System

Abstract

To provide a system that efficiently collects new information from a plurality of information sources and provides collected information after summarizing.SOLUTION: A system according to an embodiment comprises a collection unit, a summarization unit, and a provision unit. The collection unit collects new information from a plurality of information sources. The summarization unit summarizes the new information collected by the collection unit. The provision unit provides the new information summarized by the summarization 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not been sufficient in efficiently collecting new information from multiple information sources and summarizing and providing it, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect new information from multiple information sources and provide the information in a summarized form. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, a summarization unit, and a provision unit. The collection unit collects new information from a plurality of information sources. The summarization unit summarizes the new information collected by the collection unit. The provision unit provides the new information summarized by the summarization unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect new information from multiple information sources and provide the information in a summarized form. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information collection system according to the embodiment of the present invention is a system for journalists to efficiently collect new information and reflect it in articles. This information collection system collects new information from multiple information sources, summarizes the collected new information, and provides summarized information. For example, the information collection system uses crawling and scraping technology to monitor information sources such as social media and blogs. It is possible to collect information related to specific keywords and topics. It also has a function of detecting new information sources, and can collect new information from the detected information sources. Next, it summarizes the collected new information. It uses AI to analyze the collected information in real time and automatically summarize it. For example, it is possible to summarize long articles and multiple pieces of information in a short time and extract important points. The summarized information is provided to the journalist through the providing unit. The providing unit also has a function of creating the summarized information as a draft of an article, and the journalist can create an article based on the draft. It also has a function of evaluating the reliability of the collected information, and can provide summarized information together with the evaluation result. This system allows journalists to efficiently collect new information and quickly reflect it in articles. For example, it is possible to quickly provide news by collecting information related to a specific keyword or topic and creating an article based on the summarized information. In addition, by evaluating the reliability of information, accurate information can be provided. This allows the information gathering system to help journalists efficiently gather new information and incorporate it into their articles.

[0029] The information collection system according to the embodiment includes a collection unit, a summarization unit, and a provision unit. The collection unit collects new information from a plurality of information sources. The information sources include, for example, news sites, SNS, blogs, etc., but are not limited to such examples. The collection unit collects information related to a specific keyword or topic, for example, using a crawling technique. The collection unit can also use a scraping technique to monitor information sources such as social media and blogs and collect new information. Furthermore, the collection unit has a function of detecting new information sources, and can collect new information from the detected information sources. The summarization unit summarizes the new information collected by the collection unit. The summarization is performed in real time, for example, using AI. The summarization unit can summarize long articles and multiple pieces of information in a short time and extract important points. For example, the summarization unit uses a text generation AI (for example, LLM) to briefly summarize the collected information. The summarization unit can also summarize the collected information using a multimodal generation AI. The provision unit provides the new information summarized by the summarization unit. The providing unit has a function of creating the summarized information as a draft of an article, and the journalist can create an article based on the draft. For example, the providing unit provides the summarized information in a text format. The providing unit can also provide the summarized information in a graphical format. Furthermore, the providing unit has a function of evaluating the reliability of the collected information, and can provide the summarized information together with the evaluation result. In this way, the information collection system according to the embodiment allows the journalist to efficiently collect new information and reflect it in the article.

[0030] The collection unit collects new information from a plurality of information sources. The information sources include, but are not limited to, news sites, SNS, blogs, and the like. The collection unit, for example, uses crawling technology to collect information related to a specific keyword or topic. The crawling technology automatically crawls through web pages and extracts information that matches a specified condition. For example, the latest news articles are periodically checked on a specific news site, and articles containing a specified keyword are collected. The collection unit can also use scraping technology to monitor information sources such as social media and blogs to collect new information. The scraping technology analyzes the structure of a web page and extracts necessary data. For example, the content posted on a specific SNS account is periodically checked to collect related information. Furthermore, the collection unit has a function to detect new information sources, and can collect new information from the detected information sources. This function, for example, uses AI to automatically discover new information sources and add them to the collection target. AI can learn the patterns of existing information sources and identify similar new information sources. This allows the collection unit to keep collecting the latest information at all times. The collection unit centrally manages the collected information and stores it in a database. The database is designed to allow the collected information to be searched and referenced efficiently, and can be linked to other systems and departments as necessary. For example, the collected information can be made accessible to the summary department and the provision department. In addition, the collection department can flexibly set the collection frequency and collection target, so that it can respond to specific situations and conditions. This allows the collection department to collect information efficiently and effectively, improving the performance of the entire system.

[0031] The summarization unit summarizes new information collected by the collection unit. The summarization is performed in real time, for example, using AI. The summarization unit can summarize long articles and multiple pieces of information in a short time and extract important points. Specifically, the summarization unit uses a text generation AI (for example, LLM) to concisely summarize the collected information. The text generation AI has the ability to learn large amounts of text data and generate natural sentences. For example, when summarizing a news article, the AI ​​extracts the main points of the article and generates a concise summary sentence. The summarization unit can also summarize the collected information using a multimodal generation AI. The multimodal generation AI has the ability to integrate and process not only text but also different forms of data such as images and audio. For example, it can analyze images and videos related to a news article and generate a summary including their contents. The summarization unit stores the generated summary in a database and makes it accessible to the provision unit. Furthermore, the summarization unit has a function to evaluate the accuracy and quality of the summary, and can improve the summarization algorithm based on the evaluation results. For example, to evaluate the accuracy of the summary, reviews by experts are conducted and feedback is collected. The summarization unit can also adjust the content of the summary based on user feedback to provide a summary that better meets the needs of the user, thereby enabling the summarization unit to efficiently and accurately summarize information and improve the performance of the entire system.

[0032] The providing unit provides new information summarized by the summarizing unit. The providing unit has a function of creating a draft of an article from the summarized information, and a journalist can create an article based on the draft. Specifically, the providing unit provides the summarized information in a text format. The draft in text format is designed so that a journalist can easily edit and revise it, making article writing more efficient. The providing unit can also provide the summarized information in a graphical format. The draft in graphical format includes graphs and charts to visually convey information, and is useful for creating articles that are easy for readers to understand. Furthermore, the providing unit has a function of evaluating the reliability of collected information, and can provide summarized information together with the evaluation results. The reliability evaluation is performed, for example, based on the reliability of the source and the consistency of the information. The reliability of information can be automatically evaluated using AI, and highly reliable information can be provided preferentially. The providing unit stores the summarized information in a database and makes it accessible to journalists. The providing unit also provides a function of viewing and searching the summarized information through a user interface. For example, a journalist can search for summary information related to a specific keyword or topic and quickly obtain the required information. This allows the information provider to enable journalists to efficiently collect new information and incorporate it into their articles. The information provider can also collect user feedback and continually improve the accuracy and quality of the information provided. This allows the information provider to always provide the latest and most reliable information, improving the performance of the entire system.

[0033] The information collection system includes a detection unit that detects a new information source, and the collection unit can collect new information from the new information source detected by the detection unit. The detection unit detects the new information source. The new information source includes, for example, a newly added news site, a newly discovered blog, and the like, but is not limited to such examples. The detection unit can detect the new information source, for example, by using AI. For example, the detection unit automatically detects a new information source related to a specific keyword or topic. The collection unit collects new information from the new information source detected by the detection unit. For example, the collection unit collects the latest news from the new news site detected by the detection unit. In addition, the collection unit can also collect trend information from the new blog detected by the detection unit. This makes it possible to efficiently detect new information sources and expand the range of information collection. A part or all of the above-mentioned processing in the detection unit may be performed, for example, by using AI, or may be performed without using AI. For example, the detection unit can cause AI to execute the detection of the new information source.

[0034] The information collection system includes a reception unit that receives an input of a specific keyword or a specific topic, and the collection unit can collect new information related to the specific keyword or the specific topic received by the reception unit. The reception unit receives an input of a specific keyword or a specific topic. The specific keyword includes, for example, a word or phrase related to the specific topic, but is not limited to such an example. The specific topic includes, for example, categories such as politics, economy, and sports, but is not limited to such an example. The reception unit can receive, for example, a specific keyword or topic input by a user. The collection unit collects new information related to the specific keyword or the specific topic received by the reception unit. For example, the collection unit collects news articles related to the specific keyword input by the user. The collection unit can also collect blog articles related to the specific topic input by the user. This makes it possible to collect information based on the user's interests. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can cause AI to receive the keywords and topics input by the user.

[0035] The information collection system includes an identification unit that identifies a specific keyword or a specific topic based on the history of articles created by the user, and the collection unit can collect new information related to the specific keyword or the specific topic identified by the identification unit. The identification unit identifies a specific keyword or a specific topic based on the history of articles created by the user. The article history includes, for example, a database of articles created in the past, but is not limited to such an example. The identification unit can, for example, analyze the contents of articles created in the past by the user and identify a specific keyword or topic. The collection unit collects new information related to the specific keyword or the specific topic identified by the identification unit. For example, the collection unit collects the latest news related to the keyword identified by the identification unit. The collection unit can also collect blog articles related to the topic identified by the identification unit. This makes it possible to collect information based on the user's past activities. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can have AI analyze the history of articles created by the user to identify a specific keyword or topic.

