system

Abstract

An object of the system according to the embodiment is to generate a summary by efficiently analyzing the contents of a URL and removing unnecessary elements.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects contents of the URL. The analysis unit analyzes the data collected by the collection unit and converts the data into a vector format. The generation unit generates a summary on the basis of the analysis result obtained by the analysis unit. The providing unit provides the summary generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently analyze the contents of URLs and remove unnecessary elements to generate summaries.

[0005] The system according to the embodiment aims to efficiently analyze the contents of a URL, remove unnecessary elements, and generate a summary. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the contents of URLs. The analysis unit analyzes the data collected by the collection unit and converts it into a vector format. The generation unit generates a summary based on the analysis results obtained by the analysis unit. The provision unit provides the summary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently analyze the contents of a URL, remove unnecessary elements, and generate a summary. [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) An analysis system according to an embodiment of the present invention accepts a URL as input, removes unnecessary elements such as advertisements, analyzes the entire page content, and outputs a summary. The analysis system collects the URL content, analyzes the collected data, and converts it into vector format. This allows for numerically deriving the relevance between text, images, videos, and audio. Furthermore, it generates and provides a summary based on the analysis results. For example, the analysis system can be used for a variety of purposes, such as summarizing news articles, academic papers, and video content in text. These processes, performed by a generative AI, allow for customization of input and output. For example, the analysis system collects the URL content and removes unnecessary elements such as advertisements. For example, when the URL of a news site is input, the system removes advertisements and collects only the article content. Next, the analysis system analyzes the collected data and converts it into vector format. For example, text data is converted into word vectors, and image data is converted into pixel vectors. This allows for numerically deriving the relevance between data of different formats. The analysis system then generates a summary based on the analysis results. For example, when generating a summary of a news article, the system extracts important information and generates a concise summary. The same is true for summarizing academic papers or video content in text. Finally, the analysis system provides the generated summary. For example, by providing a user with a summary of a news article, the user can grasp important information in a short amount of time. The same is true for summarizing academic papers or video content in text. This enables the analysis system to efficiently collect, analyze, generate, and provide summaries based on the content of URLs. This enables the analysis system to efficiently collect, analyze, generate, and provide summaries based on the content of URLs. For example, if a user places importance on specific information, a summary based on that information can be generated. This makes it possible to provide summaries tailored to individual needs.

[0029] The analysis system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects URL content. The URL content includes, but is not limited to, web page content, metadata, images, and videos. The collection unit collects web page content using, for example, scraping technology. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using a news site's API. The collection unit also removes unnecessary elements such as advertisements. For example, the collection unit detects and removes advertising banners and pop-ups. The analysis unit analyzes the data collected by the collection unit and converts it into a vector format. Examples of vector formats include, but are not limited to, TF-IDF (Term Frequency-Inverse Document Frequency) and Word2Vec. For example, the analysis unit converts text data into vectors using TF-IDF. The analysis unit can also convert image data into pixel vectors. The analysis unit can also analyze video data frame by frame and convert it into vectors. The generation unit generates a summary based on the analysis results obtained by the analysis unit. The summary may be generated using, but is not limited to, a summarization algorithm or importance scoring. For example, the generation unit may extract important information to generate a concise summary of a news article. The generation unit may also extract key points and conclusions from an academic paper to generate a summary of the paper. The generation unit may also summarize the content of a video in text form. The providing unit provides the summary generated by the generation unit. Examples of providing methods include, but are not limited to, a web interface, an API, and email notification. For example, the providing unit may display the summary to a user through a web interface. The providing unit may also provide the summary to another system through an API. The providing unit may also send the summary to a user through email notification. This enables the analysis system according to the embodiment to efficiently collect, analyze, generate, and provide the content of a URL.

[0030] The collection unit can collect the contents of URLs and remove advertisements and other unnecessary elements. The collection unit can collect webpage content using, for example, scraping technology. For example, when the collection unit inputs the URL of a news site, it collects the content of the article. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using the news site's API. The collection unit also removes unnecessary elements such as advertisements. For example, the collection unit detects and removes advertising banners and pop-ups. This allows only necessary information to be collected by removing unnecessary elements such as advertisements. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the collection unit can input the content of a webpage into the generation AI and have the generation AI detect and remove advertising banners and pop-ups.

[0031] The analysis unit can convert the collected data into a vector format. For example, the analysis unit converts text data into a vector using TF-IDF. For example, the analysis unit converts text data of a news article into a vector using TF-IDF. The analysis unit can also convert image data into a vector of pixels. For example, the analysis unit analyzes image data pixel by pixel and converts it into a vector. The analysis unit can also analyze video data frame by frame and convert it into a vector. For example, the analysis unit divides the video data into frames and converts each frame into a vector. By converting the data into a vector format, it is possible to numerically derive the relationship between data of different formats. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI convert the data into a vector format.

[0032] The generation unit can generate a summary based on the analysis results. The generation unit, for example, uses a summarization algorithm to generate a summary of a news article. For example, the generation unit extracts important information from a news article and generates a concise summary. The generation unit can also extract the main points and conclusions of an academic paper to generate a summary of the paper. For example, the generation unit extracts the main points of an academic paper and generates a concise summary. The generation unit can also summarize the content of a video in text. For example, the generation unit extracts important scenes from a video and summarizes them in text. This allows important information to be concisely summarized by generating a summary based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the analysis results to the generation AI and have the generation AI generate a summary.

