize articles related to lifestyle and health. Furthermore, if a user is stressed, the collection unit can prioritize articles related to relaxation and mental health. By prioritizing articles and content to be collected based on a user's emotions, more appropriate content can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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 collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0076] The collection unit can filter articles based on specific keywords or topics when collecting them. For example, the collection unit can filter articles based on specific keywords or topics when collecting them. For example, the collection unit can perform filtering using keyword matching technology. The collection unit can also perform filtering using topic modeling technology. For example, the collection unit can collect related articles based on keywords such as "technology" or "AI." The collection unit can also collect related articles based on topics such as "environmental protection" or "sustainability." The collection unit can also collect related articles based on keywords such as "economy" or "market trends." This allows highly relevant articles to be collected by filtering based on specific keywords or topics. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the collected article data into a generation AI and have the generation AI perform filtering.

[0077] When collecting articles, the collection unit can refer to the user's past browsing history to preferentially collect highly relevant articles. For example, when collecting articles, the collection unit can refer to the user's past browsing history to preferentially collect highly relevant articles. For example, the collection unit can refer to the user's past browsing history using cookie information. The collection unit can also refer to the user's past browsing history using browser history. For example, the collection unit preferentially collects articles in a category that the user has frequently viewed in the past. The collection unit can also preferentially collect articles with content similar to articles that the user has previously given high ratings to. Furthermore, the collection unit can preferentially collect articles with content related to articles on which the user has previously left comments. In this way, by referring to the user's past browsing history, highly relevant articles can be preferentially collected. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past browsing history data into the generation AI and cause the generation AI to collect highly relevant articles.

[0078] The collection unit can integrate information from different data sources to enrich the collected data when collecting articles. For example, when collecting articles, the collection unit can integrate information from different data sources to enrich the collected data. The collection unit can collect articles from multiple data sources, such as news sites, blogs, and social media. The collection unit can also integrate information from academic papers and professional journals to enrich the collected data. For example, the collection unit can acquire article data using a news site's API and simultaneously acquire related post data using a social media API. Furthermore, the collection unit can collect the posts of influencers followed by the user. This allows the collected data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data acquired from different data sources into a generation AI and have the generation AI integrate the data.

[0079] The collection unit can estimate a user's emotions and adjust the timing of article collection based on the estimated user emotions. For example, the collection unit can estimate a user's emotions and adjust the timing of article collection based on the estimated user emotions. For example, the collection unit can estimate a user's emotions using an emotion analysis algorithm. The collection unit can also estimate a user's emotions using natural language processing technology. For example, the collection unit can analyze a user's comments and posted content to estimate emotions. Furthermore, the collection unit adjusts the timing of article collection based on the estimated user emotions. For example, the collection unit collects articles about relaxation and entertainment at night when the user is relaxing. Furthermore, the collection unit can collect news articles that can be read quickly during the day when the user is busy. Furthermore, when the user is relaxing on the weekend, the collection unit can collect lifestyle and travel articles suitable for the weekend. This allows articles to be provided at a more appropriate time by adjusting the timing of article collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative 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 collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0080] When collecting articles, the collection unit can prioritize collecting highly relevant articles by taking into account the user's geographical location information. For example, when collecting articles, the collection unit prioritizes collecting highly relevant articles by taking into account the user's geographical location information. The collection unit can, for example, acquire the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using an IP address. For example, the collection unit prioritizes collecting news and event information in the area where the user is currently located. Furthermore, if the user is traveling, the collection unit can collect information on tourist spots and restaurants in the travel destination. Furthermore, if the user is interested in a particular city, the collection unit can prioritize collecting articles related to that city. In this way, highly relevant articles can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant articles.