[0036] The information collection system includes a creation unit that creates a draft of an article based on new information summarized by the summarizing unit, and the providing unit can provide the draft of the article created by the creation unit. The creation unit creates a draft of the article based on new information summarized by the summarizing unit. The draft of the article includes, for example, components such as a headline, a body text, and a quote, but is not limited to such examples. The creation unit can create a draft of the article based on the summarized information, for example. The providing unit provides the draft of the article created by the creation unit. For example, the providing unit provides the draft of the article in a text format. The providing unit can also provide the draft of the article in a graphical format. This makes it possible to create and provide a draft of the article based on the summarized information. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the summarized information to AI and cause the AI ​​to create a draft of the article.

[0037] The information collection system includes an evaluation unit that evaluates the reliability of new information collected by the collection unit, and the provision unit can provide the new information summarized by the summarization unit together with the evaluation result by the evaluation unit. The evaluation unit evaluates the reliability of the new information collected by the collection unit. Reliability includes, for example, the reliability of the information source and the accuracy of the information, but is not limited to such examples. The evaluation unit can evaluate the reliability of the information, for example, using AI. For example, the evaluation unit automatically evaluates the reliability of the collected information and generates an evaluation result. The provision unit provides the new information summarized by the summarization unit together with the evaluation result by the evaluation unit. For example, the provision unit provides the reliability evaluation result together with the summarized information in a text format. The provision unit can also provide the reliability evaluation result together with the summarized information in a graphical format. This makes it possible to evaluate the reliability of the information and provide it together with the evaluation result. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can cause AI to perform the reliability evaluation of the collected information.

[0038] In the information collection system, the collection unit can analyze the past collection history and select the optimal collection method. The collection unit analyzes the past collection history. The collection history includes, for example, a pattern of information collected in the past, a collection frequency, a collection time period, and the like, but is not limited to such examples. The collection unit can analyze the past collection history, for example, using AI. For example, the collection unit analyzes the pattern of information collected in the past and selects the most effective collection method. The collection unit can also select a collection method that is effective in a specific time period from the past collection history. In addition, the collection unit can adjust the collection frequency from a specific information source based on the past collection history. This makes it possible to select the optimal collection method based on the past collection history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0039] In the information collection system, when the collection unit collects information, the collection unit can perform filtering based on the user's current field of interest. The field of interest includes, for example, the user's past search history, browsing history, etc., but is not limited to such examples. The collection unit can, for example, use AI to identify the user's field of interest and perform filtering when collecting information. For example, the collection unit collects only information related to topics in which the user is currently interested. The collection unit can also filter information with low relevance based on the user's field of interest. Furthermore, the collection unit can preferentially collect information containing a specific keyword based on the user's field of interest. This makes it possible to filter information based on the user's field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest to the generation AI and cause the generation AI to filter the information.

[0040] In the information collection system, when the collection unit collects information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The collection unit can, for example, use AI to identify the geographical location information of the user and preferentially collect highly relevant information when collecting information. For example, the collection unit preferentially collects information related to the area where the user is currently located. In addition, the collection unit can also filter information related to a specific area based on the geographical location information of the user. Furthermore, the collection unit can also preferentially collect local news and event information based on the geographical location information of the user. This makes it possible to collect highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0041] In the information collection system, the collection unit can analyze the social media activity of the user and collect related information when collecting information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit can, for example, use AI to analyze the social media activity of the user and collect related information when collecting information. For example, the collection unit preferentially collects information from accounts that the user follows on social media. The collection unit can also analyze the content of posts on the user's social media and collect related information. Furthermore, the collection unit can also collect information related to topics of interest based on the user's social media activity history. This makes it possible to collect related information based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute collection of related information.

[0042] In the information collection system, when the summarization unit generates a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. The importance of the information includes, but is not limited to, the influence and relevance of the information. The summarization unit can, for example, use AI to evaluate the importance of the information and adjust the level of detail of the summary. For example, the summarization unit can provide a detailed summary for information with high importance. In addition, the summarization unit can also provide a concise summary for information with low importance. Furthermore, the summarization unit can adjust the length of the summary according to the importance of the information. This makes it possible to adjust the level of detail of the summary according to the importance of the information. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can input the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0043] In the information collection system, the summarization unit can apply different summarization algorithms depending on the category of information when generating a summary. The categories of information include, but are not limited to, news, entertainment, and sports. The summarization unit can, for example, use AI to identify the category of information and apply different summarization algorithms when generating a summary. For example, the summarization unit can apply an algorithm that extracts important points in the case of a news article. In addition, the summarization unit can apply an algorithm that provides an overview of the content in the case of a blog article. Furthermore, the summarization unit can apply an algorithm that extracts major topics in the case of a social media post. This makes it possible to apply an optimal summarization algorithm depending on the category of information. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the category of information to the generation AI and cause the generation AI to apply the summarization algorithm.

[0044] In the information collection system, the summarization unit can determine the priority of summaries based on the time when the information was collected when generating summaries. The time when the information was collected includes, but is not limited to, the latest information and past information. The summarization unit can, for example, use AI to identify the time when the information was collected and determine the priority of summaries when generating summaries. For example, the summarization unit prioritizes summarization of the latest information. In addition, the summarization unit can also lower the priority of summaries for old information. Furthermore, the summarization unit can adjust the order of summaries according to the time when the information was collected. This makes it possible to determine the priority of summaries according to the time when the information was collected. A part or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the time when the information was collected to the generation AI and cause the generation AI to execute the determination of the priority of summaries.

[0045] In the information collection system, the summarization unit can adjust the order of summaries based on the relevance of information when generating summaries. The relevance of information includes, but is not limited to, for example, the degree of matching of topics and the degree of co-occurrence of keywords. The summarization unit can, for example, use AI to evaluate the relevance of information and adjust the order of summaries when generating summaries. For example, the summarization unit prioritizes summarization of highly relevant information. In addition, the summarization unit can postpone the order of summarization for information with low relevance. Furthermore, the summarization unit can adjust the order of summaries according to the relevance of information. This makes it possible to adjust the order of summaries according to the relevance of information. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can input the relevance of information to the generation AI and cause the generation AI to adjust the order of summaries.

[0046] In the information collection system, the providing unit can adjust the level of detail of the information provided based on the importance of the information when the information is provided. The importance of the information includes, for example, the depth and specificity of the information, but is not limited to such examples. The providing unit can, for example, use AI to evaluate the importance of the information and adjust the level of detail of the information provided. For example, the providing unit can provide a detailed explanation for information with high importance. In addition, the providing unit can also provide a concise explanation for information with low importance. Furthermore, the providing unit can adjust the level of detail of the information provided according to the importance of the information. This makes it possible to adjust the level of detail of the information provided according to the importance of the information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0047] In the information collection system, the providing unit can apply different providing methods depending on the category of information when providing the information. The categories of information include, for example, news, entertainment, sports, etc., but are not limited to such examples. The providing unit can, for example, use AI to identify the category of information and apply different providing methods when providing the information. For example, in the case of a news article, the providing unit can provide the information in a format that focuses on the main points. In addition, in the case of a blog article, the providing unit can also provide the information in a format that provides an outline of the content. Furthermore, in the case of a social media post, the providing unit can also provide the information in a format in which the main topics are extracted. This makes it possible to apply the optimal providing method depending on the category of information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of information to the generating AI and cause the generating AI to apply the providing method.

[0048] In the information collection system, the providing unit can determine the priority of provision based on the collection time of the information at the time of provision. The collection time of the information includes, for example, the latest information, past information, and the like, but is not limited to such examples. The providing unit can, for example, use AI to identify the collection time of the information and determine the priority of provision at the time of provision. For example, the providing unit provides the latest information preferentially. In addition, the providing unit can also lower the priority of provision for old information. Furthermore, the providing unit can adjust the order of provision according to the collection time of the information. This makes it possible to determine the priority of provision according to the collection time of the information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collection time of the information to the generating AI and cause the generating AI to execute the determination of the priority of provision.

[0049] In the information collection system, the providing unit can adjust the order of provision based on the relevance of information when providing the information. The relevance of information includes, for example, the degree of matching of topics, the degree of co-occurrence of keywords, and the like, but is not limited to such examples. The providing unit can, for example, use AI to evaluate the relevance of information and adjust the order of provision when providing the information. For example, the providing unit provides highly relevant information preferentially. In addition, the providing unit can also postpone the order of provision of information with low relevance. Furthermore, the providing unit can also adjust the order of provision according to the relevance of information. This makes it possible to adjust the order of provision according to the relevance of information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information to the generating AI and cause the generating AI to adjust the order of provision.

[0050] In the information collection system, when the detection unit detects, the detection unit can analyze the past detection history and select the optimal detection method. The detection history includes, for example, a pattern of information sources detected in the past, a detection frequency, a detection time period, and the like, but is not limited to such examples. The detection unit can analyze the past detection history, for example, using AI. For example, the detection unit analyzes a pattern of information sources detected in the past and selects the most effective detection method. The detection unit can also select a detection method that is effective in a specific time period from the past detection history. In addition, the detection unit can adjust the detection frequency from a specific information source based on the past detection history. This makes it possible to select the optimal detection method based on the past detection history. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the past detection history to the generation AI and cause the generation AI to select the optimal detection method.