[0033] The providing unit can provide the generated summary. The providing unit can, for example, display the summary to the user through a web interface. For example, the providing unit can display the summary on a web page to allow the user to easily access it. The providing unit can also provide the summary to other systems through an API. For example, the providing unit can send the summary to other applications through the API. The providing unit can also send the summary to the user through an email notification. For example, the providing unit can send the summary to the user's email address. In this way, by providing the generated summary, the user can grasp important information in a short time. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the generated summary to the generation AI and have the generation AI select a presentation method.

[0034] The generation unit can generate text summaries of news articles, academic papers, and video content. For example, to generate summaries of news articles, the generation unit extracts important information and generates concise summaries. For example, the generation unit extracts important points from news articles and generates summaries. The generation unit can also extract key points and conclusions from academic papers and generate summaries. For example, the generation unit extracts key points from academic papers and generates concise summaries. Furthermore, the generation unit can also summarize video content in text. For example, the generation unit extracts important scenes from videos and summarizes them in text. This allows for efficient summaries of news articles, academic papers, and video content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data from news articles, academic papers, and videos into the generation AI and have the generation AI generate summaries.

[0035] The collection unit can analyze a user's past URL collection history and select an appropriate collection method. For example, the collection unit analyzes the patterns of URLs frequently collected by the user in the past and prioritizes collection of URLs with similar patterns. For example, the collection unit stores the user's past collection history in a database and analyzes it using pattern mining technology. The collection unit can also analyze the time periods of URLs collected by the user in the past and collect them during the same time periods. For example, the collection unit can analyze the user's collection history as time-series data and identify the optimal collection time. Furthermore, the collection unit can analyze the types of URLs collected by the user in the past and prioritize collection of URLs of the same type. For example, the collection unit can classify the user's collection history by category and prioritize collection of URLs in the same category. This allows the optimal collection method to be selected by analyzing the past URL collection history. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past collection history into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting URLs, the collection unit can filter them based on the user's current areas of interest. For example, the collection unit prioritizes collecting URLs related to topics that the user is currently interested in. For example, the collection unit can analyze the user's search history to identify related topics. The collection unit can also filter related URLs based on keywords recently searched by the user. For example, the collection unit can store the user's search keywords in a database and identify related URLs using keyword matching technology. Furthermore, the collection unit can filter URLs based on the content of posts from accounts the user follows on social media. For example, the collection unit can analyze the user's social media accounts to identify related posts. This allows highly relevant information to be collected by filtering URLs based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0037] When collecting URLs, the collection unit can select an appropriate collection method depending on the user's input method. For example, if the user inputs a URL by voice, the collection unit collects the URL using voice recognition technology. For example, the collection unit records the user's voice with a microphone and converts the URL into text using voice recognition software. Alternatively, if the user inputs a URL as text, the collection unit can collect the URL using text analysis technology. For example, the collection unit analyzes the text entered by the user and identifies the URL. Furthermore, if the user inputs a URL as an image, the collection unit can collect the URL using image recognition technology. For example, the collection unit analyzes an image taken by the user and identifies the URL in the image. This allows URLs to be collected efficiently by selecting the optimal collection method depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the collection method.

[0038] When collecting URLs, the collection unit can prioritize collecting highly relevant URLs by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting news and event information related to the user's current location. For example, the collection unit acquires the user's GPS data and identifies information related to the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting tourist spot and restaurant information at the user's travel destination. For example, the collection unit analyzes the user's IP address and identifies information about the travel destination. Furthermore, if the user is interested in a particular region, the collection unit can prioritize collecting information related to that region. For example, the collection unit analyzes the user's search history and identifies the region of interest. This allows highly relevant information to be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to identify highly relevant URLs.

[0039] When collecting URLs, the collection unit can analyze the user's social media activities and collect related URLs. The collection unit, for example, collects related URLs based on the content posted by accounts the user follows on social media. For example, the collection unit analyzes the user's social media accounts and identifies the content posted by the accounts the user follows. The collection unit can also collect URLs related to articles the user shared on social media. For example, the collection unit analyzes the user's sharing history and identifies related URLs. The collection unit can also analyze the user's social media activity history and collect related URLs. For example, the collection unit analyzes the user's likes and comments history and identifies related URLs. This makes it possible to collect highly relevant information by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and have the generation AI identify related URLs.