[0081] The collection unit can analyze the user's social media activity when collecting articles and collect related articles. For example, the collection unit can analyze the user's social media activity when collecting articles and collect related articles. For example, the collection unit can analyze the content of social media posts. The collection unit can also analyze the number of likes and comments on social media. For example, the collection unit can collect articles with content related to articles shared by the user on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related articles. Furthermore, the collection unit can collect articles with content similar to articles that the user has "liked" on social media. In this way, related articles can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related articles.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting articles. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting articles. For example, the collection unit can customize the collection method by analyzing user reviews. The collection unit can also customize the collection method by analyzing survey results. For example, the collection unit analyzes the characteristics of articles that users have rated highly and preferentially collects similar articles. The collection unit can also adjust the collection method to avoid the characteristics of articles that users have rated poorly. Furthermore, the collection unit can customize the categories and topics of articles to be collected based on feedback provided by the user. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input user feedback data into the 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 level of analysis detail based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions using an emotion analysis algorithm. The analysis unit can also estimate the user's emotions using natural language processing technology. For example, the analysis unit can analyze the user's comments and posted content to estimate emotions. Furthermore, the analysis unit adjusts the level of analysis detail based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually easy-to-understand analysis results when the user is excited. By adjusting the level of analysis detail based on 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 generative 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 analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0084] The analysis unit can determine the analysis priority based on the importance of the article during analysis. For example, the analysis unit can determine the analysis priority based on the importance of the article during analysis. For example, the analysis unit can evaluate the importance of the article based on the number of views or shares. The analysis unit can also evaluate the importance of the article based on the number of comments. For example, the analysis unit can prioritize analyzing important news articles. The analysis unit can also prioritize analyzing articles that have received high reader responses. Furthermore, the analysis unit can prioritize analyzing articles related to a specific theme. In this way, by determining the analysis priority based on the importance of the article, important articles can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article importance data into a generation AI and have the generation AI determine the analysis priority.

[0085] The analysis unit can apply different analysis algorithms depending on the article category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the article category during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news articles. The analysis unit can also apply an entertainment-specific analysis algorithm to entertainment articles. For example, the analysis unit can apply an academic-specific analysis algorithm to academic papers. In this way, by applying different analysis algorithms depending on the article category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input article category data into the generation AI and have the generation AI apply the analysis algorithm.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past reports. The analysis unit can also improve the accuracy of the analysis by referring to a database. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also adjust the analysis method to avoid analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's past analysis result data 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 display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using an emotion analysis algorithm. The analysis unit can also estimate the user's emotions using natural language processing technology. For example, the analysis unit can analyze the user's comments and posted content to estimate emotions. Furthermore, the analysis unit adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can provide a simple, highly visible display method if the user is nervous. Furthermore, the analysis unit can provide a display method that includes detailed information if the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points if the user is in a hurry. This allows the display method of the analysis results to be adjusted based on the user's emotions, thereby providing a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0088] The analysis unit can determine the analysis priority based on the posting time of the article during analysis. The analysis unit, for example, determines the analysis priority based on the posting time of the article during analysis. The analysis unit can evaluate the posting time of the article based on the posting date and time, for example. The analysis unit can also evaluate the posting time of the article based on the season or an event. For example, the analysis unit prioritizes analyzing the most recent article. The analysis unit can also prioritize analyzing articles posted within a specific period. Furthermore, the analysis unit can prioritize analyzing articles posted at a specific time in the past. In this way, by determining the analysis priority based on the posting time of the article, the most recent information can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article posting time data to the generation AI and have the generation AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the articles during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the articles during analysis. For example, the analysis unit can evaluate the relevance of the articles using keyword matching technology. The analysis unit can also evaluate the relevance of the articles using topic modeling technology. For example, the analysis unit can prioritize analyzing articles related to a specific theme. The analysis unit can also prioritize analyzing articles related to user interests. Furthermore, the analysis unit can prioritize analyzing articles that have received a high reader response. As a result, by adjusting the order of analysis based on the relevance of the articles, highly relevant articles can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input article relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0090] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can, for example, evaluate the user's level of expertise based on survey results. The analysis unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user is a general reader, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the use of technical terms 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 in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0091] The prediction unit can estimate a user's emotion and adjust the level of detail of the prediction based on the estimated user's emotion. For example, the prediction unit can estimate a user's emotion and adjust the level of detail of the prediction based on the estimated user's emotion. For example, the prediction unit can estimate a user's emotion using a sentiment analysis algorithm. The prediction unit can also estimate a user's emotion using natural language processing technology. For example, the prediction unit can analyze a user's comments and posted content to estimate the emotion. Furthermore, the prediction unit adjusts the level of detail of the prediction based on the estimated user's emotion. For example, the prediction unit can provide a detailed prediction result when the user is relaxed. Furthermore, the prediction unit can provide a concise prediction result that focuses on the main points when the user is in a hurry. Furthermore, the prediction unit can provide a visually easy-to-understand prediction result when the user is excited. Thus, by adjusting the level of detail of the prediction based on the user's emotion, more appropriate prediction results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative 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 prediction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the prediction unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0092] The prediction unit can improve the accuracy of the prediction based on past data when making a prediction. For example, the prediction unit can improve the accuracy of the prediction based on past data when making a prediction. For example, the prediction unit can improve the accuracy of the prediction by referring to past reports. The prediction unit can also improve the accuracy of the prediction by referring to a database. For example, the prediction unit can predict reader preferences based on past data. The prediction unit can also predict the popularity of articles on a specific topic based on past data. Furthermore, the prediction unit can predict the effectiveness of advertisements based on past data. This allows for improving the accuracy of the prediction based on past data, thereby providing more accurate prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input past data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0093] The prediction unit can apply different prediction algorithms to each article category during prediction. For example, the prediction unit can apply different prediction algorithms to each article category during prediction. For example, the prediction unit can apply a news-specific prediction algorithm to news articles. The prediction unit can also apply an entertainment-specific prediction algorithm to entertainment articles. For example, the prediction unit can apply an academic-specific prediction algorithm to academic papers. In this way, by applying different prediction algorithms to each article category, more appropriate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit can be performed using, or without, a generation AI. For example, the prediction unit can input article category data into the generation AI and cause the generation AI to apply the prediction algorithm.