[0051] In the information collection system, the detection unit can preferentially detect highly relevant information sources in consideration of the geographical location information of the user when the detection unit detects. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The detection unit can, for example, use AI to identify the geographical location information of the user and preferentially detect highly relevant information sources when the detection unit detects. For example, the detection unit preferentially detects information sources related to the area where the user is currently located. The detection unit can also filter information sources related to a specific area based on the geographical location information of the user. Furthermore, the detection unit can also preferentially detect local news and event information sources based on the geographical location information of the user. This makes it possible to detect highly relevant information sources based on the geographical location information of the user. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the geographical location information of the user to the generation AI and cause the generation AI to detect highly relevant information sources.

[0052] In the information collection system, the reception unit can select the optimal reception method by analyzing the past reception history when receiving a call. The reception history includes, for example, keywords and topics input in the past, input methods, reception time periods, etc., but is not limited to such examples. The reception unit can analyze the past reception history using, for example, AI. For example, the reception unit automatically displays keywords and topics that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that have been used in the past. Furthermore, the reception unit can predict and suggest keywords and topics that will be used in a specific time period from the past reception history. This makes it possible to select the optimal reception method based on the past reception history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past reception history to the generation AI and cause the generation AI to select the optimal reception method.

[0053] In the information collection system, the reception unit can preferentially receive highly relevant keywords and topics in consideration of the geographical location information of the user when receiving the information. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The reception unit can, for example, use AI to identify the geographical location information of the user and preferentially receive highly relevant keywords and topics when receiving the information. For example, the reception unit preferentially receives keywords and topics related to the area where the user is currently located. The reception unit can also filter keywords and topics related to a specific area based on the geographical location information of the user. Furthermore, the reception unit can also preferentially receive keywords and topics related to local news and events based on the geographical location information of the user. This makes it possible to receive highly relevant keywords and topics based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the reception of highly relevant keywords and topics.

[0054] In the information collection system, the identification unit can select an optimal identification method by analyzing the past identification history at the time of identification. The identification history includes, for example, keywords and topics identified in the past, identification methods, and identification time periods, but is not limited to such examples. The identification unit can analyze the past identification history using, for example, AI. For example, the identification unit analyzes the patterns of keywords and topics identified in the past and selects the most effective identification method. The identification unit can also select an effective identification method for a specific time period from the past identification history. Furthermore, the identification unit can adjust the identification frequency of a specific keyword or topic based on the past identification history. This makes it possible to select an optimal identification method based on the past identification history. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the past identification history to the generation AI and cause the generation AI to select the optimal identification method.

[0055] In the information collection system, the identification unit can preferentially identify highly relevant keywords and topics in consideration of the geographical location information of the user at the time of identification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The identification unit can, for example, use AI to identify the geographical location information of the user and preferentially identify highly relevant keywords and topics at the time of identification. For example, the identification unit preferentially identifies keywords and topics related to the area where the user is currently located. The identification unit can also filter keywords and topics related to a specific area based on the geographical location information of the user. Furthermore, the identification unit can also preferentially identify keywords and topics related to local news and events based on the geographical location information of the user. This makes it possible to identify highly relevant keywords and topics based on the geographical location information of the user. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the identification of highly relevant keywords and topics.

[0056] In the information collection system, the creation unit can select the optimal creation method by analyzing the past creation history at the time of creation. The creation history includes, for example, patterns of articles created in the past, creation frequency, creation time period, etc., but is not limited to such examples. The creation unit can analyze the past creation history, for example, using AI. For example, the creation unit analyzes the patterns of articles created in the past and selects the most effective creation method. The creation unit can also select an effective creation method for a specific time period from the past creation history. Furthermore, the creation unit can adjust the creation frequency of drafts related to a specific topic based on the past creation history. This makes it possible to select the optimal creation method based on the past creation history. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the past creation history to the generation AI and cause the generation AI to select the optimal creation method.

[0057] In the information collection system, the creation unit can preferentially create a draft of a highly relevant article in consideration of the geographical location information of the user at the time of creation. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The creation unit can, for example, use AI to identify the geographical location information of the user and preferentially create a draft of a highly relevant article at the time of creation. For example, the creation unit preferentially creates a draft of an article related to the area where the user is currently located. The creation unit can also filter drafts of articles related to a specific area based on the geographical location information of the user. Furthermore, the creation unit can also preferentially create drafts of articles related to local news and events based on the geographical location information of the user. This makes it possible to create a draft of a highly relevant article based on the geographical location information of the user. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the creation of a draft of a highly relevant article.

[0058] In the information collection system, the evaluation unit can select the optimal evaluation method by analyzing the past evaluation history at the time of evaluation. The evaluation history includes, for example, a pattern of information evaluated in the past, an evaluation frequency, an evaluation time period, and the like, but is not limited to such examples. The evaluation unit can analyze the past evaluation history, for example, using AI. For example, the evaluation unit analyzes a pattern of information evaluated in the past and selects the most effective evaluation method. The evaluation unit can also select an evaluation method that is effective in a specific time period from the past evaluation history. Furthermore, the evaluation unit can adjust the evaluation frequency from a specific information source based on the past evaluation history. This makes it possible to select the optimal evaluation method based on the past evaluation history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation history to the generation AI and cause the generation AI to select the optimal evaluation method.

[0059] In the information collection system, the evaluation unit can prioritize highly relevant information in consideration of the geographical location information of the user at the time of evaluation. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The evaluation unit can, for example, use AI to identify the geographical location information of the user and prioritize highly relevant information at the time of evaluation. For example, the evaluation unit prioritizes the reliability of information related to the area where the user is currently located. The evaluation unit can also filter the reliability of information related to a specific area based on the geographical location information of the user. Furthermore, the evaluation unit can also prioritize the reliability of information related to local news and events based on the geographical location information of the user. This makes it possible to evaluate highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the evaluation of highly relevant information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] In the information collection system, the collection unit can analyze the user's past search history and preferentially collect new related information. For example, the collection unit collects related latest news articles based on keywords and topics searched for by the user in the past. The collection unit can also preferentially collect information from a specific information source based on the user's past search history. Furthermore, the collection unit can analyze the user's past search history and collect related blog articles and social media posts. This makes it possible to efficiently collect more relevant information based on the user's past search history.

[0062] In the information collection system, when the collection unit collects information, the collection unit can adjust the information collection method taking into account the usage status of the user's device. For example, when the user is using a smartphone, the collection unit can collect information optimized for mobile devices. In addition, when the user is using a personal computer, the collection unit can also collect detailed information. Furthermore, when the user is using a tablet, the collection unit can also collect information that is visually easy to view. This makes it possible to adjust the information collection method according to the usage status of the user's device.

[0063] In the information collection system, when the collection unit collects information, the collection unit can analyze the user's past browsing history and prioritize collection of new related information. For example, the collection unit collects the latest related information based on news articles and blog articles that the user has viewed in the past. The collection unit can also prioritize collection of information from a specific information source based on the user's past browsing history. Furthermore, the collection unit can analyze the user's past browsing history and collect related social media posts. This makes it possible to efficiently collect more relevant information based on the user's past browsing history.

[0064] In the information collection system, when the collection unit collects information, the collection unit can adjust the method of collecting information taking into consideration the number of followers of the user on social media. For example, when the user has many followers, the collection unit can preferentially collect influential information. In addition, when the user has few followers, the collection unit can also collect information based on personal interests. Furthermore, the collection unit can preferentially collect information from a specific source based on the number of followers of the user. This makes it possible to adjust the method of collecting information according to the number of followers of the user on social media.

[0065] In the information collection system, when the collection unit collects information, the collection unit can analyze the user's past subscription history and prioritize collection of new related information. For example, the collection unit collects the latest related information based on newsletters and e-mail magazines to which the user has previously subscribed. The collection unit can also prioritize collection of information from a specific information source based on the user's past subscription history. Furthermore, the collection unit can analyze the user's past subscription history and collect related blog articles and social media posts. This makes it possible to efficiently collect more relevant information based on the user's past subscription history.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The collection unit collects new information from multiple sources. Sources include news sites, social media, blogs, etc. The collection unit uses crawling and scraping technologies to collect information related to specific keywords and topics. The collection unit can also detect new sources and collect new information from them. Step 2: The summarization unit summarizes the new information collected by the collection unit. Summarization is done in real time using AI to quickly summarize long articles and multiple pieces of information and extract important points. The summarization unit uses text generation AI and multimodal generation AI to concisely summarize the collected information. Step 3: The presentation unit provides new information summarized by the summarization unit. The presentation unit has a function of creating a draft of an article from the summarized information, so that a journalist can create an article based on the draft. The presentation unit provides the summarized information in text and graphical formats. The presentation unit can also evaluate the reliability of the collected information and provide the summarized information along with the evaluation results.