[0040] When collecting URLs, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes the collection method for URLs that the user has previously rated highly. For example, the collection unit stores user feedback data in a database and identifies highly rated collection methods. The collection unit can also avoid collection methods for URLs that the user has previously rated poorly. For example, the collection unit analyzes the user's feedback data and identifies poorly rated collection methods. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. For example, the collection unit inputs the user's feedback data into a machine learning algorithm and suggests an optimal collection method. This allows the optimal collection method to be selected by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a frequency analysis of the data to identify data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs an impact score on the data to identify data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit adjusts the order of analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the text data and performs keyword extraction and topic modeling. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes the image data and performs object detection and image classification. The analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit analyzes the video data and performs scene detection and action recognition. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit may refer to analysis results that the user has previously rated highly and apply a similar analysis method. For example, the analysis unit may store the user's past analysis results in a database and identify an analysis method using a machine learning algorithm. The analysis unit may also avoid analysis results that the user has previously rated poorly. For example, the analysis unit may analyze the user's past analysis results and identify low-rated analysis methods. Furthermore, the analysis unit may analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit may input the user's past analysis results into a machine learning algorithm and suggest an optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the data collection time. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit stores the data collection time in a database and identifies the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit analyzes the data collection time and identifies older data. Furthermore, the analysis unit can adjust the analysis priority according to the data collection time. For example, the analysis unit adjusts the order of analysis based on the data collection time. This enables efficient analysis by determining the analysis priority based on the data collection time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data collection time to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit performs correlation analysis of the data to identify highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit performs clustering of the data to identify less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, the analysis unit can analyze the user's profile data to identify the user's level of expertise. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. For example, the analysis unit can analyze the user's profile data to identify the user's level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way the analysis results are presented based on 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 into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0047] When generating a summary, the generation unit can adjust the level of detail of the summary based on the importance of the analysis result. For example, the generation unit generates a detailed summary for an analysis result with high importance. For example, the generation unit performs a frequency analysis of the analysis results to identify results with high importance. The generation unit can also generate a concise summary for analysis results with low importance. For example, the generation unit performs impact scoring of the analysis results to identify results with low importance. Furthermore, the generation unit can adjust the level of detail of the summary according to the importance of the analysis result. For example, the generation unit adjusts the level of detail of the summary based on the importance of the analysis result. This enables efficient summary generation by adjusting the level of detail of the summary based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0048] When generating a summary, the generation unit can apply different summary generation algorithms depending on the data category. For example, the generation unit applies a natural language processing algorithm to text data to generate a summary. For example, the generation unit analyzes the text data and performs keyword extraction and topic modeling. The generation unit can also apply an image recognition algorithm to image data to generate a summary. For example, the generation unit analyzes the image data and performs object detection and image classification. The generation unit can also apply a video analysis algorithm to video data to generate a summary. For example, the generation unit analyzes the video data and performs scene detection and action recognition. This improves the accuracy of summary generation by applying an appropriate summary generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the data category to the generation AI and cause the generation AI to select an appropriate summary generation algorithm.

[0049] When generating a summary, the generation unit can improve the accuracy of the summary by referring to the user's past summary results. For example, the generation unit generates a similar summary by referring to summary results that the user previously rated highly. For example, the generation unit stores the user's past summary results in a database and identifies a summary generation method using a machine learning algorithm. The generation unit can also avoid summary results that the user previously rated poorly. For example, the generation unit analyzes the user's past summary results and identifies a low-rated summary generation method. Furthermore, the generation unit can analyze the user's past summary results and suggest an optimal summary generation method. For example, the generation unit inputs the user's past summary results into a machine learning algorithm and suggests an optimal summary generation method. This improves the accuracy of the summary by referring to the user's past summary results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past summary results into the generation AI and causes the generation AI to improve the accuracy of the summary.

[0050] When generating summaries, the generation unit can determine the priority of summaries based on the time when the data was collected. The generation unit, for example, prioritizes generating summaries from the most recent data. For example, the generation unit stores the time when the data was collected in a database and identifies the most recent data. The generation unit can also generate summaries by putting older data on hold. For example, the generation unit analyzes the time when the data was collected and identifies older data. Furthermore, the generation unit can adjust the priority of summary generation according to the time when the data was collected. For example, the generation unit adjusts the order of summary generation based on the time when the data was collected. This enables efficient summary generation by determining the priority of summaries based on the time when the data was collected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of summary generation.

[0051] The generation unit can adjust the order of summaries based on the relevance of the data when generating summaries. The generation unit, for example, prioritizes generating summaries for highly relevant data. For example, the generation unit performs a correlation analysis of the data to identify highly relevant data. The generation unit can also postpone generating summaries for less relevant data. For example, the generation unit performs data clustering to identify less relevant data. The generation unit can also adjust the order of summary generation according to the relevance of the data. For example, the generation unit adjusts the order of summary generation based on the relevance of the data. This enables efficient summary generation by adjusting the order of summaries based on the relevance of the data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of summary generation.

[0052] When generating a summary, the generation unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a summary that uses a lot of technical terms. For example, the generation unit analyzes the user's profile data to identify the user's level of expertise. Furthermore, if the user does not have technical expertise, the generation unit can generate a summary that avoids technical terms. For example, the generation unit analyzes the user's profile data to identify the user's level of expertise. Furthermore, the generation unit can adjust the way the summary is expressed according to the user's level of expertise. For example, the generation unit adjusts the way the summary is expressed based on the user's level of expertise. This allows for the provision of a more appropriate summary by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the way the summary is expressed.

[0053] When providing a summary, the providing unit can select the optimal delivery method by referring to the user's past usage history. For example, the providing unit prioritizes delivery methods that the user has previously rated highly. For example, the providing unit stores the user's usage history data in a database and identifies delivery methods that have been highly rated. The providing unit can also avoid delivery methods that the user has previously rated poorly. For example, the providing unit analyzes the user's usage history data and identifies delivery methods that have been poorly rated. Furthermore, the providing unit can analyze the user's past usage history and suggest the optimal delivery method. For example, the providing unit inputs the user's usage history data into a machine learning algorithm and suggests the optimal delivery method. This allows the optimal delivery method to be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's usage history data into the generation AI and have the generation AI select the delivery method.

[0054] When providing a summary, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is working, the providing unit prioritizes providing information related to work. For example, the providing unit analyzes the user's task data and identifies information related to work. Furthermore, when the user is taking a break, the providing unit can also provide information that will help the user relax. For example, the providing unit analyzes the user's task data and identifies information that will help the user relax. Furthermore, when the user is traveling, the providing unit can also provide tourist information about the travel destination. For example, the providing unit analyzes the user's task data and identifies information about the travel destination. This allows the content to be customized according to the user's current task, thereby providing more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's task data into the generation AI and cause the generation AI to customize the content to be provided.