[0094] The prediction unit can improve the accuracy of predictions by referring to the user's past prediction results when making predictions. For example, the prediction unit can improve the accuracy of predictions by referring to the user's past prediction results when making predictions. The prediction unit can improve the accuracy of predictions by referring to past reports, for example. The prediction unit can also improve the accuracy of predictions by referring to a database. For example, the prediction unit can improve the accuracy of predictions by referring to prediction results that the user has previously rated highly. The prediction unit can also adjust the prediction method to avoid prediction results that the user has previously rated poorly. Furthermore, the prediction unit can optimize the prediction algorithm based on the user's past prediction results. This can improve the accuracy of predictions by referring to the user's past prediction results. Some or all of the above-described processing in the prediction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the prediction unit can input the user's past prediction result data into the generation AI and cause the generation AI to improve the accuracy of predictions.

[0095] The prediction unit can estimate a user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, the prediction unit can estimate a user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, the prediction unit can estimate a user's emotions using an emotion analysis algorithm. The prediction unit can also estimate a user's emotions using natural language processing technology. For example, the prediction unit can analyze a user's comments and posted content to estimate emotions. Furthermore, the prediction unit can adjust the display method of the prediction results based on the estimated user emotions. For example, the prediction unit can provide a simple, highly visible display method if the user is nervous. Furthermore, the prediction unit can provide a display method including detailed information if the user is relaxed. Furthermore, the prediction unit can provide a display method that focuses on the main points if the user is in a hurry. This allows for a more appropriate display method by adjusting the display method of the prediction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 prediction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the prediction unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0096] The prediction unit can determine the priority of predictions based on the posting time of the article at the time of prediction. For example, the prediction unit can determine the priority of predictions based on the posting time of the article at the time of prediction. The prediction unit can evaluate the posting time of the article based on, for example, the posting date and time. The prediction unit can also evaluate the posting time of the article based on the season or an event. For example, the prediction unit can predict the most recent article with priority. The prediction unit can also predict articles posted within a specific period with priority. Furthermore, the prediction unit can predict articles posted at a specific time in the past with priority. As a result, by determining the priority of predictions based on the posting time of the article, the most recent information can be predicted with priority. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input article posting time data to the generation AI and cause the generation AI to determine the priority of predictions.