[0068] (Example 2) The information collection system according to the embodiment of the present invention is a system for journalists to efficiently collect new information and reflect it in articles. This information collection system collects new information from multiple information sources, summarizes the collected new information, and provides summarized information. For example, the information collection system uses crawling and scraping technology to monitor information sources such as social media and blogs. It is possible to collect information related to specific keywords and topics. It also has a function of detecting new information sources, and can collect new information from the detected information sources. Next, it summarizes the collected new information. It uses AI to analyze the collected information in real time and automatically summarize it. For example, it is possible to summarize long articles and multiple pieces of information in a short time and extract important points. The summarized information is provided to the journalist through the providing unit. The providing unit also has a function of creating the summarized information as a draft of an article, and the journalist can create an article based on the draft. It also has a function of evaluating the reliability of the collected information, and can provide summarized information together with the evaluation result. This system allows journalists to efficiently collect new information and quickly reflect it in articles. For example, it is possible to quickly provide news by collecting information related to a specific keyword or topic and creating an article based on the summarized information. In addition, by evaluating the reliability of information, accurate information can be provided. This allows the information gathering system to help journalists efficiently gather new information and incorporate it into their articles.

[0069] The information collection system according to the embodiment includes a collection unit, a summarization unit, and a provision unit. The collection unit collects new information from a plurality of information sources. The information sources include, for example, news sites, SNS, blogs, etc., but are not limited to such examples. The collection unit collects information related to a specific keyword or topic, for example, using a crawling technique. The collection unit can also use a scraping technique to monitor information sources such as social media and blogs and collect new information. Furthermore, the collection unit has a function of detecting new information sources, and can collect new information from the detected information sources. The summarization unit summarizes the new information collected by the collection unit. The summarization is performed in real time, for example, using AI. The summarization unit can summarize long articles and multiple pieces of information in a short time and extract important points. For example, the summarization unit uses a text generation AI (for example, LLM) to briefly summarize the collected information. The summarization unit can also summarize the collected information using a multimodal generation AI. The provision unit provides the new information summarized by the summarization unit. The providing unit has a function of creating the summarized information as a draft of an article, and the journalist can create an article based on the draft. For example, the providing unit provides the summarized information in a text format. The providing unit can also provide the summarized information in a graphical format. Furthermore, the providing unit has a function of evaluating the reliability of the collected information, and can provide the summarized information together with the evaluation result. In this way, the information collection system according to the embodiment allows the journalist to efficiently collect new information and reflect it in the article.

[0070] The collection unit collects new information from a plurality of information sources. The information sources include, but are not limited to, news sites, SNS, blogs, and the like. The collection unit, for example, uses crawling technology to collect information related to a specific keyword or topic. The crawling technology automatically crawls through web pages and extracts information that matches a specified condition. For example, the latest news articles are periodically checked on a specific news site, and articles containing a specified keyword are collected. The collection unit can also use scraping technology to monitor information sources such as social media and blogs to collect new information. The scraping technology analyzes the structure of a web page and extracts necessary data. For example, the content posted on a specific SNS account is periodically checked to collect related information. Furthermore, the collection unit has a function to detect new information sources, and can collect new information from the detected information sources. This function, for example, uses AI to automatically discover new information sources and add them to the collection target. AI can learn the patterns of existing information sources and identify similar new information sources. This allows the collection unit to keep collecting the latest information at all times. The collection unit centrally manages the collected information and stores it in a database. The database is designed to allow the collected information to be searched and referenced efficiently, and can be linked to other systems and departments as necessary. For example, the collected information can be made accessible to the summary department and the provision department. In addition, the collection department can flexibly set the collection frequency and collection target, so that it can respond to specific situations and conditions. This allows the collection department to collect information efficiently and effectively, improving the performance of the entire system.

[0071] The summarization unit summarizes new information collected by the collection unit. The summarization is performed in real time, for example, using AI. The summarization unit can summarize long articles and multiple pieces of information in a short time and extract important points. Specifically, the summarization unit uses a text generation AI (for example, LLM) to concisely summarize the collected information. The text generation AI has the ability to learn large amounts of text data and generate natural sentences. For example, when summarizing a news article, the AI ​​extracts the main points of the article and generates a concise summary sentence. The summarization unit can also summarize the collected information using a multimodal generation AI. The multimodal generation AI has the ability to integrate and process not only text but also different forms of data such as images and audio. For example, it can analyze images and videos related to a news article and generate a summary including their contents. The summarization unit stores the generated summary in a database and makes it accessible to the provision unit. Furthermore, the summarization unit has a function to evaluate the accuracy and quality of the summary, and can improve the summarization algorithm based on the evaluation results. For example, to evaluate the accuracy of the summary, reviews by experts are conducted and feedback is collected. The summarization unit can also adjust the content of the summary based on user feedback to provide a summary that better meets the needs of the user, thereby enabling the summarization unit to efficiently and accurately summarize information and improve the performance of the entire system.

[0072] The providing unit provides new information summarized by the summarizing unit. The providing unit has a function of creating a draft of an article from the summarized information, and a journalist can create an article based on the draft. Specifically, the providing unit provides the summarized information in a text format. The draft in text format is designed so that a journalist can easily edit and revise it, making article writing more efficient. The providing unit can also provide the summarized information in a graphical format. The draft in graphical format includes graphs and charts to visually convey information, and is useful for creating articles that are easy for readers to understand. Furthermore, the providing unit has a function of evaluating the reliability of collected information, and can provide summarized information together with the evaluation results. The reliability evaluation is performed, for example, based on the reliability of the source and the consistency of the information. The reliability of information can be automatically evaluated using AI, and highly reliable information can be provided preferentially. The providing unit stores the summarized information in a database and makes it accessible to journalists. The providing unit also provides a function of viewing and searching the summarized information through a user interface. For example, a journalist can search for summary information related to a specific keyword or topic and quickly obtain the required information. This allows the information provider to enable journalists to efficiently collect new information and incorporate it into their articles. The information provider can also collect user feedback and continually improve the accuracy and quality of the information provided. This allows the information provider to always provide the latest and most reliable information, improving the performance of the entire system.

[0073] The information collection system includes a detection unit that detects a new information source, and the collection unit can collect new information from the new information source detected by the detection unit. The detection unit detects the new information source. The new information source includes, for example, a newly added news site, a newly discovered blog, and the like, but is not limited to such examples. The detection unit can detect the new information source, for example, by using AI. For example, the detection unit automatically detects a new information source related to a specific keyword or topic. The collection unit collects new information from the new information source detected by the detection unit. For example, the collection unit collects the latest news from the new news site detected by the detection unit. In addition, the collection unit can also collect trend information from the new blog detected by the detection unit. This makes it possible to efficiently detect new information sources and expand the range of information collection. A part or all of the above-mentioned processing in the detection unit may be performed, for example, by using AI, or may be performed without using AI. For example, the detection unit can cause AI to execute the detection of the new information source.

[0074] The information collection system includes a reception unit that receives an input of a specific keyword or a specific topic, and the collection unit can collect new information related to the specific keyword or the specific topic received by the reception unit. The reception unit receives an input of a specific keyword or a specific topic. The specific keyword includes, for example, a word or phrase related to the specific topic, but is not limited to such an example. The specific topic includes, for example, categories such as politics, economy, and sports, but is not limited to such an example. The reception unit can receive, for example, a specific keyword or topic input by a user. The collection unit collects new information related to the specific keyword or the specific topic received by the reception unit. For example, the collection unit collects news articles related to the specific keyword input by the user. The collection unit can also collect blog articles related to the specific topic input by the user. This makes it possible to collect information based on the user's interests. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can cause AI to receive the keywords and topics input by the user.

[0075] The information collection system includes an identification unit that identifies a specific keyword or a specific topic based on the history of articles created by the user, and the collection unit can collect new information related to the specific keyword or the specific topic identified by the identification unit. The identification unit identifies a specific keyword or a specific topic based on the history of articles created by the user. The article history includes, for example, a database of articles created in the past, but is not limited to such an example. The identification unit can, for example, analyze the contents of articles created in the past by the user and identify a specific keyword or topic. The collection unit collects new information related to the specific keyword or the specific topic identified by the identification unit. For example, the collection unit collects the latest news related to the keyword identified by the identification unit. The collection unit can also collect blog articles related to the topic identified by the identification unit. This makes it possible to collect information based on the user's past activities. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can have AI analyze the history of articles created by the user to identify a specific keyword or topic.

[0076] The information collection system includes a creation unit that creates a draft of an article based on new information summarized by the summarizing unit, and the providing unit can provide the draft of the article created by the creation unit. The creation unit creates a draft of the article based on new information summarized by the summarizing unit. The draft of the article includes, for example, components such as a headline, a body text, and a quote, but is not limited to such examples. The creation unit can create a draft of the article based on the summarized information, for example. The providing unit provides the draft of the article created by the creation unit. For example, the providing unit provides the draft of the article in a text format. The providing unit can also provide the draft of the article in a graphical format. This makes it possible to create and provide a draft of the article based on the summarized information. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the summarized information to AI and cause the AI ​​to create a draft of the article.

