System

Abstract

To collect new information from a plurality of information sources to summarize / provide the information according to an attribute of a user.SOLUTION: A system according to an embodiment includes a collection unit, a summarization unit, a providing unit, an analysis unit, and a prediction 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 according to an attribute of a user. The providing unit provides the information summarized by the summarization unit. The analysis unit analyzes trend data on the basis of the new information collected by the collection unit. The prediction unit predicts a field in which the user is interested on the basis of the trend data analyzed by the analysis 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 technologies have not been able to efficiently collect necessary information from a huge number of information sources and summarize and provide it according to the user's attributes, so there is room for improvement.

[0005] The system according to the embodiment aims to collect new information from multiple information sources and summarize and provide it according to the user's attributes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a summarization unit, a provision unit, an analysis unit, and a prediction unit. The collection unit collects new information from multiple information sources. The summarization unit summarizes the new information collected by the collection unit according to user attributes. The provision unit provides the information summarized by the summarization unit. The analysis unit analyzes trend data based on the new information collected by the collection unit. The prediction unit predicts areas of interest for the user based on the trend data analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can collect new information from multiple sources and summarize and provide it according to the user's attributes. [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 a 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 and summarization system according to the embodiment of the present invention is a system that collects new information from multiple information sources in real time and summarizes it according to the user's interests and priorities. The system collects new information from multiple information sources, summarizes the collected information according to the user's attributes, and provides the summarized information to the user. In addition, the system analyzes trends based on the collected information and predicts the field of interest of the user. For example, the system collects information in real time from social media platforms, news sites, etc. Next, the collected information is learned from the user's past browsing history and interests, and only topics and news of interest are picked up and summarized. Furthermore, information collected from different information sources is integrated, and information is summarized by eliminating duplicate information. The summarized information is provided as information based on the language and region desired by the user. For example, if the user desires information in Japanese, information in Japanese is provided. In addition, trends are analyzed based on the collected information to grasp the current topic and topic trends. Finally, a prediction is made of the field of interest of the user based on the analyzed trends. For example, new information related to a topic searched for by the user in the past is predicted and provided. This system allows the user to receive only the information that is truly necessary from among information overload. In addition to text, it is also possible to extract key points from video and audio and present them as text summaries. Furthermore, it is possible to provide news summaries in the language and region that the user prefers. This allows the information gathering and summarization system to collect, summarize, provide, analyze, and predict information according to the user's interests and priorities.

[0029] The information collection and summarization system according to the embodiment includes a collection unit, a summarization unit, a provision unit, an analysis unit, and a prediction unit. The collection unit collects new information from a plurality of information sources. For example, the collection unit collects information in real time from social media platforms and news sites. The collection unit can collect new information from, for example, X (formerly Twitter (registered trademark)), Facebook (registered trademark), news sites, and the like. The summarization unit summarizes the new information collected by the collection unit according to the attributes of the user. For example, the summarization unit learns the user's past browsing history and interests, and picks up and summarizes only topics and news of interest. For example, the summarization unit can prioritize summarizing new information related to information previously viewed by the user. In addition, the summarization unit can also integrate information collected from different information sources and summarize by eliminating overlapping information. Furthermore, the summarization unit can evaluate the reliability of information and prioritize summarizing highly reliable information. The provision unit provides the information summarized by the summarization unit. The provision unit can provide, for example, information of interest to the user from among the summarized information. In addition, the provision unit can provide information based on the language and region desired by the user. For example, if a user desires information in Japanese, information in Japanese can be provided. The analysis unit analyzes trends based on new information collected by the collection unit. The analysis unit can, for example, analyze the collected information and grasp current topics and topic trends. For example, the analysis unit can analyze news and information from around the world and grasp current topics and topic trends. The prediction unit predicts areas of interest to the user based on the trends analyzed by the analysis unit. The prediction unit can, for example, predict topics that the user is likely to be interested in in the future based on the user's past search history and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past. As a result, the information collection and summarization system according to the embodiment can collect, summarize, provide, analyze, and predict information according to the user's interests and priorities.

[0030] The collection unit collects new information from multiple sources. For example, the collection unit collects information in real time from social media platforms and news sites. Specifically, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. Data from these sources is automatically obtained through APIs. The collection unit uses the API of each source to filter posts and articles related to specific keywords and hashtags and extract the necessary information. For example, the API of X (formerly Twitter) is used to collect tweets related to a specific hashtag, and the API of Facebook is used to collect posts on a specific topic. In addition, the latest articles can be obtained from news sites using RSS feeds and scraping techniques. The collection unit collects this information in real time and stores it in a central database. In addition, the collection unit adds metadata (e.g., posting date and time, poster, source, etc.) to the collected information for subsequent processing. This allows the collection unit to efficiently collect a wide range of data from various sources and strengthen the information infrastructure of the entire system.

[0031] The summarizing unit summarizes new information collected by the collecting unit according to the user's attributes. For example, the summarizing unit learns the user's past browsing history and interests, and picks up and summarizes only topics and news of interest. Specifically, the summarizing unit uses natural language processing (NLP) technology to analyze the collected information and extract important keywords and phrases. Furthermore, a machine learning algorithm is used to analyze the user's past browsing history and search history, and to model the user's interests. This allows the summarizing unit to prioritize and summarize information that is likely to interest the user. The summarizing unit can also integrate information collected from different sources and summarize by eliminating duplicate information. For example, when the same news is reported by multiple sources, the summarizing unit integrates these pieces of information and provides them as one summary. Furthermore, the summarizing unit can evaluate the reliability of information and prioritize and summarize highly reliable information. To evaluate the reliability, an evaluation model based on the reliability score and past performance of the information source is used. This allows the summarizing unit to efficiently provide the user with reliable and interesting information.