[0097] The prediction unit can adjust the order of predictions based on the relevance of the articles during prediction. For example, the prediction unit can adjust the order of predictions based on the relevance of the articles during prediction. The prediction unit can evaluate the relevance of the articles using keyword matching technology, for example. The prediction unit can also evaluate the relevance of the articles using topic modeling technology. For example, the prediction unit can preferentially predict articles related to a specific theme. The prediction unit can also preferentially predict articles related to user interests. Furthermore, the prediction unit can preferentially predict articles that have received a high reader response. As a result, by adjusting the order of predictions based on the relevance of the articles, highly relevant articles can be preferentially predicted. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input article relevance data into the generation AI and cause the generation AI to adjust the order of predictions.

[0098] The prediction unit can adjust the use of technical terms in the prediction according to the user's level of expertise during prediction. For example, the prediction unit can adjust the use of technical terms in the prediction according to the user's level of expertise during prediction. The prediction unit can evaluate the user's level of expertise based on, for example, survey results. The prediction unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the prediction unit can provide a prediction result that uses a lot of technical terms. Furthermore, if the user is a general reader, the prediction unit can provide a prediction result that avoids technical terms. Furthermore, the prediction unit can adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate prediction results to be provided by adjusting the use of technical terms in the prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The suggestion unit can estimate a user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, the suggestion unit can estimate a user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, the suggestion unit can estimate a user's emotions using an emotion analysis algorithm. The suggestion unit can also estimate a user's emotions using natural language processing technology. For example, the suggestion unit can analyze a user's comments or posted content to estimate emotions. Furthermore, the suggestion unit adjusts the way suggestions are presented based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually easy-to-understand suggestions when the user is excited. This allows for more appropriate suggestions to be provided by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user comment data into the generation AI and cause the generation AI to estimate emotions.

[0100] The suggestion unit can adjust the level of detail of the proposal based on the prediction result when making the proposal. The suggestion unit, for example, adjusts the level of detail of the proposal based on the prediction result when making the proposal. For example, if the prediction result is detailed, the suggestion unit can make a detailed proposal. Furthermore, if the prediction result is concise, the suggestion unit can also make a concise proposal. For example, the suggestion unit can adjust the level of detail of the proposal based on the prediction result. As a result, by adjusting the level of detail of the proposal based on the prediction result, it is possible to provide a more appropriate proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input prediction result data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0101] The suggestion unit can apply different suggestion algorithms depending on the article category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the article category when making a suggestion. For example, the suggestion unit can apply a news-specific suggestion algorithm to news articles. The suggestion unit can also apply an entertainment-specific suggestion algorithm to entertainment articles. For example, the suggestion unit can apply an academic-specific suggestion algorithm to academic papers. This makes it possible to provide more appropriate suggestions by applying different suggestion algorithms depending on the article category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input article category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0102] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to past reports, for example. The suggestion unit can also improve the accuracy of the suggestion by referring to a database. For example, the suggestion unit can improve the accuracy of the suggestion by referring to suggestion results that the user has previously rated highly. The suggestion unit can also adjust the suggestion method to avoid suggestion results that the user has previously rated poorly. Furthermore, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. This can improve the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0103] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion using a sentiment analysis algorithm. The suggestion unit can also estimate the user's emotion using natural language processing technology. For example, the suggestion unit can analyze the user's comments and posted content to estimate the emotion. Furthermore, the suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually easy-to-understand suggestions when the user is excited. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using 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 suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user comment data to the generation AI and cause the generation AI to estimate emotions.