[0077] The information collection system includes an evaluation unit that evaluates the reliability of new information collected by the collection unit, and the provision unit can provide the new information summarized by the summarization unit together with the evaluation result by the evaluation unit. The evaluation unit evaluates the reliability of the new information collected by the collection unit. Reliability includes, for example, the reliability of the information source and the accuracy of the information, but is not limited to such examples. The evaluation unit can evaluate the reliability of the information, for example, using AI. For example, the evaluation unit automatically evaluates the reliability of the collected information and generates an evaluation result. The provision unit provides the new information summarized by the summarization unit together with the evaluation result by the evaluation unit. For example, the provision unit provides the reliability evaluation result together with the summarized information in a text format. The provision unit can also provide the reliability evaluation result together with the summarized information in a graphical format. This makes it possible to evaluate the reliability of the information and provide it together with the evaluation result. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can cause AI to perform the reliability evaluation of the collected information.

[0078] In the information collection system, the collection unit estimates the user's emotion, and the timing of information collection can be adjusted based on the estimated user's emotion. The collection unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The collection unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The collection unit adjusts the timing of information collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects only important information. In addition, when the user is relaxed, the collection unit can also increase the frequency of information collection and collect a wide range of information. Furthermore, when the user is in a hurry, the collection unit can also collect information in real time and provide it quickly. This makes it possible to adjust the timing of information collection according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0079] In the information collection system, the collection unit can analyze the past collection history and select the optimal collection method. The collection unit analyzes the past collection history. The collection history includes, for example, a pattern of information collected in the past, a collection frequency, a collection time period, and the like, but is not limited to such examples. The collection unit can analyze the past collection history, for example, using AI. For example, the collection unit analyzes the pattern of information collected in the past and selects the most effective collection method. The collection unit can also select a collection method that is effective in a specific time period from the past collection history. In addition, the collection unit can adjust the collection frequency from a specific information source based on the past collection history. This makes it possible to select the optimal collection method based on the past collection history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0080] In the information collection system, when the collection unit collects information, the collection unit can perform filtering based on the user's current field of interest. The field of interest includes, for example, the user's past search history, browsing history, etc., but is not limited to such examples. The collection unit can, for example, use AI to identify the user's field of interest and perform filtering when collecting information. For example, the collection unit collects only information related to topics in which the user is currently interested. The collection unit can also filter information with low relevance based on the user's field of interest. Furthermore, the collection unit can preferentially collect information containing a specific keyword based on the user's field of interest. This makes it possible to filter information based on the user's field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest to the generation AI and cause the generation AI to filter the information.

[0081] In the information collection system, the collection unit estimates the user's emotion and can determine the priority of information to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The collection unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The collection unit determines the priority of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects information of high importance. In addition, when the user is relaxed, the collection unit can also collect a wide range of information and lower the priority. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect information that can be collected quickly. This makes it possible to determine the priority of information according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input a user's emotional data into the generation AI and have the generation AI determine the priority of the information.

[0082] In the information collection system, when the collection unit collects information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The collection unit can, for example, use AI to identify the geographical location information of the user and preferentially collect highly relevant information when collecting information. For example, the collection unit preferentially collects information related to the area where the user is currently located. In addition, the collection unit can also filter information related to a specific area based on the geographical location information of the user. Furthermore, the collection unit can also preferentially collect local news and event information based on the geographical location information of the user. This makes it possible to collect highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0083] In the information collection system, the collection unit can analyze the social media activity of the user and collect related information when collecting information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit can, for example, use AI to analyze the social media activity of the user and collect related information when collecting information. For example, the collection unit preferentially collects information from accounts that the user follows on social media. The collection unit can also analyze the content of posts on the user's social media and collect related information. Furthermore, the collection unit can also collect information related to topics of interest based on the user's social media activity history. This makes it possible to collect related information based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute collection of related information.

[0084] In the information collection system, the summarization unit estimates the user's emotion and adjusts the expression method of the summary based on the estimated user's emotion. The summarization unit estimates the user's emotion. The user's emotion includes, but is not limited to, stress, relaxation, and hurry. The summarization unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The summarization unit adjusts the expression method of the summary based on the estimated user's emotion. For example, the summarization unit provides a concise summary that covers the main points when the user is stressed. In addition, the summarization unit can also provide a summary that includes detailed information when the user is relaxed. Furthermore, the summarization unit can also provide a summary that can be understood in a short time when the user is in a hurry. This makes it possible to adjust the expression method of the summary according to the user's emotion. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the summary is expressed.

[0085] In the information collection system, when the summarization unit generates a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. The importance of the information includes, but is not limited to, the influence and relevance of the information. The summarization unit can, for example, use AI to evaluate the importance of the information and adjust the level of detail of the summary. For example, the summarization unit can provide a detailed summary for information with high importance. In addition, the summarization unit can also provide a concise summary for information with low importance. Furthermore, the summarization unit can adjust the length of the summary according to the importance of the information. This makes it possible to adjust the level of detail of the summary according to the importance of the information. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can input the importance of the information to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0086] In the information collection system, the summarization unit can apply different summarization algorithms depending on the category of information when generating a summary. The categories of information include, but are not limited to, news, entertainment, and sports. The summarization unit can, for example, use AI to identify the category of information and apply different summarization algorithms when generating a summary. For example, the summarization unit can apply an algorithm that extracts important points in the case of a news article. In addition, the summarization unit can apply an algorithm that provides an overview of the content in the case of a blog article. Furthermore, the summarization unit can apply an algorithm that extracts major topics in the case of a social media post. This makes it possible to apply an optimal summarization algorithm depending on the category of information. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the category of information to the generation AI and cause the generation AI to apply the summarization algorithm.

[0087] In the information collection system, the summarization unit estimates the user's emotion and adjusts the length of the summary based on the estimated user's emotion. The summarization unit estimates the user's emotion. The user's emotion includes, but is not limited to, stress, relaxation, and hurry. The summarization unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to, for example. The summarization unit adjusts the length of the summary based on the estimated user's emotion. For example, the summarization unit provides a short summary that covers the main points when the user is stressed. In addition, the summarization unit can also provide a longer summary including detailed information when the user is relaxed. Furthermore, the summarization unit can also provide a summary that can be understood in a short time when the user is in a hurry. This makes it possible to adjust the length of the summary according to the user's emotion. A part or all of the above-mentioned processing in the summarization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarization unit can input user emotion data to the generation AI and have the generation AI adjust the length of the summary.

[0088] In the information collection system, the summarization unit can determine the priority of summaries based on the time when the information was collected when generating summaries. The time when the information was collected includes, but is not limited to, the latest information and past information. The summarization unit can, for example, use AI to identify the time when the information was collected and determine the priority of summaries when generating summaries. For example, the summarization unit prioritizes summarization of the latest information. In addition, the summarization unit can also lower the priority of summaries for old information. Furthermore, the summarization unit can adjust the order of summaries according to the time when the information was collected. This makes it possible to determine the priority of summaries according to the time when the information was collected. A part or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the time when the information was collected to the generation AI and cause the generation AI to execute the determination of the priority of summaries.

[0089] In the information collection system, the summarization unit can adjust the order of summaries based on the relevance of information when generating summaries. The relevance of information includes, but is not limited to, for example, the degree of matching of topics and the degree of co-occurrence of keywords. The summarization unit can, for example, use AI to evaluate the relevance of information and adjust the order of summaries when generating summaries. For example, the summarization unit prioritizes summarization of highly relevant information. In addition, the summarization unit can postpone the order of summarization for information with low relevance. Furthermore, the summarization unit can adjust the order of summaries according to the relevance of information. This makes it possible to adjust the order of summaries according to the relevance of information. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can input the relevance of information to the generation AI and cause the generation AI to adjust the order of summaries.

[0090] In the information collection system, the providing unit estimates the user's emotion and adjusts the format of the information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The providing unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit adjusts the format of the information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides information in a concise and visual format. In addition, when the user is relaxed, the providing unit can also provide information in a detailed text format. Furthermore, when the user is in a hurry, the providing unit can also provide information in a bullet-point format that covers the main points. This makes it possible to adjust the format of the information to be provided according to the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotional data to the generating AI and cause the generating AI to adjust the format in which information is provided.

[0091] In the information collection system, the providing unit can adjust the level of detail of the information provided based on the importance of the information when the information is provided. The importance of the information includes, for example, the depth and specificity of the information, but is not limited to such examples. The providing unit can, for example, use AI to evaluate the importance of the information and adjust the level of detail of the information provided. For example, the providing unit can provide a detailed explanation for information with high importance. In addition, the providing unit can also provide a concise explanation for information with low importance. Furthermore, the providing unit can adjust the level of detail of the information provided according to the importance of the information. This makes it possible to adjust the level of detail of the information provided according to the importance of the information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0092] In the information collection system, the providing unit can apply different providing methods depending on the category of information when providing the information. The categories of information include, for example, news, entertainment, sports, etc., but are not limited to such examples. The providing unit can, for example, use AI to identify the category of information and apply different providing methods when providing the information. For example, in the case of a news article, the providing unit can provide the information in a format that focuses on the main points. In addition, in the case of a blog article, the providing unit can also provide the information in a format that provides an outline of the content. Furthermore, in the case of a social media post, the providing unit can also provide the information in a format in which the main topics are extracted. This makes it possible to apply the optimal providing method depending on the category of information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of information to the generating AI and cause the generating AI to apply the providing method.