[0032] The providing unit provides the information summarized by the summarizing unit. The providing unit can provide, for example, information of interest to the user from among the summarized information. Specifically, the providing unit provides information in an optimal format based on the user's profile information and settings. For example, information based on the language and region desired by the user can be provided. If the user desires information in Japanese, information in Japanese can be provided. The providing unit also displays the summarized information visually and easily through a user interface. For example, the latest news and topics can be displayed in a dashboard format, allowing the user to quickly check information of interest. Furthermore, the providing unit has a notification function, and can immediately send a notification to the user when important information or urgent news occurs. Notification is performed using multiple means, such as push notification, email, and SMS. This allows the providing unit to quickly provide appropriate information to the user and satisfy the information needs of the user.

[0033] The analysis unit analyzes trends based on new information collected by the collection unit. The analysis unit can, for example, analyze the collected information and grasp the trends of current topics and topics. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the contents of the collected information and extract frequently occurring keywords and phrases. Furthermore, the analysis unit uses a machine learning algorithm to analyze the frequency of occurrence and relevance of these keywords and phrases to identify current trends. For example, if a specific keyword is rapidly increasing, it can be determined that topics related to the keyword are currently attracting attention. Furthermore, the analysis unit can detect trend fluctuations and the emergence of new topics by comparing with past data. This allows the analysis unit to grasp trends in real time and provide the latest information to users. Furthermore, the analysis unit can also analyze trends by region and trends in specific user groups. This allows the analysis unit to provide trend information according to the user's attributes and region, thereby realizing more personalized information provision.

[0034] The prediction unit predicts the fields in which the user is interested based on the trends analyzed by the analysis unit. For example, the prediction unit can predict topics that the user is likely to be interested in in the future based on the user's past search history and provide the predicted information. Specifically, the prediction unit uses a machine learning algorithm to analyze the user's past behavioral data and model future interests and concerns. For example, the prediction unit can analyze the patterns of topics that the user has searched for in the past and articles that the user has viewed, and predict new related topics. The prediction unit can also predict future trend fluctuations based on current trend information. For example, if a specific keyword is rapidly increasing, it can predict that topics related to the keyword will continue to attract attention in the future. Furthermore, the prediction unit can make predictions according to the user's attributes and region. As a result, the prediction unit can provide the user with information that the user is likely to be interested in in the future and anticipate the user's information needs. The prediction unit can continuously update the prediction results and provide predictions based on the latest information, thereby always providing valuable information to the user.

[0035] The providing unit can provide information of interest to the user among the information summarized by the summarizing unit. The providing unit provides, for example, information of interest to the user among the information summarized by the summarizing unit. For example, the providing unit can provide information of interest to the user preferentially based on the user's past browsing history and interests. The providing unit can also provide similar information preferentially based on information evaluated by the user. For example, the providing unit can provide information from information sources that the user has highly rated in the past preferentially. This makes it possible to provide information based on the user's interests. 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 past browsing history and evaluation history into AI, and cause AI to identify information of interest.

[0036] The providing unit can provide information based on a language or region desired by the user from among the information summarized by the summarizing unit. The providing unit provides, for example, information based on a language or region desired by the user from among the information summarized by the summarizing unit. For example, when a user desires information in Japanese, the providing unit can provide information in Japanese. In addition, when a user desires information about a specific region, the providing unit can also provide information about the region. For example, the providing unit can preferentially provide news and information related to a region designated by the user. This makes it possible to provide information based on a language or region desired by the user. 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 language setting and region setting into AI, and cause AI to select appropriate information.

[0037] The collection unit can collect information in real time from social media platforms and news sites as multiple information sources. For example, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. The collection unit can also preferentially collect posts from celebrities and influencers followed by the user. For example, the collection unit can preferentially collect posts from accounts followed by the user. This makes it possible to collect information from various information sources in real time. Some 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 cause AI to collect information from social media platforms and news sites.

[0038] The summarizing unit can automatically change the level of detail of the summary based on the user's interest level. For example, the summarizing unit can adjust the level of detail of information of interest based on the user's past browsing history and interests. The summarizing unit can also dynamically change the level of detail of the summary based on user feedback. For example, the summarizing unit can provide a detailed summary when the user desires detailed information, and provide a concise summary when the user desires concise information. This enables a detailed summary according to the user's interest level. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the user's interest level into AI and cause AI to adjust the level of detail of the summary.

[0039] The collection unit can preferentially collect new information related to information previously viewed by the user. For example, the collection unit can preferentially collect new related information based on the user's past browsing history. The collection unit can also preferentially collect new related information based on the user's search history. For example, the collection unit can preferentially collect new information related to a topic previously searched by the user. This makes it possible to collect information based on the user's past browsing history. Some 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 past browsing history and search history into AI and cause AI to collect new related information.