[0055] The providing unit can improve the delivery method by reflecting user feedback when providing a summary. For example, the providing unit preferentially adopts delivery methods that users have given high ratings. For example, the providing unit stores user feedback data in a database and identifies delivery methods that users have given high ratings. The providing unit can also avoid delivery methods that users have given low ratings. For example, the providing unit analyzes user feedback data and identifies delivery methods that users have given low ratings. Furthermore, the providing unit can analyze user feedback and suggest an optimal delivery method. For example, the providing unit inputs user feedback data into a machine learning algorithm and suggests an optimal delivery method. This allows the delivery method to be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the delivery method.

[0056] When providing a summary, the providing unit can select the optimal presentation method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a presentation method tailored to the screen size. For example, the providing unit acquires the user's device information and selects a display format optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a presentation method optimized for a large screen. For example, the providing unit acquires the user's device information and selects a display format optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a presentation method that is concise and highly visible. For example, the providing unit acquires the user's device information and selects a display format optimized for the smartwatch. This allows the optimal presentation method to be selected by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select a presentation method.

[0057] When providing a summary, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the summary based on the language setting of the user's device. For example, the providing unit acquires the user's device information and identifies the language setting. The providing unit can also provide a language switching function if the user uses multiple languages. For example, the providing unit analyzes the user's profile data and identifies the language used. Furthermore, if the user selects a specific language, the providing unit can provide the summary in that language. For example, the providing unit generates a summary based on the language selected by the user. This makes it possible to provide more appropriate information by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the provided content.

[0058] When providing a summary, the providing unit can customize the content to be provided based on the user's occupation and lifestyle. For example, if the user is a doctor, the providing unit can prioritize providing medical-related information. For example, the providing unit can analyze the user's profile data to identify their occupation. Furthermore, if the user is a student, the providing unit can prioritize providing information related to their studies. For example, the providing unit can analyze the user's profile data to identify their occupation. Furthermore, if the user likes to travel, the providing unit can prioritize providing information related to travel. For example, the providing unit can analyze the user's profile data to identify their lifestyle. This allows the content to be customized based on the user's occupation and lifestyle, thereby providing more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's profile data into the generation AI and have the generation AI customize the content to be provided.

[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 system can further analyze the user's past browsing history and prioritize collecting related information. For example, the collection unit analyzes the patterns of websites the user has frequently visited in the past and prioritizes collecting URLs with similar patterns. The collection unit can also analyze the time period during which the user previously collected data and collect data during the same time period. The collection unit can also analyze the type of data the user previously collected and prioritize collecting data of the same type. This makes it possible to collect more relevant information by utilizing the user's past browsing history.

[0061] The analysis system can further determine the priority of analysis based on the time of data collection. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of data collection. This allows for efficient analysis by determining the priority of analysis based on the time of data collection.

[0062] The analysis system can further adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented 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.

[0063] The analysis system can also apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a video analysis algorithm to video data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category.

[0064] The analysis system can further improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly and applies a similar analysis method. The analysis unit can also avoid analysis results that the user has previously rated poorly. Furthermore, the analysis unit can analyze the user's past analysis results and suggest the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0065] The analysis system can further adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit can perform a detailed analysis if the user has specialized knowledge. Alternatively, the analysis unit can perform a brief analysis if the user does not have specialized knowledge. Furthermore, the analysis unit can determine the priority of the analysis according to the user's level of expertise. This allows the system to provide more appropriate analysis results by adjusting the level of detail of the analysis according to the user's level of expertise.

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

[0067] Step 1: The collector collects the URL content, including web page content, metadata, images, videos, etc. The collector uses scraping techniques and APIs to obtain the data and removes unnecessary elements such as advertisements. Step 2: The analysis unit analyzes the data collected by the collection unit and converts it into a vector format. Vector formats include TF-IDF and Word2Vec. The analysis unit converts text data, image data, and video data into appropriate vector formats. Step 3: The generator generates a summary based on the analysis results obtained by the analyzer. The generator uses summarization algorithms and importance scoring to generate a summary. The generator summarizes the content of news articles, academic papers, and videos. Step 4: The provider provides the summary generated by the generator. Methods of provisioning include a web interface, an API, email notification, etc. The provider displays the summary to the user, provides the summary to other systems, and sends the summary via email notification.

[0068] (Example 2) An analysis system according to an embodiment of the present invention accepts a URL as input, removes unnecessary elements such as advertisements, analyzes the entire page content, and outputs a summary. The analysis system collects the URL content, analyzes the collected data, and converts it into vector format. This allows for numerically deriving the relevance between text, images, videos, and audio. Furthermore, it generates and provides a summary based on the analysis results. For example, the analysis system can be used for a variety of purposes, such as summarizing news articles, academic papers, and video content in text. These processes, performed by a generative AI, allow for customization of input and output. For example, the analysis system collects the URL content and removes unnecessary elements such as advertisements. For example, when the URL of a news site is input, the system removes advertisements and collects only the article content. Next, the analysis system analyzes the collected data and converts it into vector format. For example, text data is converted into word vectors, and image data is converted into pixel vectors. This allows for numerically deriving the relevance between data of different formats. The analysis system then generates a summary based on the analysis results. For example, when generating a summary of a news article, the system extracts important information and generates a concise summary. The same is true for summarizing academic papers or video content in text. Finally, the analysis system provides the generated summary. For example, by providing a user with a summary of a news article, the user can grasp important information in a short amount of time. The same is true for summarizing academic papers or video content in text. This enables the analysis system to efficiently collect, analyze, generate, and provide summaries based on the content of URLs. This enables the analysis system to efficiently collect, analyze, generate, and provide summaries based on the content of URLs. For example, if a user places importance on specific information, a summary based on that information can be generated. This makes it possible to provide summaries tailored to individual needs.