[0093] In the information collection system, the providing unit estimates the user's emotion and adjusts the order of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The providing unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit adjusts the order of information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides important information first. In addition, when the user is relaxed, the providing unit can also provide detailed information in an orderly manner. Furthermore, when the user is in a hurry, the providing unit can also provide information with the main points first. This makes it possible to adjust the order of information to be provided according to the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data to the generating AI and cause the generating AI to adjust the order in which information is provided.

[0094] In the information collection system, the providing unit can determine the priority of provision based on the collection time of the information at the time of provision. The collection time of the information includes, for example, the latest information, past information, and the like, but is not limited to such examples. The providing unit can, for example, use AI to identify the collection time of the information and determine the priority of provision at the time of provision. For example, the providing unit provides the latest information preferentially. In addition, the providing unit can also lower the priority of provision for old information. Furthermore, the providing unit can adjust the order of provision according to the collection time of the information. This makes it possible to determine the priority of provision according to the collection time of the information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collection time of the information to the generating AI and cause the generating AI to execute the determination of the priority of provision.

[0095] In the information collection system, the providing unit can adjust the order of provision based on the relevance of information when providing the information. The relevance of information includes, for example, the degree of matching of topics, the degree of co-occurrence of keywords, and the like, but is not limited to such examples. The providing unit can, for example, use AI to evaluate the relevance of information and adjust the order of provision when providing the information. For example, the providing unit provides highly relevant information preferentially. In addition, the providing unit can also postpone the order of provision of information with low relevance. Furthermore, the providing unit can also adjust the order of provision according to the relevance of information. This makes it possible to adjust the order of provision according to the relevance of information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information to the generating AI and cause the generating AI to adjust the order of provision.

[0096] In the information collection system, the detection unit estimates the user's emotion, and the detection method of the new information source can be adjusted based on the estimated user's emotion. The detection unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The detection unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The detection unit adjusts the detection method of the new information source based on the estimated user's emotion. For example, when the user is feeling stressed, the detection unit preferentially detects information sources with high importance. In addition, when the user is relaxed, the detection unit can also detect a wide range of information sources. Furthermore, when the user is in a hurry, the detection unit can also preferentially detect information sources that can be detected quickly. This makes it possible to adjust the detection method of the new information source according to the user's emotion. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the detection method for a new information source.

[0097] In the information collection system, when the detection unit detects, the detection unit can analyze the past detection history and select the optimal detection method. The detection history includes, for example, a pattern of information sources detected in the past, a detection frequency, a detection time period, and the like, but is not limited to such examples. The detection unit can analyze the past detection history, for example, using AI. For example, the detection unit analyzes a pattern of information sources detected in the past and selects the most effective detection method. The detection unit can also select a detection method that is effective in a specific time period from the past detection history. In addition, the detection unit can adjust the detection frequency from a specific information source based on the past detection history. This makes it possible to select the optimal detection method based on the past detection history. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the past detection history to the generation AI and cause the generation AI to select the optimal detection method.

[0098] In the information collection system, the detection unit estimates the user's emotion, and the priority of the new information source can be determined based on the estimated user's emotion. The detection unit estimates the user's emotion. The user's emotion includes, but is not limited to, stress, relaxation, and hurry. The detection unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to, for example. The detection unit determines the priority of the new information source based on the estimated user's emotion. For example, when the user is feeling stressed, the detection unit preferentially detects information sources with high importance. In addition, when the user is relaxed, the detection unit can also detect a wide range of information sources. Furthermore, when the user is in a hurry, the detection unit can also preferentially detect information sources that can be detected quickly. This makes it possible to determine the priority of the new information source according to the user's emotion. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input a user's emotional data into the generation AI and have the generation AI determine the priorities of new information sources.

[0099] In the information collection system, the detection unit can preferentially detect highly relevant information sources in consideration of the geographical location information of the user when the detection unit detects. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The detection unit can, for example, use AI to identify the geographical location information of the user and preferentially detect highly relevant information sources when the detection unit detects. For example, the detection unit preferentially detects information sources related to the area where the user is currently located. The detection unit can also filter information sources related to a specific area based on the geographical location information of the user. Furthermore, the detection unit can also preferentially detect local news and event information sources based on the geographical location information of the user. This makes it possible to detect highly relevant information sources based on the geographical location information of the user. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the geographical location information of the user to the generation AI and cause the generation AI to detect highly relevant information sources.

[0100] In the information collection system, the reception unit estimates the user's emotion, and can adjust the reception method of keywords and topics based on the estimated user's emotion. The reception unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The reception unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The reception unit adjusts the reception method of keywords and topics based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input keywords and topics. This makes it possible to adjust the reception method of keywords and topics according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the reception method of keywords and topics.

[0101] In the information collection system, the reception unit can select the optimal reception method by analyzing the past reception history when receiving a call. The reception history includes, for example, keywords and topics input in the past, input methods, reception time periods, etc., but is not limited to such examples. The reception unit can analyze the past reception history using, for example, AI. For example, the reception unit automatically displays keywords and topics that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that have been used in the past. Furthermore, the reception unit can predict and suggest keywords and topics that will be used in a specific time period from the past reception history. This makes it possible to select the optimal reception method based on the past reception history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past reception history to the generation AI and cause the generation AI to select the optimal reception method.

[0102] In the information collection system, the reception unit estimates the user's emotion and can determine the priority of keywords and topics to be received based on the estimated user's emotion. The reception unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The reception unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The reception unit determines the priority of keywords and topics to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives keywords and topics with high importance. In addition, when the user is relaxed, the reception unit can also receive a wide range of keywords and topics. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive keywords and topics that can be quickly received. This makes it possible to determine the priority of keywords and topics according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input a user's emotional data into the generation AI and have the generation AI determine the priority of keywords and topics.

[0103] In the information collection system, the reception unit can preferentially receive highly relevant keywords and topics in consideration of the geographical location information of the user when receiving the information. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The reception unit can, for example, use AI to identify the geographical location information of the user and preferentially receive highly relevant keywords and topics when receiving the information. For example, the reception unit preferentially receives keywords and topics related to the area where the user is currently located. The reception unit can also filter keywords and topics related to a specific area based on the geographical location information of the user. Furthermore, the reception unit can also preferentially receive keywords and topics related to local news and events based on the geographical location information of the user. This makes it possible to receive highly relevant keywords and topics based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the reception of highly relevant keywords and topics.

[0104] In the information collection system, the identification unit estimates the user's emotion, and can adjust the keyword and topic identification method based on the estimated user's emotion. The identification unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The identification unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The identification unit adjusts the keyword and topic identification method based on the estimated user's emotion. For example, when the user is feeling stressed, the identification unit preferentially identifies keywords and topics with high importance. In addition, when the user is relaxed, the identification unit can also identify a wide range of keywords and topics. Furthermore, when the user is in a hurry, the identification unit can also preferentially identify keywords and topics that can be quickly identified. This makes it possible to adjust the keyword and topic identification method according to the user's emotion. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the method of identifying keywords and topics.

[0105] In the information collection system, the identification unit can select an optimal identification method by analyzing the past identification history at the time of identification. The identification history includes, for example, keywords and topics identified in the past, identification methods, and identification time periods, but is not limited to such examples. The identification unit can analyze the past identification history using, for example, AI. For example, the identification unit analyzes the patterns of keywords and topics identified in the past and selects the most effective identification method. The identification unit can also select an effective identification method for a specific time period from the past identification history. Furthermore, the identification unit can adjust the identification frequency of a specific keyword or topic based on the past identification history. This makes it possible to select an optimal identification method based on the past identification history. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the past identification history to the generation AI and cause the generation AI to select the optimal identification method.

[0106] In the information collection system, the identification unit estimates the user's emotion and can determine the priority of keywords and topics to be identified based on the estimated user's emotion. The identification unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The identification unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The identification unit determines the priority of keywords and topics to be identified based on the estimated user's emotion. For example, when the user is feeling stressed, the identification unit preferentially identifies keywords and topics with high importance. In addition, when the user is relaxed, the identification unit can also identify a wide range of keywords and topics. Furthermore, when the user is in a hurry, the identification unit can also preferentially identify keywords and topics that can be quickly identified. This makes it possible to determine the priority of keywords and topics according to the user's emotion. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input user emotion data into the generation AI and have the generation AI determine the priority of keywords and topics.

[0107] In the information collection system, the identification unit can preferentially identify highly relevant keywords and topics in consideration of the geographical location information of the user at the time of identification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The identification unit can, for example, use AI to identify the geographical location information of the user and preferentially identify highly relevant keywords and topics at the time of identification. For example, the identification unit preferentially identifies keywords and topics related to the area where the user is currently located. The identification unit can also filter keywords and topics related to a specific area based on the geographical location information of the user. Furthermore, the identification unit can also preferentially identify keywords and topics related to local news and events based on the geographical location information of the user. This makes it possible to identify highly relevant keywords and topics based on the geographical location information of the user. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the identification of highly relevant keywords and topics.