[0040] The summarization unit can integrate information collected from different sources and summarize by eliminating duplicate information. For example, the summarization unit can integrate information collected from social media platforms and news sites and summarize by eliminating duplicate information. The summarization unit can also evaluate the reliability of information and prioritize highly reliable information for summarization. For example, the summarization unit can prioritize highly reliable information for summarization based on the reliability of the information source. This eliminates duplicate information and enables efficient summarization. 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 information collected from different sources into AI and cause AI to eliminate duplicate information.

[0041] The summarizing unit can evaluate the reliability of the information collected by the collecting unit and summarize highly reliable information with priority. For example, the summarizing unit can summarize highly reliable information with priority based on the reliability of the information source. The summarizing unit can also evaluate the reliability of information based on past performance. For example, the summarizing unit can summarize information sources that have provided highly reliable information in the past with priority. This makes it possible to provide highly reliable information by summarizing highly reliable information with priority. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can cause AI to perform a reliability evaluation of the collected information and summarize highly reliable information with priority.

[0042] The prediction unit can predict topics that the user may be interested in in the future based on the user's past search history and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past. The prediction unit can also analyze the user's past search history and identify topics that the user may be interested in in the future. For example, the prediction unit can predict and provide highly relevant topics based on the user's search history. This makes it possible to predict future topics based on the user's past search history. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's search history into AI and cause AI to predict topics that the user may be interested in in the future.

[0043] The summarization unit can set the priority of summaries based on the importance of the information collected by the collection unit. For example, the summarization unit can set the priority of summaries based on the novelty and impact of the information. The summarization unit can also set the priority of summaries based on the relevance of the information. For example, the summarization unit can preferentially summarize information related to the user's field of interest. This makes it possible to set the priority of summaries based on the importance of the information. 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 cause AI to perform an importance evaluation of the collected information and set the priority of summaries.

[0044] The collection unit can preferentially collect posts from celebrities and influencers followed by the user. For example, the collection unit can preferentially collect posts from accounts followed by the user. The collection unit can also preferentially collect posts from celebrities and influencers related to topics in which the user is interested. For example, the collection unit can preferentially collect posts related to topics in which the user is interested. This makes it possible to collect information based on the user's interests by preferentially collecting posts from celebrities and influencers followed by 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 information about accounts followed by the user into AI and cause AI to collect related posts.

[0045] The providing unit can provide similar information preferentially based on information previously evaluated by the user. For example, the providing unit can provide information from an information source that the user previously highly rated. The providing unit can also provide highly relevant information preferentially based on the user's evaluation history. For example, the providing unit can provide new information related to information previously evaluated by the user preferentially. This makes it possible to provide information based on information previously evaluated by the user. 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 evaluation history into AI and cause AI to provide related information.

[0046] The collection unit can analyze the user's past browsing history and select an appropriate information collection method. For example, the collection unit can preferentially collect information sources that the user has frequently browsed in the past. The collection unit can also select information sources to be collected during a specific time period from the user's past browsing history. Furthermore, the collection unit can also select information sources with high relevance based on the user's past browsing history. This makes it possible to select an optimal information collection method based on the user's past browsing history. 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 past browsing history into AI and cause AI to select an appropriate information collection method.

[0047] The collection unit can perform filtering based on the user's current field of interest when collecting information. For example, the collection unit can preferentially collect information related to topics in which the user is currently interested. The collection unit can also filter unnecessary information based on the user's current field of interest. Furthermore, the collection unit can also collect new information related to the user's current field of interest in real time. This enables information filtering based on the user's current field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current field of interest into AI and have the AI ​​perform information filtering.

[0048] The collection unit can collect highly relevant information preferentially based on the user's geographical location information when collecting information. For example, the collection unit can collect news and information related to the user's current location preferentially. The collection unit can also collect area-limited information based on the user's geographical location information. Furthermore, the collection unit can select a highly relevant information source based on the user's geographical location information. This makes it possible to collect highly relevant information based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's geographical location information to the AI ​​and cause the AI ​​to collect highly relevant information.

[0049] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can preferentially collect posts from accounts that the user follows. The collection unit can also collect related information based on the user's social media activities. Furthermore, the collection unit can also collect information based on the user's social media topics of interest. This makes it possible to collect related information based on the user's social media activities. Some 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 into AI and cause AI to collect related information.

[0050] The summarization unit can determine the priority of summaries based on the time of information submission when generating summaries. For example, the summarization unit can prioritize summarization of the latest information. The summarization unit can also postpone information that was submitted earlier. Furthermore, the summarization unit can adjust the order of summaries based on the time of submission. This makes it possible to determine the priority of summaries based on the time of information submission. Some 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 time of information submission to AI and have the AI ​​execute the determination of the priority of summaries.

[0051] The summarization unit can adjust the order of summaries based on the relevance of information when generating summaries. For example, the summarization unit can prioritize summarizing highly relevant information. The summarization unit can also postpone summarizing less relevant information. Furthermore, the summarization unit can adjust the order of summaries based on the relevance. This makes it possible to adjust the order of summaries based on the relevance of information. Some 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 AI and have the AI ​​adjust the order of summaries.

[0052] When providing information, the providing unit can select optimal information by referring to the user's past evaluation history. For example, the providing unit can preferentially provide information from information sources that the user has given high ratings to in the past. The providing unit can also select highly relevant information based on the user's past evaluation history. Furthermore, the providing unit can analyze the user's past evaluation history and provide the most suitable information. This makes it possible to provide optimal information based on the user's past evaluation history. 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 evaluation history into AI and cause AI to select optimal information.