[0069] The analysis system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects URL content. The URL content includes, but is not limited to, web page content, metadata, images, and videos. The collection unit collects web page content using, for example, scraping technology. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using a news site's API. The collection unit also removes unnecessary elements such as advertisements. For example, the collection unit detects and removes advertising banners and pop-ups. The analysis unit analyzes the data collected by the collection unit and converts it into a vector format. Examples of vector formats include, but are not limited to, TF-IDF (Term Frequency-Inverse Document Frequency) and Word2Vec. For example, the analysis unit converts text data into vectors using TF-IDF. The analysis unit can also convert image data into pixel vectors. The analysis unit can also analyze video data frame by frame and convert it into vectors. The generation unit generates a summary based on the analysis results obtained by the analysis unit. The summary may be generated using, but is not limited to, a summarization algorithm or importance scoring. For example, the generation unit may extract important information to generate a concise summary of a news article. The generation unit may also extract key points and conclusions from an academic paper to generate a summary of the paper. The generation unit may also summarize the content of a video in text form. The providing unit provides the summary generated by the generation unit. Examples of providing methods include, but are not limited to, a web interface, an API, and email notification. For example, the providing unit may display the summary to a user through a web interface. The providing unit may also provide the summary to another system through an API. The providing unit may also send the summary to a user through email notification. This enables the analysis system according to the embodiment to efficiently collect, analyze, generate, and provide the content of a URL.

[0070] The collection unit can collect the contents of URLs and remove advertisements and other unnecessary elements. The collection unit can collect webpage content using, for example, scraping technology. For example, when the collection unit inputs the URL of a news site, it collects the content of the article. The collection unit can also acquire data using an API. For example, the collection unit can acquire article data using the news site's API. The collection unit also removes unnecessary elements such as advertisements. For example, the collection unit detects and removes advertising banners and pop-ups. This allows only necessary information to be collected by removing unnecessary elements such as advertisements. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the collection unit can input the content of a webpage into the generation AI and have the generation AI detect and remove advertising banners and pop-ups.

[0071] The analysis unit can convert the collected data into a vector format. For example, the analysis unit converts text data into a vector using TF-IDF. For example, the analysis unit converts text data of a news article into a vector using TF-IDF. The analysis unit can also convert image data into a vector of pixels. For example, the analysis unit analyzes image data pixel by pixel and converts it into a vector. The analysis unit can also analyze video data frame by frame and convert it into a vector. For example, the analysis unit divides the video data into frames and converts each frame into a vector. By converting the data into a vector format, it is possible to numerically derive the relationship between data of different formats. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI convert the data into a vector format.

[0072] The generation unit can generate a summary based on the analysis results. The generation unit, for example, uses a summarization algorithm to generate a summary of a news article. For example, the generation unit extracts important information from a news article and generates a concise summary. The generation unit can also extract the main points and conclusions of an academic paper to generate a summary of the paper. For example, the generation unit extracts the main points of an academic paper and generates a concise summary. The generation unit can also summarize the content of a video in text. For example, the generation unit extracts important scenes from a video and summarizes them in text. This allows important information to be concisely summarized by generating a summary based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the analysis results to the generation AI and have the generation AI generate a summary.

[0073] The providing unit can provide the generated summary. The providing unit can, for example, display the summary to the user through a web interface. For example, the providing unit can display the summary on a web page to allow the user to easily access it. The providing unit can also provide the summary to other systems through an API. For example, the providing unit can send the summary to other applications through the API. The providing unit can also send the summary to the user through an email notification. For example, the providing unit can send the summary to the user's email address. In this way, by providing the generated summary, the user can grasp important information in a short time. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the generated summary to the generation AI and have the generation AI select a presentation method.

[0074] The generation unit can generate text summaries of news articles, academic papers, and video content. For example, to generate summaries of news articles, the generation unit extracts important information and generates concise summaries. For example, the generation unit extracts important points from news articles and generates summaries. The generation unit can also extract key points and conclusions from academic papers and generate summaries. For example, the generation unit extracts key points from academic papers and generates concise summaries. Furthermore, the generation unit can also summarize video content in text. For example, the generation unit extracts important scenes from videos and summarizes them in text. This allows for efficient summaries of news articles, academic papers, and video content. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data from news articles, academic papers, and videos into the generation AI and have the generation AI generate summaries.

[0075] The collection unit can estimate the user's emotions and adjust the timing of URL collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and waits until the user relaxes. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is concentrating, the collection unit can immediately collect URLs and quickly start analysis. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is tired, the collection unit can adjust the collection timing to collect URLs after the user has taken a break. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the URL collection timing to be adjusted according to the user's emotions, thereby enabling URLs to be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0076] The collection unit can analyze a user's past URL collection history and select an appropriate collection method. For example, the collection unit analyzes the patterns of URLs frequently collected by the user in the past and prioritizes collection of URLs with similar patterns. For example, the collection unit stores the user's past collection history in a database and analyzes it using pattern mining technology. The collection unit can also analyze the time periods of URLs collected by the user in the past and collect them during the same time periods. For example, the collection unit can analyze the user's collection history as time-series data and identify the optimal collection time. Furthermore, the collection unit can analyze the types of URLs collected by the user in the past and prioritize collection of URLs of the same type. For example, the collection unit can classify the user's collection history by category and prioritize collection of URLs in the same category. This allows the optimal collection method to be selected by analyzing the past URL collection history. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past collection history into the generation AI and have the generation AI select the optimal collection method.