[0108] In the information collection system, the creation unit estimates the user's emotions, and the method of creating a draft of an article can be adjusted based on the estimated user's emotions. The creation unit estimates the user's emotions. The user's emotions include, for example, stress, relaxation, and being in a hurry, but are not limited to such examples. The creation unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but are not limited to such examples. The creation unit adjusts the method of creating a draft of an article based on the estimated user's emotions. For example, when the user is feeling stressed, the creation unit creates a concise draft that covers the main points. In addition, when the user is relaxed, the creation unit can also create a draft that includes detailed information. Furthermore, when the user is in a hurry, the creation unit can also create a draft that can be understood in a short time. This makes it possible to adjust the method of creating a draft of an article according to the user's emotions. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation department can input a user's emotional data into the generation AI and have the generation AI adjust the way in which an article draft is created.

[0109] In the information collection system, the creation unit can select the optimal creation method by analyzing the past creation history at the time of creation. The creation history includes, for example, patterns of articles created in the past, creation frequency, creation time period, etc., but is not limited to such examples. The creation unit can analyze the past creation history, for example, using AI. For example, the creation unit analyzes the patterns of articles created in the past and selects the most effective creation method. The creation unit can also select an effective creation method for a specific time period from the past creation history. Furthermore, the creation unit can adjust the creation frequency of drafts related to a specific topic based on the past creation history. This makes it possible to select the optimal creation method based on the past creation history. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the past creation history to the generation AI and cause the generation AI to select the optimal creation method.

[0110] In the information collection system, the creation unit estimates the user's emotion and can determine the priority of the article drafts based on the estimated user's emotion. The creation unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The creation unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The creation unit determines the priority of the article drafts based on the estimated user's emotion. For example, when the user is feeling stressed, the creation unit prioritizes the creation of drafts with high importance. In addition, when the user is relaxed, the creation unit can also create a wide range of drafts. Furthermore, when the user is in a hurry, the creation unit can also prioritize the creation of drafts that can be created quickly. This makes it possible to determine the priority of the article drafts according to the user's emotion. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation department can input user emotional data into the generation AI and have the generation AI determine the priorities of article drafts.

[0111] In the information collection system, the creation unit can preferentially create a draft of a highly relevant article in consideration of the geographical location information of the user at the time of creation. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The creation unit can, for example, use AI to identify the geographical location information of the user and preferentially create a draft of a highly relevant article at the time of creation. For example, the creation unit preferentially creates a draft of an article related to the area where the user is currently located. The creation unit can also filter drafts of articles related to a specific area based on the geographical location information of the user. Furthermore, the creation unit can also preferentially create drafts of articles related to local news and events based on the geographical location information of the user. This makes it possible to create a draft of a highly relevant article based on the geographical location information of the user. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the creation of a draft of a highly relevant article.

[0112] In the information collection system, the evaluation unit estimates the user's emotion, and the reliability evaluation method of the information can be adjusted based on the estimated user's emotion. The evaluation unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The evaluation unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The evaluation unit adjusts the reliability evaluation method of the information based on the estimated user's emotion. For example, the evaluation unit evaluates the reliability of the information with strict criteria when the user is stressed. In addition, the evaluation unit can also evaluate the reliability of the information with flexible criteria when the user is relaxed. Furthermore, the evaluation unit can evaluate the reliability of the information with criteria that can be quickly evaluated when the user is in a hurry. This makes it possible to adjust the reliability evaluation method of the information according to the user's emotion. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input user emotion data to the generation AI and cause the generation AI to adjust the method of evaluating the reliability of information.

[0113] In the information collection system, the evaluation unit can select the optimal evaluation method by analyzing the past evaluation history at the time of evaluation. The evaluation history includes, for example, a pattern of information evaluated in the past, an evaluation frequency, an evaluation time period, and the like, but is not limited to such examples. The evaluation unit can analyze the past evaluation history, for example, using AI. For example, the evaluation unit analyzes a pattern of information evaluated in the past and selects the most effective evaluation method. The evaluation unit can also select an evaluation method that is effective in a specific time period from the past evaluation history. Furthermore, the evaluation unit can adjust the evaluation frequency from a specific information source based on the past evaluation history. This makes it possible to select the optimal evaluation method based on the past evaluation history. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation history to the generation AI and cause the generation AI to select the optimal evaluation method.

[0114] In the information collection system, the evaluation unit estimates the user's emotion, and the priority of the reliability evaluation of information can be determined based on the estimated user's emotion. The evaluation unit estimates the user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The evaluation unit estimates the user's emotion using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The evaluation unit determines the priority of the reliability evaluation of information based on the estimated user's emotion. For example, when the user is feeling stressed, the evaluation unit preferentially evaluates the reliability of information with high importance. In addition, when the user is relaxed, the evaluation unit can also evaluate the reliability of a wide range of information. Furthermore, when the user is in a hurry, the evaluation unit can also preferentially evaluate the reliability of information that can be quickly evaluated. This makes it possible to determine the priority of the reliability evaluation of information according to the user's emotion. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input a user's emotional data into the generation AI and have the generation AI determine the priority of the information reliability evaluation.

[0115] In the information collection system, the evaluation unit can prioritize highly relevant information in consideration of the geographical location information of the user at the time of evaluation. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The evaluation unit can, for example, use AI to identify the geographical location information of the user and prioritize highly relevant information at the time of evaluation. For example, the evaluation unit prioritizes the reliability of information related to the area where the user is currently located. The evaluation unit can also filter the reliability of information related to a specific area based on the geographical location information of the user. Furthermore, the evaluation unit can also prioritize the reliability of information related to local news and events based on the geographical location information of the user. This makes it possible to evaluate highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the evaluation of highly relevant information.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] In the information collection system, the collection unit can analyze the user's past search history and preferentially collect new related information. For example, the collection unit collects related latest news articles based on keywords and topics searched for by the user in the past. The collection unit can also preferentially collect information from a specific information source based on the user's past search history. Furthermore, the collection unit can analyze the user's past search history and collect related blog articles and social media posts. This makes it possible to efficiently collect more relevant information based on the user's past search history.

[0118] In the information collection system, the collection unit can estimate the user's emotions and adjust the categories of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit preferentially collects information about entertainment and hobbies that can relax the user. In addition, when the user is relaxed, the collection unit can also collect information about news and business. Furthermore, when the user is in a hurry, the collection unit can also collect information that focuses on the main points and can be understood in a short time. This makes it possible to adjust the categories of information to be collected according to the user's emotions.

[0119] In the information collection system, when the collection unit collects information, the collection unit can adjust the information collection method taking into account the usage status of the user's device. For example, when the user is using a smartphone, the collection unit can collect information optimized for mobile devices. In addition, when the user is using a personal computer, the collection unit can also collect detailed information. Furthermore, when the user is using a tablet, the collection unit can also collect information that is visually easy to view. This makes it possible to adjust the information collection method according to the usage status of the user's device.

[0120] In the information collection system, the collection unit estimates the user's emotions and adjusts the reliability evaluation criteria of the information based on the estimated user's emotions. For example, when the user is stressed, the collection unit evaluates the reliability of the information using strict criteria. Also, when the user is relaxed, the collection unit can evaluate the reliability of the information using flexible criteria. Furthermore, when the user is in a hurry, the collection unit can evaluate the reliability of the information using criteria that can be quickly evaluated. This makes it possible to adjust the reliability evaluation criteria of the information according to the user's emotions.

[0121] In the information collection system, when the collection unit collects information, the collection unit can analyze the user's past browsing history and prioritize collection of new related information. For example, the collection unit collects the latest related information based on news articles and blog articles that the user has viewed in the past. The collection unit can also prioritize collection of information from a specific information source based on the user's past browsing history. Furthermore, the collection unit can analyze the user's past browsing history and collect related social media posts. This makes it possible to efficiently collect more relevant information based on the user's past browsing history.

[0122] In the information collection system, the collection unit estimates the user's emotions and adjusts the frequency of information collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects only important information. Also, when the user is relaxed, the collection unit can increase the frequency of information collection and collect a wide range of information. Furthermore, when the user is in a hurry, the collection unit can collect information in real time and provide it quickly. This makes it possible to adjust the frequency of information collection according to the user's emotions.

[0123] In the information collection system, when the collection unit collects information, the collection unit can adjust the method of collecting information taking into consideration the number of followers of the user on social media. For example, when the user has many followers, the collection unit can preferentially collect influential information. In addition, when the user has few followers, the collection unit can also collect information based on personal interests. Furthermore, the collection unit can preferentially collect information from a specific source based on the number of followers of the user. This makes it possible to adjust the method of collecting information according to the number of followers of the user on social media.

[0124] In the information collection system, the collection unit estimates the user's emotions and adjusts the format of the information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit collects information in a concise, visual format. When the user is relaxed, the collection unit can also collect detailed text-format information. Furthermore, when the user is in a hurry, the collection unit can also collect information in a bullet-point format that focuses on the main points. In this way, the format of the information to be collected can be adjusted according to the user's emotions.

[0125] In the information collection system, when the collection unit collects information, the collection unit can analyze the user's past subscription history and prioritize collection of new related information. For example, the collection unit collects the latest related information based on newsletters and e-mail magazines to which the user has previously subscribed. The collection unit can also prioritize collection of information from a specific information source based on the user's past subscription history. Furthermore, the collection unit can analyze the user's past subscription history and collect related blog articles and social media posts. This makes it possible to efficiently collect more relevant information based on the user's past subscription history.