[0053] The providing unit can customize the way information is displayed based on the user's current field of interest when providing information. For example, the providing unit can preferentially display information related to topics in which the user is currently interested. The providing unit can also customize the way information is displayed based on the user's current field of interest. Furthermore, the providing unit can display new information related to the user's current field of interest in real time. This enables customization of the way information is displayed based on the user's current field of interest. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's field of interest to AI and cause AI to customize the way information is displayed.

[0054] The providing unit can provide highly relevant information based on the geographical location information of the user when providing information. For example, the providing unit can preferentially provide news and information related to the user's current location. The providing unit can also provide area-limited information based on the geographical location information of the user. Furthermore, the providing unit can also provide highly relevant information based on the geographical location information of the user. This makes it possible to provide highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to provide highly relevant information.

[0055] The providing unit can analyze the user's social media activity and provide related information when providing information. For example, the providing unit can preferentially provide posts from accounts that the user follows. The providing unit can also provide related information based on the user's social media activity. Furthermore, the providing unit can also provide information based on the user's social media topics of interest. This makes it possible to provide related information based on the user's social media activity. 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 social media activity data into AI and cause AI to provide related information.

[0056] During trend analysis, the analysis unit can predict a current trend by referring to past trend data. For example, the analysis unit can predict a current trend based on past trend data. The analysis unit can also analyze past trend data and identify factors that affect the current trend. Furthermore, the analysis unit can predict changes in the current trend by referring to past trend data. This makes it possible to predict a current trend based on past trend data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past trend data to AI and cause AI to execute a prediction of a current trend.

[0057] The analysis unit can apply different analysis methods to each information category during trend analysis. For example, the analysis unit can apply a news-specific analysis method to the news category. The analysis unit can also apply a social media-specific analysis method to social media posts. Furthermore, the analysis unit can apply a multimedia-specific analysis method to video and audio. This makes it possible to apply an optimal analysis method according to the information category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the information category to AI and cause the AI ​​to apply an appropriate analysis method.

[0058] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. For example, the analysis unit can prioritize analysis of the latest information and grasp changes in trends. The analysis unit can also postpone analysis of information submitted earlier. Furthermore, the analysis unit can predict changes in trends based on the time of submission. This makes it possible to analyze changes in trends based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of information submission to AI and cause AI to analyze changes in trends.

[0059] During trend analysis, the analysis unit can analyze the trend by referring to the relevant market data of the information. For example, the analysis unit can analyze changes in the trend based on the relevant market data. The analysis unit can also predict the impact of the trend by referring to the related market data. Furthermore, the analysis unit can evaluate the importance of the trend based on the related market data. This enables trend analysis based on the related market data of the information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the related market data to AI and cause the AI ​​to perform trend analysis.

[0060] At the time of prediction, the prediction unit can select the optimal prediction method by analyzing the user's past search history. For example, the prediction unit can select a highly relevant prediction method based on the user's past search history. The prediction unit can also analyze the user's past search history and provide the most suitable prediction method. Furthermore, the prediction unit can refer to the user's past search history to predict topics that the user is likely to be interested in in the future. This makes it possible to select the optimal prediction method based on the user's past search history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's search history into AI and cause the AI ​​to select the optimal prediction method.

[0061] The prediction unit can customize the means of prediction based on the user's current field of interest at the time of prediction. For example, the prediction unit can provide predictions related to topics in which the user is currently interested. The prediction unit can also customize the means of prediction based on the user's current field of interest. Furthermore, the prediction unit can predict new information related to the user's current field of interest. This enables customization of the prediction means based on the user's current field of interest. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input the user's field of interest into AI and cause AI to perform customization of the means of prediction.

[0062] The prediction unit can provide a highly relevant prediction based on the user's geographical location information at the time of prediction. For example, the prediction unit can provide a prediction related to the user's current location. The prediction unit can also provide a region-limited prediction based on the user's geographical location information. Furthermore, the prediction unit can also provide a highly relevant prediction based on the user's geographical location information. This makes it possible to provide a highly relevant prediction based on the user's geographical location information. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's geographical location information to the AI ​​and cause the AI ​​to provide a highly relevant prediction.

[0063] At the time of prediction, the prediction unit can analyze the user's social media activity and provide a related prediction. For example, the prediction unit can provide a related prediction based on posts of accounts followed by the user. The prediction unit can also provide a related prediction based on the user's social media activity. Furthermore, the prediction unit can also suggest a means of prediction based on the user's social media topics of interest. This makes it possible to provide a related prediction based on the user's social media activity. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input the user's social media activity data into AI and cause the AI ​​to provide a related prediction.

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

[0065] The collection unit can preferentially collect information related to a specific area based on the geographical location information of the user. For example, the collection unit can preferentially collect news and event information in the area where the user is currently located. The collection unit can also collect information related to areas that the user has visited in the past. Furthermore, the collection unit can also collect information related to areas that the user plans to visit in the future. This makes it possible to collect 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 using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to an AI and cause the AI ​​to collect related information.

[0066] The summarizing unit can evaluate the reliability of the collected information and eliminate information with low reliability. For example, the summarizing unit can eliminate information with low reliability based on the reliability of the information source. The summarizing unit can also analyze the content of the information and identify information with low reliability. Furthermore, the summarizing unit can eliminate information sources with low reliability based on past performance. This makes it possible to summarize only information with high reliability. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can cause AI to perform a reliability evaluation of the collected information and eliminate information with low reliability.