[0077] When collecting URLs, the collection unit can filter them based on the user's current areas of interest. For example, the collection unit prioritizes collecting URLs related to topics that the user is currently interested in. For example, the collection unit can analyze the user's search history to identify related topics. The collection unit can also filter related URLs based on keywords recently searched by the user. For example, the collection unit can store the user's search keywords in a database and identify related URLs using keyword matching technology. Furthermore, the collection unit can filter URLs based on the content of posts from accounts the user follows on social media. For example, the collection unit can analyze the user's social media accounts to identify related posts. This allows highly relevant information to be collected by filtering URLs based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0078] When collecting URLs, the collection unit can select an appropriate collection method depending on the user's input method. For example, if the user inputs a URL by voice, the collection unit collects the URL using voice recognition technology. For example, the collection unit records the user's voice with a microphone and converts the URL into text using voice recognition software. Alternatively, if the user inputs a URL as text, the collection unit can collect the URL using text analysis technology. For example, the collection unit analyzes the text entered by the user and identifies the URL. Furthermore, if the user inputs a URL as an image, the collection unit can collect the URL using image recognition technology. For example, the collection unit analyzes an image taken by the user and identifies the URL in the image. This allows URLs to be collected efficiently by selecting the optimal collection method depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the collection method.

[0079] The collection unit can estimate the user's emotions and prioritize the URLs to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest news and trending information. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can prioritize collecting URLs related to entertainment and hobbies. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is stressed, the collection unit can prioritize collecting URLs related to relaxation and mental health. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for more appropriate information to be collected by prioritizing URLs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of URLs.

[0080] When collecting URLs, the collection unit can prioritize collecting highly relevant URLs by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting news and event information related to the user's current location. For example, the collection unit acquires the user's GPS data and identifies information related to the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting tourist spot and restaurant information at the user's travel destination. For example, the collection unit analyzes the user's IP address and identifies information about the travel destination. Furthermore, if the user is interested in a particular region, the collection unit can prioritize collecting information related to that region. For example, the collection unit analyzes the user's search history and identifies the region of interest. This allows highly relevant information to be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to identify highly relevant URLs.

[0081] When collecting URLs, the collection unit can analyze the user's social media activities and collect related URLs. The collection unit, for example, collects related URLs based on the content posted by accounts the user follows on social media. For example, the collection unit analyzes the user's social media accounts and identifies the content posted by the accounts the user follows. The collection unit can also collect URLs related to articles the user shared on social media. For example, the collection unit analyzes the user's sharing history and identifies related URLs. The collection unit can also analyze the user's social media activity history and collect related URLs. For example, the collection unit analyzes the user's likes and comments history and identifies related URLs. This makes it possible to collect highly relevant information by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and have the generation AI identify related URLs.

[0082] When collecting URLs, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes the collection method for URLs that the user has previously rated highly. For example, the collection unit stores user feedback data in a database and identifies highly rated collection methods. The collection unit can also avoid collection methods for URLs that the user has previously rated poorly. For example, the collection unit analyzes the user's feedback data and identifies poorly rated collection methods. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal collection method. For example, the collection unit inputs the user's feedback data into a machine learning algorithm and suggests an optimal collection method. This allows the optimal collection method to be selected by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotions using an emotion estimation algorithm. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a frequency analysis of the data to identify data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs an impact score on the data to identify data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit adjusts the order of analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the text data and performs keyword extraction and topic modeling. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes the image data and performs object detection and image classification. The analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit analyzes the video data and performs scene detection and action recognition. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select an appropriate analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit may refer to analysis results that the user has previously rated highly and apply a similar analysis method. For example, the analysis unit may store the user's past analysis results in a database and identify an analysis method using a machine learning algorithm. The analysis unit may also avoid analysis results that the user has previously rated poorly. For example, the analysis unit may analyze the user's past analysis results and identify low-rated analysis methods. Furthermore, the analysis unit may analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit may input the user's past analysis results into a machine learning algorithm and suggest an optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the analysis priority based on the data collection time. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit stores the data collection time in a database and identifies the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit analyzes the data collection time and identifies older data. Furthermore, the analysis unit can adjust the analysis priority according to the data collection time. For example, the analysis unit adjusts the order of analysis based on the data collection time. This enables efficient analysis by determining the analysis priority based on the data collection time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data collection time to the generation AI and have the generation AI determine the analysis priority.

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

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, the analysis unit can analyze the user's profile data to identify the user's level of expertise. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. For example, the analysis unit can analyze the user's profile data to identify the user's level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way the analysis results are presented based on 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 into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0091] The generation unit can estimate the user's emotions and adjust the summary generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a summary that progresses at a leisurely pace. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the generation unit can generate a summary that emphasizes the shortest route. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a summary that adds visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the summary generation method to be adjusted according to the user's emotions, thereby providing a more appropriate summary. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or 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 generation unit may be performed using, or without, a generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the summary generation method.