[0126] In the information collection system, the collection unit estimates the user's emotions and can determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting information of high importance. Also, when the user is relaxed, the collection unit can collect a wide range of information and lower the priority. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, the priority of information to be collected can be determined according to the user's emotions.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The collection unit collects new information from multiple sources. Sources include news sites, social media, blogs, etc. The collection unit uses crawling and scraping technologies to collect information related to specific keywords and topics. The collection unit can also detect new sources and collect new information from them. Step 2: The summarization unit summarizes the new information collected by the collection unit. Summarization is done in real time using AI to quickly summarize long articles and multiple pieces of information and extract important points. The summarization unit uses text generation AI and multimodal generation AI to concisely summarize the collected information. Step 3: The presentation unit provides new information summarized by the summarization unit. The presentation unit has a function of creating a draft of an article from the summarized information, so that a journalist can create an article based on the draft. The presentation unit provides the summarized information in text and graphical formats. The presentation unit can also evaluate the reliability of the collected information and provide the summarized information along with the evaluation results.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0130] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the collection unit is realized by a specific processing unit 290 of the data processing device 12. For example, the collection unit is also realized by a control unit 46A of the smart device 14. For example, the summarization unit is realized by a specific processing unit 290 of the data processing device 12. For example, the summarization unit is also realized by a control unit 46A of the smart device 14. For example, the provision unit is realized by a specific processing unit 290 of the data processing device 12. For example, the provision unit is also realized by a control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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, the RAM 30, and the 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] Fig. 4 shows an example of 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.

[0141] 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.

[0142] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the collection unit is also realized by the control unit 46A of the smart glasses 214. For example, the summarization unit is realized by the specific processing unit 290 of the data processing device 12. For example, the summarization unit is also realized by the control unit 46A of the smart glasses 214. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is also realized by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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, the RAM 30, and the 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] 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 performed in a secure state.

[0156] 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.

[0157] 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.

[0158] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] For example, the collection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the collection unit is also realized by the control unit 46A of the headset type terminal 314. For example, the summarization unit is realized by the specific processing unit 290 of the data processing device 12. For example, the summarization unit is also realized by the control unit 46A of the headset type terminal 314. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is also realized by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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, the RAM 30, and the 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.

[0168] 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.

[0169] 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 the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] 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 performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0173] 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.

[0174] 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.

[0175] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0177] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] 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 a voice indicating a user input for 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.

[0179] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0181] For example, the collection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the collection unit is also realized by the control unit 46A of the robot 414. For example, the summarization unit is realized by the specific processing unit 290 of the data processing device 12. For example, the summarization unit is also realized by the control unit 46A of the robot 414. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is also realized by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0189] 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.

[0190] 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 Universal Serial Bus (USB) 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.

[0191] In addition, 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 upon request from the data processing device 12.

[0192] 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.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0195] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) a collection unit for collecting new information from multiple sources; a summarizing unit that summarizes the new information collected by the collecting unit; A providing unit that provides new information summarized by the summarizing unit. A system characterized by: (Appendix 2) A detection unit is provided for detecting a new information source, The collection department: Collect new information from new information sources detected by the detection unit 2. The system of claim 1, (Appendix 3) A reception unit is provided for receiving an input of a specific keyword or a specific topic, The collection department: Collect new information related to a specific keyword or topic accepted by the acceptance section 2. The system of claim 1, (Appendix 4) An identification unit for identifying a specific keyword or a specific topic based on a history of articles created by a user; The collecting unit includes: Collecting new information related to the specific keyword or the specific topic identified by the identification unit 2. The system of claim 1, (Appendix 5) a creating unit that creates a draft of an article based on the new information summarized by the summarizing unit; The providing unit is Provide a draft of the article prepared by the writing department 2. The system of claim 1, (Appendix 6) an evaluation unit that evaluates the reliability of new information collected by the collection unit; The providing unit is providing the new information summarized by the summarizing unit together with the evaluation results by the evaluating unit 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past collection history and select collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: As information is collected, it is filtered based on the user's current interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, the system prioritizes collecting information that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 13) The summary portion comprises: Estimate the user's emotions and adjust the way summaries are presented based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The summary portion comprises: When generating summaries, adjust the level of detail in the summary based on the importance of the information 2. The system of claim 1, (Appendix 15) The summary portion comprises: When generating summaries, apply summarization algorithms according to the category of information 2. The system of claim 1, (Appendix 16) The summary portion comprises: Estimating user sentiment and adjusting the length of a summary based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The summary portion comprises: When generating summaries, prioritize summaries based on when the information was collected 2. The system of claim 1, (Appendix 18) The summary portion comprises: When generating summaries, adjust the order of summaries based on the relevance of the information 2. The system of claim 1, (Appendix 19) The providing unit is To estimate a user's emotion and adjust a format of information to be provided based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The providing unit is When providing information, adjust the level of detail provided based on the importance of the information 2. The system of claim 1, (Appendix 21) The providing unit is When providing information, apply different methods of providing information depending on the category of information 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and adjust the order of information provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The providing unit is At the time of provision, the provision will be prioritized based on when the information was collected. 2. The system of claim 1, (Appendix 24) The providing unit is When serving, adjust the order of serving based on the relevance of the information 2. The system of claim 1, (Appendix 25) The detection unit is Estimate user emotions and adjust the detection method of new information sources based on the estimated user emotions. 3. The system of claim 2, (Appendix 26) The detection unit is When a detection occurs, past detection history is analyzed to select the optimal detection method. 3. The system of claim 2, (Appendix 27) The detection unit is Estimate user emotions and prioritize new information sources based on the estimated user emotions. 3. The system of claim 2, (Appendix 28) The detection unit is During detection, the system takes into account the user's geographic location to prioritize more relevant sources of information. 3. The system of claim 2, (Appendix 29) The reception unit is Estimate user sentiment and adjust keyword and topic acceptance based on the estimated user sentiment 4. The system of claim 3. (Appendix 30) The reception unit is When accepting a call, analyze past acceptance history and select the optimal acceptance method. 4. The system of claim 3. (Appendix 31) The reception unit is Estimate user sentiment and prioritize keywords and topics to accept based on the estimated user sentiment 4. The system of claim 3. (Appendix 32) The reception unit is At the time of reception, the system takes into account the user's geographic location information and prioritizes highly relevant keywords and topics. 4. The system of claim 3. (Appendix 33) The identification unit is Infer user sentiment and adjust keyword and topic identification methods based on the inferred user sentiment 5. The system of claim 4. (Appendix 34) The identification unit is When identifying, analyze past identification history to select the optimal identification method 5. The system of claim 4. (Appendix 35) The identification unit is Infer user sentiment and prioritize keywords and topics to be identified based on the inferred user sentiment 5. The system of claim 4. (Appendix 36) The identification unit is During identification, it takes into account the user's geographic location to prioritize relevant keywords and topics 5. The system of claim 4. (Appendix 37) The creation unit is Infer user sentiment and adjust article draft generation based on the inferred user sentiment 6. The system of claim 5, (Appendix 38) The creation unit is When creating a document, analyze past creation history to select the optimal creation method. 6. The system of claim 5, (Appendix 39) The creation unit is Infer user sentiment and prioritize article drafts based on the estimated user sentiment 6. The system of claim 5, (Appendix 40) The creation unit is At the time of creation, it takes into account the user's geographic location to prioritize relevant article drafts. 6. The system of claim 5, (Appendix 41) The evaluation unit is To estimate a user's emotion and adjust a method of evaluating the reliability of information based on the estimated user's emotion. 7. The system of claim 6, (Appendix 42) The evaluation unit is At the time of evaluation, past evaluation history is analyzed to select the most appropriate evaluation method. 7. The system of claim 6, (Appendix 43) The evaluation unit is A method for estimating a user's emotions and determining the priority of the reliability evaluation of information based on the estimated user's emotions 7. The system of claim 6, (Appendix 44) The evaluation unit is During evaluation, the geographic location of the user is taken into account to prioritize the most relevant information. 7. The system of claim 6, [Explanation of symbols]

[0201] 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 for collecting new information from multiple sources; a summary unit for summarizing the new information collected by the collection unit; a providing unit that provides new information summarized by the summarizing unit. A system characterized by:

2. A detection unit is provided for detecting a new information source, The collecting unit includes: Collecting new information from a new information source detected by the detection unit 2. The system of claim 1 .

3. A reception unit is provided for receiving an input of a specific keyword or a specific topic, The collecting unit includes: Collecting new information related to a specific keyword or a specific topic accepted by the accepting unit 2. The system of claim 1 .

4. An identification unit for identifying a specific keyword or a specific topic based on a history of articles created by a user; The collecting unit includes: Collecting new information related to the specific keyword or the specific topic identified by the identification unit 2. The system of claim 1 .

5. a creating unit that creates a draft of an article based on the new information summarized by the summarizing unit; The providing unit is Provide a draft of the article prepared by the writing department 2. The system of claim 1 .

6. an evaluation unit that evaluates the reliability of new information collected by the collection unit; The providing unit is providing the new information summarized by the summarizing unit together with the evaluation results by the evaluating unit 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions.

2. The system of claim 1 .

8. The collecting unit includes: Analyze past collection history and select collection method 2. The system of claim 1 .

Images