[0067] The analysis unit can apply different analysis methods to each category of collected information. For example, the analysis unit can apply a news-specific analysis method to the news category. The analysis unit can also apply a social media-specific analysis method to social media posts. Furthermore, the analysis unit can apply a multimedia-specific analysis method to video and audio. This makes it possible to apply an optimal analysis method according to the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to AI and cause AI to apply an appropriate analysis method.

[0068] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can preferentially collect posts from accounts that the user follows. The collection unit can also collect related information based on the user's social media activities. Furthermore, the collection unit can also collect information based on the user's social media topics of interest. This makes it possible to collect related information based on the user's social media activities. Some 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 into AI and cause AI to collect related information.

[0069] The providing unit can provide highly relevant information based on the geographical location information of the user when providing information. For example, the providing unit can preferentially provide news and information related to the user's current location. The providing unit can also provide area-limited information based on the geographical location information of the user. Furthermore, the providing unit can also provide highly relevant information based on the geographical location information of the user. This makes it possible to provide highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to provide highly relevant information.

[0070] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. For example, the analysis unit can prioritize analysis of the latest information and grasp changes in trends. The analysis unit can also postpone analysis of information submitted earlier. Furthermore, the analysis unit can predict changes in trends based on the time of submission. This makes it possible to analyze changes in trends based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of information submission to AI and cause AI to analyze changes in trends.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The collection unit collects new information from multiple sources. For example, the collection unit collects information in real time from social media platforms and news sites. For example, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. Step 2: The summarization unit summarizes the new information collected by the collection unit according to the user's attributes. For example, the summarization unit learns the user's past browsing history and interests, and picks out and summarizes only topics and news of interest. For example, the summarization unit can prioritize summarizing new information related to information the user has previously viewed. The summarization unit can also integrate information collected from different sources and summarize by eliminating duplicate information. Furthermore, the summarization unit can evaluate the reliability of information and prioritize summarizing highly reliable information. Step 3: The providing unit provides the information summarized by the summarizing unit. The providing unit can provide, for example, information that is of interest to the user from among the summarized information. The providing unit can also provide information based on a language or region desired by the user. For example, if the user desires information in Japanese, information in Japanese can be provided. Step 4: The analysis unit analyzes trends based on the new information collected by the collection unit. The analysis unit may, for example, analyze the collected information to understand current topics and trending topics. For example, the analysis unit may analyze news and information from around the world to understand current topics and trending topics. Step 5: The prediction unit predicts areas of interest to the user based on the trends analyzed by the analysis unit. The prediction unit can predict topics that the user is likely to be interested in in the future based on the user's past search history, for example, and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past.

[0073] (Example 2) The information collection and summarization system according to the embodiment of the present invention is a system that collects new information from multiple information sources in real time and summarizes it according to the user's interests and priorities. The system collects new information from multiple information sources, summarizes the collected information according to the user's attributes, and provides the summarized information to the user. In addition, the system analyzes trends based on the collected information and predicts the field of interest of the user. For example, the system collects information in real time from social media platforms, news sites, etc. Next, the collected information is learned from the user's past browsing history and interests, and only topics and news of interest are picked up and summarized. Furthermore, information collected from different information sources is integrated, and information is summarized by eliminating duplicate information. The summarized information is provided as information based on the language and region desired by the user. For example, if the user desires information in Japanese, information in Japanese is provided. In addition, trends are analyzed based on the collected information to grasp the current topic and topic trends. Finally, a prediction is made of the field of interest of the user based on the analyzed trends. For example, new information related to a topic searched for by the user in the past is predicted and provided. This system allows the user to receive only the information that is truly necessary from among information overload. In addition to text, it is also possible to extract key points from video and audio and present them as text summaries. Furthermore, it is possible to provide news summaries in the language and region that the user prefers. This allows the information gathering and summarization system to collect, summarize, provide, analyze, and predict information according to the user's interests and priorities.

[0074] The information collection and summarization system according to the embodiment includes a collection unit, a summarization unit, a provision unit, an analysis unit, and a prediction unit. The collection unit collects new information from a plurality of information sources. For example, the collection unit collects information in real time from social media platforms and news sites. The collection unit can collect new information from, for example, X (formerly Twitter), Facebook, news sites, and the like. The summarization unit summarizes the new information collected by the collection unit according to the attributes of the user. For example, the summarization unit learns the user's past browsing history and interests, and picks up and summarizes only topics and news of interest. For example, the summarization unit can prioritize summarizing new information related to information previously viewed by the user. In addition, the summarization unit can also integrate information collected from different information sources and summarize by eliminating overlapping information. Furthermore, the summarization unit can evaluate the reliability of information and prioritize summarizing highly reliable information. The provision unit provides the information summarized by the summarization unit. The provision unit can provide, for example, information of interest to the user from among the summarized information. In addition, the provision unit can provide information based on the language and region desired by the user. For example, if a user desires information in Japanese, information in Japanese can be provided. The analysis unit analyzes trends based on new information collected by the collection unit. The analysis unit can, for example, analyze the collected information and grasp current topics and topic trends. For example, the analysis unit can analyze news and information from around the world and grasp current topics and topic trends. The prediction unit predicts areas of interest to the user based on the trends analyzed by the analysis unit. The prediction unit can, for example, predict topics that the user is likely to be interested in in the future based on the user's past search history and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past. As a result, the information collection and summarization system according to the embodiment can collect, summarize, provide, analyze, and predict information according to the user's interests and priorities.