[0092] When generating a summary, the generation unit can adjust the level of detail of the summary based on the importance of the analysis result. For example, the generation unit generates a detailed summary for an analysis result with high importance. For example, the generation unit performs a frequency analysis of the analysis results to identify results with high importance. The generation unit can also generate a concise summary for analysis results with low importance. For example, the generation unit performs impact scoring of the analysis results to identify results with low importance. Furthermore, the generation unit can adjust the level of detail of the summary according to the importance of the analysis result. For example, the generation unit adjusts the level of detail of the summary based on the importance of the analysis result. This enables efficient summary generation by adjusting the level of detail of the summary based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0093] When generating a summary, the generation unit can apply different summary generation algorithms depending on the data category. For example, the generation unit applies a natural language processing algorithm to text data to generate a summary. For example, the generation unit analyzes the text data and performs keyword extraction and topic modeling. The generation unit can also apply an image recognition algorithm to image data to generate a summary. For example, the generation unit analyzes the image data and performs object detection and image classification. The generation unit can also apply a video analysis algorithm to video data to generate a summary. For example, the generation unit analyzes the video data and performs scene detection and action recognition. This improves the accuracy of summary generation by applying an appropriate summary generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the data category to the generation AI and cause the generation AI to select an appropriate summary generation algorithm.

[0094] When generating a summary, the generation unit can improve the accuracy of the summary by referring to the user's past summary results. For example, the generation unit generates a similar summary by referring to summary results that the user previously rated highly. For example, the generation unit stores the user's past summary results in a database and identifies a summary generation method using a machine learning algorithm. The generation unit can also avoid summary results that the user previously rated poorly. For example, the generation unit analyzes the user's past summary results and identifies a low-rated summary generation method. Furthermore, the generation unit can analyze the user's past summary results and suggest an optimal summary generation method. For example, the generation unit inputs the user's past summary results into a machine learning algorithm and suggests an optimal summary generation method. This improves the accuracy of the summary by referring to the user's past summary results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past summary results into the generation AI and causes the generation AI to improve the accuracy of the summary.

[0095] The generation unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise summary. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the generation unit can generate a longer summary with detailed explanations. For example, the generation unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a summary with visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the length of the summary to be adjusted according to the user's emotions, thereby providing a more appropriate summary. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or 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 generation unit may be performed using, or without, a generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the length of the summary.

[0096] When generating summaries, the generation unit can determine the priority of summaries based on the time when the data was collected. The generation unit, for example, prioritizes generating summaries from the most recent data. For example, the generation unit stores the time when the data was collected in a database and identifies the most recent data. The generation unit can also generate summaries by putting older data on hold. For example, the generation unit analyzes the time when the data was collected and identifies older data. Furthermore, the generation unit can adjust the priority of summary generation according to the time when the data was collected. For example, the generation unit adjusts the order of summary generation based on the time when the data was collected. This enables efficient summary generation by determining the priority of summaries based on the time when the data was collected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of summary generation.

[0097] The generation unit can adjust the order of summaries based on the relevance of the data when generating summaries. The generation unit, for example, prioritizes generating summaries for highly relevant data. For example, the generation unit performs a correlation analysis of the data to identify highly relevant data. The generation unit can also postpone generating summaries for less relevant data. For example, the generation unit performs data clustering to identify less relevant data. The generation unit can also adjust the order of summary generation according to the relevance of the data. For example, the generation unit adjusts the order of summary generation based on the relevance of the data. This enables efficient summary generation by adjusting the order of summaries based on the relevance of the data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of summary generation.

[0098] When generating a summary, the generation unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a summary that uses a lot of technical terms. For example, the generation unit analyzes the user's profile data to identify the user's level of expertise. Furthermore, if the user does not have technical expertise, the generation unit can generate a summary that avoids technical terms. For example, the generation unit analyzes the user's profile data to identify the user's level of expertise. Furthermore, the generation unit can adjust the way the summary is expressed according to the user's level of expertise. For example, the generation unit adjusts the way the summary is expressed based on the user's level of expertise. This allows for the provision of a more appropriate summary by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the way the summary is expressed.

[0099] The providing unit can estimate the user's emotions and adjust the summary presentation method based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible presentation method. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide a presentation method that includes detailed information. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide a summary that focuses on the main points. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate summary presentation by adjusting the summary presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to adjust the providing method.

[0100] When providing a summary, the providing unit can select the optimal delivery method by referring to the user's past usage history. For example, the providing unit prioritizes delivery methods that the user has previously rated highly. For example, the providing unit stores the user's usage history data in a database and identifies delivery methods that have been highly rated. The providing unit can also avoid delivery methods that the user has previously rated poorly. For example, the providing unit analyzes the user's usage history data and identifies delivery methods that have been poorly rated. Furthermore, the providing unit can analyze the user's past usage history and suggest the optimal delivery method. For example, the providing unit inputs the user's usage history data into a machine learning algorithm and suggests the optimal delivery method. This allows the optimal delivery method to be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's usage history data into the generation AI and have the generation AI select the delivery method.

[0101] When providing a summary, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is working, the providing unit prioritizes providing information related to work. For example, the providing unit analyzes the user's task data and identifies information related to work. Furthermore, when the user is taking a break, the providing unit can also provide information that will help the user relax. For example, the providing unit analyzes the user's task data and identifies information that will help the user relax. Furthermore, when the user is traveling, the providing unit can also provide tourist information about the travel destination. For example, the providing unit analyzes the user's task data and identifies information about the travel destination. This allows the content to be customized according to the user's current task, thereby providing more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's task data into the generation AI and cause the generation AI to customize the content to be provided.

[0102] The providing unit can improve the delivery method by reflecting user feedback when providing a summary. For example, the providing unit preferentially adopts delivery methods that users have given high ratings. For example, the providing unit stores user feedback data in a database and identifies delivery methods that users have given high ratings. The providing unit can also avoid delivery methods that users have given low ratings. For example, the providing unit analyzes user feedback data and identifies delivery methods that users have given low ratings. Furthermore, the providing unit can analyze user feedback and suggest an optimal delivery method. For example, the providing unit inputs user feedback data into a machine learning algorithm and suggests an optimal delivery method. This allows the delivery method to be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the delivery method.