[0104] The suggestion unit can determine the priority of the suggestion based on the prediction result when making a suggestion. The suggestion unit, for example, determines the priority of the suggestion based on the prediction result when making a suggestion. For example, if the prediction result has high accuracy, the suggestion unit can prioritize the suggestion. Furthermore, if the prediction result has low accuracy, the suggestion unit can postpone the suggestion. For example, the suggestion unit can adjust the priority of the suggestion based on the prediction result. This makes it possible to provide more appropriate suggestions by determining the priority of the suggestion based on the prediction result. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input prediction result data to the generation AI and cause the generation AI to determine the priority of the suggestion.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the prediction results when making suggestions. The suggestion unit, for example, can adjust the order of suggestions based on the relevance of the prediction results when making suggestions. The suggestion unit can evaluate the relevance of the prediction results using keyword matching technology, for example. The suggestion unit can also evaluate the relevance of the prediction results using topic modeling technology. For example, if a prediction result has high relevance, the suggestion unit can prioritize that suggestion. Also, if a prediction result has low relevance, the suggestion unit can postpone that suggestion. Furthermore, the suggestion unit can adjust the order of suggestions based on the prediction results. As a result, by adjusting the order of suggestions based on the relevance of the prediction results, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input relevance data of the prediction results to the generation AI and cause the generation AI to adjust the order of suggestions.

[0106] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can, for example, evaluate the user's level of expertise based on survey results. The suggestion unit can also evaluate the user's level of expertise based on past behavioral history. For example, if the user is an expert, the suggestion unit can make a proposal that uses a lot of technical terms. For example, if the user is a general reader, the suggestion unit can make a proposal that avoids technical terms. Furthermore, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects data using web scraping technology or an API. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using text mining technology or natural language processing technology. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts reader preferences using machine learning algorithms or statistical models. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests article themes and advertisements using generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and suggestion unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data using web scraping technology or an API. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using text mining technology or natural language processing technology. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and predicts reader preferences using machine learning algorithms or statistical models. The suggestion unit is implemented, for example, by the control unit 46A of the smart glasses 214 and suggests article themes and advertisements using generative AI. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and suggestion unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset-type terminal 314 and collects data using web scraping technology or an API. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using text mining technology or natural language processing technology. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and predicts reader preferences using machine learning algorithms or statistical models. The suggestion unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and suggests article themes and advertisements using generative AI. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects data using web scraping technology or an API. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using text mining technology or natural language processing technology. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts reader preferences using machine learning algorithms or statistical models. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests article themes and advertisements using generation AI.

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

[0108] The analysis unit can analyze browsing patterns during specific time periods based on the user's past browsing history. For example, if the user tends to prefer reading news articles during their morning commute, the analysis unit can prioritize analyzing news articles tailored to that time period. Also, if the user prefers to read entertainment articles at night, the analysis unit can prioritize analyzing entertainment articles tailored to that time period. Furthermore, the analysis unit can prioritize analyzing articles related to lifestyle and travel tailored to the time period when the user is relaxing on weekends. This allows for more appropriate articles to be provided by analyzing browsing patterns during specific time periods based on the user's past browsing history.

[0109] The collection unit can identify topics of interest to a user based on the user's social media activity and prioritize collecting articles related to those topics. For example, the collection unit can analyze the content of articles frequently shared by the user and collect articles related to similar topics. The collection unit can also collect related articles based on the content of posts by influencers the user follows. Furthermore, the collection unit can collect related articles based on the content of posts that the user has "liked." This allows the system to provide more appropriate content by prioritized collection of articles related to topics of interest to the user based on the user's social media activity.

[0110] The prediction unit can estimate the user's emotion and adjust the reliability of the prediction result based on the estimated user's emotion. For example, the prediction unit can increase the reliability of the prediction result when the user is expressing a positive emotion. Furthermore, the prediction unit can set the reliability of the prediction result to a low level when the user is expressing a negative emotion. Furthermore, the prediction unit can set the reliability of the prediction result to a medium level when the user is expressing a neutral emotion. In this way, by adjusting the reliability of the prediction result based on the user's emotion, it is possible to provide a more appropriate prediction result.