[0075] The collection unit collects new information from multiple sources. For example, the collection unit collects information in real time from social media platforms and news sites. Specifically, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. Data from these sources is automatically obtained through APIs. The collection unit uses the API of each source to filter posts and articles related to specific keywords and hashtags and extract the necessary information. For example, the API of X (formerly Twitter) is used to collect tweets related to a specific hashtag, and the API of Facebook is used to collect posts on a specific topic. In addition, the latest articles can be obtained from news sites using RSS feeds and scraping techniques. The collection unit collects this information in real time and stores it in a central database. In addition, the collection unit adds metadata (e.g., posting date and time, poster, source, etc.) to the collected information for subsequent processing. This allows the collection unit to efficiently collect a wide range of data from various sources and strengthen the information infrastructure of the entire system.

[0076] The summarizing unit summarizes new information collected by the collecting unit according to the user's attributes. For example, the summarizing unit learns the user's past browsing history and interests, and picks up and summarizes only topics and news of interest. Specifically, the summarizing unit uses natural language processing (NLP) technology to analyze the collected information and extract important keywords and phrases. Furthermore, a machine learning algorithm is used to analyze the user's past browsing history and search history, and to model the user's interests. This allows the summarizing unit to prioritize and summarize information that is likely to interest the user. The summarizing unit can also integrate information collected from different sources and summarize by eliminating duplicate information. For example, when the same news is reported by multiple sources, the summarizing unit integrates these pieces of information and provides them as one summary. Furthermore, the summarizing unit can evaluate the reliability of information and prioritize and summarize highly reliable information. To evaluate the reliability, an evaluation model based on the reliability score and past performance of the information source is used. This allows the summarizing unit to efficiently provide the user with reliable and interesting information.

[0077] The providing unit provides the information summarized by the summarizing unit. The providing unit can provide, for example, information of interest to the user from among the summarized information. Specifically, the providing unit provides information in an optimal format based on the user's profile information and settings. For example, information based on the language and region desired by the user can be provided. If the user desires information in Japanese, information in Japanese can be provided. The providing unit also displays the summarized information visually and easily through a user interface. For example, the latest news and topics can be displayed in a dashboard format, allowing the user to quickly check information of interest. Furthermore, the providing unit has a notification function, and can immediately send a notification to the user when important information or urgent news occurs. Notification is performed using multiple means, such as push notification, email, and SMS. This allows the providing unit to quickly provide appropriate information to the user and satisfy the information needs of the user.

[0078] The analysis unit analyzes trends based on new information collected by the collection unit. The analysis unit can, for example, analyze the collected information and grasp the trends of current topics and topics. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the contents of the collected information and extract frequently occurring keywords and phrases. Furthermore, the analysis unit uses a machine learning algorithm to analyze the frequency of occurrence and relevance of these keywords and phrases to identify current trends. For example, if a specific keyword is rapidly increasing, it can be determined that topics related to the keyword are currently attracting attention. Furthermore, the analysis unit can detect trend fluctuations and the emergence of new topics by comparing with past data. This allows the analysis unit to grasp trends in real time and provide the latest information to users. Furthermore, the analysis unit can also analyze trends by region and trends in specific user groups. This allows the analysis unit to provide trend information according to the user's attributes and region, thereby realizing more personalized information provision.

[0079] The prediction unit predicts the fields in which the user is interested based on the trends analyzed by the analysis unit. For example, the prediction unit can predict topics that the user is likely to be interested in in the future based on the user's past search history and provide the predicted information. Specifically, the prediction unit uses a machine learning algorithm to analyze the user's past behavioral data and model future interests and concerns. For example, the prediction unit can analyze the patterns of topics that the user has searched for in the past and articles that the user has viewed, and predict new related topics. The prediction unit can also predict future trend fluctuations based on current trend information. For example, if a specific keyword is rapidly increasing, it can predict that topics related to the keyword will continue to attract attention in the future. Furthermore, the prediction unit can make predictions according to the user's attributes and region. As a result, the prediction unit can provide the user with information that the user is likely to be interested in in the future and anticipate the user's information needs. The prediction unit can continuously update the prediction results and provide predictions based on the latest information, thereby always providing valuable information to the user.

[0080] The providing unit can provide information of interest to the user among the information summarized by the summarizing unit. The providing unit provides, for example, information of interest to the user among the information summarized by the summarizing unit. For example, the providing unit can provide information of interest to the user preferentially based on the user's past browsing history and interests. The providing unit can also provide similar information preferentially based on information evaluated by the user. For example, the providing unit can provide information from information sources that the user has highly rated in the past preferentially. This makes it possible to provide information based on the user's interests. 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 past browsing history and evaluation history into AI, and cause AI to identify information of interest.

[0081] The providing unit can provide information based on a language or region desired by the user from among the information summarized by the summarizing unit. The providing unit provides, for example, information based on a language or region desired by the user from among the information summarized by the summarizing unit. For example, when a user desires information in Japanese, the providing unit can provide information in Japanese. In addition, when a user desires information about a specific region, the providing unit can also provide information about the region. For example, the providing unit can preferentially provide news and information related to a region designated by the user. This makes it possible to provide information based on a language or region desired by the user. 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 language setting and region setting into AI, and cause AI to select appropriate information.