[0103] The providing unit can estimate the user's emotions and adjust the order in which summaries are provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important information. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide the summary in an order that includes detailed information. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide the summary in an order that highlights the main points. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate summary provision by adjusting the order in which summaries are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to adjust the providing order.

[0104] When providing a summary, the providing unit can select the optimal presentation method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a presentation method tailored to the screen size. For example, the providing unit acquires the user's device information and selects a display format optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a presentation method optimized for a large screen. For example, the providing unit acquires the user's device information and selects a display format optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a presentation method that is concise and highly visible. For example, the providing unit acquires the user's device information and selects a display format optimized for the smartwatch. This allows the optimal presentation method to be selected by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select a presentation method.

[0105] When providing a summary, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the summary based on the language setting of the user's device. For example, the providing unit acquires the user's device information and identifies the language setting. The providing unit can also provide a language switching function if the user uses multiple languages. For example, the providing unit analyzes the user's profile data and identifies the language used. Furthermore, if the user selects a specific language, the providing unit can provide the summary in that language. For example, the providing unit generates a summary based on the language selected by the user. This makes it possible to provide more appropriate information by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the provided content.

[0106] When providing a summary, the providing unit can customize the content to be provided based on the user's occupation and lifestyle. For example, if the user is a doctor, the providing unit can prioritize providing medical-related information. For example, the providing unit can analyze the user's profile data to identify their occupation. Furthermore, if the user is a student, the providing unit can prioritize providing information related to their studies. For example, the providing unit can analyze the user's profile data to identify their occupation. Furthermore, if the user likes to travel, the providing unit can prioritize providing information related to travel. For example, the providing unit can analyze the user's profile data to identify their lifestyle. This allows the content to be customized based on the user's occupation and lifestyle, thereby providing more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's profile data into the generation AI and have the generation AI customize the content to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit 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 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit 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 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[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 system can further analyze the user's past browsing history and prioritize collecting related information. For example, the collection unit analyzes the patterns of websites the user has frequently visited in the past and prioritizes collecting URLs with similar patterns. The collection unit can also analyze the time period during which the user previously collected data and collect data during the same time period. The collection unit can also analyze the type of data the user previously collected and prioritize collecting data of the same type. This makes it possible to collect more relevant information by utilizing the user's past browsing history.

[0109] The analysis system can further estimate the user's emotions and adjust the type of data to be collected based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation and mental health. Also, if the user is excited, the collection unit can prioritize collecting the latest news and trend information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to entertainment and hobbies. By adjusting the type of data to be collected according to the user's emotions, more appropriate information can be provided.

[0110] The analysis system can further determine the priority of analysis based on the time of data collection. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of data collection. This allows for efficient analysis by determining the priority of analysis based on the time of data collection.

[0111] The analysis system can further adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented 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.

[0112] The analysis system can further estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide a concise analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0113] The analysis system can also apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a video analysis algorithm to video data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category.

[0114] The analysis system can further estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0115] The analysis system can further improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly and applies a similar analysis method. The analysis unit can also avoid analysis results that the user has previously rated poorly. Furthermore, the analysis unit can analyze the user's past analysis results and suggest the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0116] The analysis system can further estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the latest news and trend information. If the user is relaxed, the analysis unit can also prioritize analyzing data related to entertainment and hobbies. If the user is stressed, the analysis unit can also prioritize analyzing data related to relaxation and mental health. In this way, by determining the priority of analysis according to the user's emotions, more appropriate information can be provided.

[0117] The analysis system can further adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit can perform a detailed analysis if the user has specialized knowledge. Alternatively, the analysis unit can perform a brief analysis if the user does not have specialized knowledge. Furthermore, the analysis unit can determine the priority of the analysis according to the user's level of expertise. This allows the system to provide more appropriate analysis results by adjusting the level of detail of the analysis according to the user's level of expertise.

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

[0119] Step 1: The collector collects the URL content, including web page content, metadata, images, videos, etc. The collector uses scraping techniques and APIs to obtain the data and removes unnecessary elements such as advertisements. Step 2: The analysis unit analyzes the data collected by the collection unit and converts it into a vector format. Vector formats include TF-IDF and Word2Vec. The analysis unit converts text data, image data, and video data into appropriate vector formats. Step 3: The generator generates a summary based on the analysis results obtained by the analyzer. The generator uses summarization algorithms and importance scoring to generate a summary. The generator summarizes the content of news articles, academic papers, and videos. Step 4: The provider provides the summary generated by the generator. Methods of provisioning include a web interface, an API, email notification, etc. The provider displays the summary to the user, provides the summary to other systems, and sends the summary via email notification.

[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 above example, 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 above example, 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 above example, and various modifications are possible.

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

2. The collecting unit Collects URL content and removes ads and other unwanted content 2. The system of claim 1.

3. The analysis unit Convert the collected data into vector format 2. The system of claim 1.

4. The generation unit Generate a summary based on the analysis results 2. The system of claim 1.

5. The providing unit Providing a generated summary 2. The system of claim 1.

6. The generation unit Summarize news articles, academic papers, and videos in text 2. The system of claim 1.

7. The collecting unit Estimate user sentiment and adjust URL collection timing based on the estimated user sentiment.

2. The system of claim 1.

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

9. The collecting unit When collecting URLs, filter them based on the user's current interests.

2. The system of claim 1.

10. The collecting unit When collecting URLs, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

Images