[0111] The suggestion unit can customize the suggestion content based on the user's past feedback. For example, the suggestion unit can make similar suggestions based on suggestions that the user has given a high rating. The suggestion unit can also adjust the suggestion method to avoid suggestions that the user has given a low rating. Furthermore, the suggestion unit can customize the categories and topics of the suggestions based on the feedback provided by the user. This allows the suggestion content to be customized by reflecting the user's past feedback, thereby providing more appropriate suggestions.

[0112] The collection unit can prioritize collecting articles related to region-specific topics based on the user's geographical location information. For example, the collection unit prioritizes collecting news and event information for the region where the user is currently located. If the user is traveling, the collection unit can collect information about tourist attractions and restaurants at the travel destination. Furthermore, if the user is interested in a particular city, the collection unit can prioritize collecting articles related to that city. In this way, by taking the user's geographical location information into consideration, articles related to region-specific topics can be prioritized and more appropriate content can be provided.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display method can be provided.

[0114] The suggestion unit can adjust the use of technical terms in the proposed content according to the user's level of expertise. For example, if the user is an expert, the suggestion unit can make suggestions that use a lot of technical terms. Also, if the user is a general reader, the suggestion unit can make suggestions that avoid technical terms. Furthermore, the suggestion unit can adjust the use of technical terms according to the user's level of expertise. As a result, by adjusting the use of technical terms in the proposed content according to the user's level of expertise, more appropriate suggestions can be provided.

[0115] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the prediction results based on the user's emotions, a more appropriate display method can be provided.

[0116] The collection unit can integrate information from different data sources to enrich the collected data. For example, the collection unit can collect articles from multiple data sources, such as news sites, blogs, and social media. The collection unit can also integrate information from academic papers and professional journals to enrich the collected data. Furthermore, the collection unit can collect posts from influencers that the user follows. In this way, by integrating information from different data sources, the collected data can be enriched and more diverse content can be provided.

[0117] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually easy-to-understand suggestions when the user is excited. In this way, by adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be provided.

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

[0119] Step 1: The collection unit collects past articles or content. The collection unit can collect data such as blog articles, news articles, and social media posts. The collection unit can obtain data using web scraping technology or APIs. For example, the collection unit can obtain article data using the API of a news site. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using text mining technology or natural language processing technology. For example, it extracts frequently occurring keywords from the collected article data. Step 3: The prediction unit predicts the reader's preferences based on the analysis results obtained by the analysis unit. The prediction unit can predict preferences using machine learning algorithms or statistical models. For example, it can predict the topics of articles that readers will like based on past data. Step 4: The suggestion unit makes suggestions on article creation, advertising campaigns, posting timing, etc. based on the results predicted by the prediction unit. The suggestion unit can use generative AI to suggest article themes and advertisements tailored to the reader's preferences. For example, it suggests creating an article on a specific theme and posting it at the appropriate time.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0157] 7, 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects past articles or content; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts reader preferences based on the analysis results obtained by the analysis unit; a suggestion unit that suggests article creation or advertising deployment and posting timing based on the results predicted by the prediction unit; Equipped with A system characterized by:

2. The collecting unit Collect data on article title, body of text, posting date and time, and reader comments or likes 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract patterns in articles and content 2. The system of claim 1.

4. The prediction unit Predicting reader preferences based on past data 2. The system of claim 1.

5. The proposal unit Based on the prediction results, suggestions are made on article creation, advertising deployment, posting timing, etc.

2. The system of claim 1.

6. The proposal unit Tailor your ads to your readers' preferences 2. The system of claim 1.

7. The collecting unit Estimate user sentiment and prioritize articles and content to be collected based on the estimated user sentiment.

2. The system of claim 1.

8. The collecting unit When collecting articles, filter them based on specific keywords or topics.

2. The system of claim 1.

Images