[0082] The collection unit can collect information in real time from social media platforms and news sites as multiple information sources. For example, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. The collection unit can also preferentially collect posts from celebrities and influencers followed by the user. For example, the collection unit can preferentially collect posts from accounts followed by the user. This makes it possible to collect information from various information sources in real time. Some 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 cause AI to collect information from social media platforms and news sites.

[0083] The summarizing unit can automatically change the level of detail of the summary based on the user's interest level. For example, the summarizing unit can adjust the level of detail of information of interest based on the user's past browsing history and interests. The summarizing unit can also dynamically change the level of detail of the summary based on user feedback. For example, the summarizing unit can provide a detailed summary when the user desires detailed information, and provide a concise summary when the user desires concise information. This enables a detailed summary according to the user's interest level. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the user's interest level into AI and cause AI to adjust the level of detail of the summary.

[0084] The collection unit can preferentially collect new information related to information previously viewed by the user. For example, the collection unit can preferentially collect new related information based on the user's past browsing history. The collection unit can also preferentially collect new related information based on the user's search history. For example, the collection unit can preferentially collect new information related to a topic previously searched by the user. This makes it possible to collect information based on the user's past browsing history. Some 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 past browsing history and search history into AI and cause AI to collect new related information.

[0085] The summarization unit can integrate information collected from different sources and summarize by eliminating duplicate information. For example, the summarization unit can integrate information collected from social media platforms and news sites and summarize by eliminating duplicate information. The summarization unit can also evaluate the reliability of information and prioritize highly reliable information for summarization. For example, the summarization unit can prioritize highly reliable information for summarization based on the reliability of the information source. This eliminates duplicate information and enables efficient summarization. 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 information collected from different sources into AI and cause AI to eliminate duplicate information.

[0086] The summarizing unit can evaluate the reliability of the information collected by the collecting unit and summarize highly reliable information with priority. For example, the summarizing unit can summarize highly reliable information with priority based on the reliability of the information source. The summarizing unit can also evaluate the reliability of information based on past performance. For example, the summarizing unit can summarize information sources that have provided highly reliable information in the past with priority. This makes it possible to provide highly reliable information by summarizing highly reliable information with priority. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can cause AI to perform a reliability evaluation of the collected information and summarize highly reliable information with priority.

[0087] The prediction unit can predict topics that the user may be interested in in the future based on the user's past search history and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past. The prediction unit can also analyze the user's past search history and identify topics that the user may be interested in in the future. For example, the prediction unit can predict and provide highly relevant topics based on the user's search history. This makes it possible to predict future topics based on the user's past search history. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's search history into AI and cause AI to predict topics that the user may be interested in in the future.

[0088] The summarization unit can set the priority of summaries based on the importance of the information collected by the collection unit. For example, the summarization unit can set the priority of summaries based on the novelty and impact of the information. The summarization unit can also set the priority of summaries based on the relevance of the information. For example, the summarization unit can preferentially summarize information related to the user's field of interest. This makes it possible to set the priority of summaries based on the importance of the information. 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 cause AI to perform an importance evaluation of the collected information and set the priority of summaries.

[0089] The collection unit can preferentially collect posts from celebrities and influencers followed by the user. For example, the collection unit can preferentially collect posts from accounts followed by the user. The collection unit can also preferentially collect posts from celebrities and influencers related to topics in which the user is interested. For example, the collection unit can preferentially collect posts related to topics in which the user is interested. This makes it possible to collect information based on the user's interests by preferentially collecting posts from celebrities and influencers followed by 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 information about accounts followed by the user into AI and cause AI to collect related posts.

[0090] The providing unit can provide similar information preferentially based on information previously evaluated by the user. For example, the providing unit can provide information from an information source that the user previously highly rated. The providing unit can also provide highly relevant information preferentially based on the user's evaluation history. For example, the providing unit can provide new information related to information previously evaluated by the user preferentially. This makes it possible to provide information based on information previously evaluated by the user. 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 evaluation history into AI and cause AI to provide related information.

[0091] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can reduce the frequency of information collection and collect only important information. Also, when the user is relaxed, the collection unit can increase the frequency of information collection and provide a lot of information. Furthermore, when the user is in a hurry, the collection unit can preferentially collect important information in real time. This makes it possible to adjust the timing of information collection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to adjust the timing of information collection.

[0092] The collection unit can analyze the user's past browsing history and select an appropriate information collection method. For example, the collection unit can preferentially collect information sources that the user has frequently browsed in the past. The collection unit can also select information sources to be collected during a specific time period from the user's past browsing history. Furthermore, the collection unit can also select information sources with high relevance based on the user's past browsing history. This makes it possible to select an optimal information collection method based on the user's past browsing history. 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 past browsing history into AI and cause AI to select an appropriate information collection method.

[0093] The collection unit can perform filtering based on the user's current field of interest when collecting information. For example, the collection unit can preferentially collect information related to topics in which the user is currently interested. The collection unit can also filter unnecessary information based on the user's current field of interest. Furthermore, the collection unit can also collect new information related to the user's current field of interest in real time. This enables information filtering based on the user's current field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current field of interest into AI and have the AI ​​perform information filtering.

[0094] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can preferentially collect only important information when the user is feeling stressed. In addition, the collection unit can also collect a lot of information and provide a wide range of topics when the user is relaxed. Furthermore, the collection unit can preferentially collect important information in real time when the user is in a hurry. This makes it possible to determine the priority of information collection based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and cause the AI ​​to execute the priority of information collection.

[0095] The collection unit can collect highly relevant information preferentially based on the user's geographical location information when collecting information. For example, the collection unit can collect news and information related to the user's current location preferentially. The collection unit can also collect area-limited information based on the user's geographical location information. Furthermore, the collection unit can select a highly relevant information source based on the user's geographical location information. This makes it possible to collect highly relevant information based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's geographical location information to the AI ​​and cause the AI ​​to collect highly relevant information.

[0096] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can preferentially collect posts from accounts that the user follows. The collection unit can also collect related information based on the user's social media activities. Furthermore, the collection unit can also collect information based on the user's social media topics of interest. This makes it possible to collect related information based on the user's social media activities. Some 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 into AI and cause AI to collect related information.

[0097] The summarizing unit can estimate the user's emotion and adjust the way of expressing the summary based on the estimated emotion of the user. For example, when the user is stressed, the summarizing unit can provide a simple summary that covers the main points. In addition, when the user is relaxed, the summarizing unit can provide a summary that includes detailed information. Furthermore, when the user is in a hurry, the summarizing unit can provide a summary that can be understood in a short time. This makes it possible to adjust the way of expressing the summary based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can input the user's emotion data into an AI and cause the AI ​​to adjust the way of expressing the summary.

[0098] The summarization unit can determine the priority of summaries based on the time of information submission when generating summaries. For example, the summarization unit can prioritize summarization of the latest information. The summarization unit can also postpone information that was submitted earlier. Furthermore, the summarization unit can adjust the order of summaries based on the time of submission. This makes it possible to determine the priority of summaries based on the time of information submission. Some 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 time of information submission to AI and have the AI ​​execute the determination of the priority of summaries.

[0099] The summarization unit can adjust the order of summaries based on the relevance of information when generating summaries. For example, the summarization unit can prioritize summarizing highly relevant information. The summarization unit can also postpone summarizing less relevant information. Furthermore, the summarization unit can adjust the order of summaries based on the relevance. This makes it possible to adjust the order of summaries based on the relevance of information. Some 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 AI and have the AI ​​adjust the order of summaries.

[0100] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide information in a simple and highly visible manner. In addition, when the user is relaxed, the providing unit can also provide information in a manner including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide information in a manner that can be understood in a short time. This makes it possible to adjust the information providing method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the method of providing information.

[0101] When providing information, the providing unit can select optimal information by referring to the user's past evaluation history. For example, the providing unit can preferentially provide information from information sources that the user has given high ratings to in the past. The providing unit can also select highly relevant information based on the user's past evaluation history. Furthermore, the providing unit can analyze the user's past evaluation history and provide the most suitable information. This makes it possible to provide optimal information based on the user's past evaluation history. 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 evaluation history into AI and cause AI to select optimal information.

[0102] The providing unit can customize the way information is displayed based on the user's current field of interest when providing information. For example, the providing unit can preferentially display information related to topics in which the user is currently interested. The providing unit can also customize the way information is displayed based on the user's current field of interest. Furthermore, the providing unit can display new information related to the user's current field of interest in real time. This enables customization of the way information is displayed based on the user's current field of interest. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's field of interest to AI and cause AI to customize the way information is displayed.

[0103] The providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide only important information with priority. In addition, when the user is relaxed, the providing unit can provide a lot of information and provide a wide range of topics. Furthermore, when the user is in a hurry, the providing unit can provide important information with priority in real time. This makes it possible to determine the priority of information provision based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the priority of information provision.

[0104] The providing unit can provide highly relevant information based on the geographical location information of the user when providing information. For example, the providing unit can preferentially provide news and information related to the user's current location. The providing unit can also provide area-limited information based on the geographical location information of the user. Furthermore, the providing unit can also provide highly relevant information based on the geographical location information of the user. This makes it possible to provide highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to provide highly relevant information.

[0105] The providing unit can analyze the user's social media activity and provide related information when providing information. For example, the providing unit can preferentially provide posts from accounts that the user follows. The providing unit can also provide related information based on the user's social media activity. Furthermore, the providing unit can also provide information based on the user's social media topics of interest. This makes it possible to provide related information based on the user's social media activity. 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 social media activity data into AI and cause AI to provide related information.

[0106] The analysis unit can estimate the user's emotion and adjust the trend analysis method based on the estimated user's emotion. For example, the analysis unit can provide a simple and to the point trend analysis when the user is stressed. The analysis unit can also provide a trend analysis including detailed information when the user is relaxed. Furthermore, the analysis unit can provide a trend analysis that can be understood in a short time when the user is in a hurry. This enables the trend analysis method to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the trend analysis method.

[0107] During trend analysis, the analysis unit can predict a current trend by referring to past trend data. For example, the analysis unit can predict a current trend based on past trend data. The analysis unit can also analyze past trend data and identify factors that affect the current trend. Furthermore, the analysis unit can predict changes in the current trend by referring to past trend data. This makes it possible to predict a current trend based on past trend data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past trend data to AI and cause AI to execute a prediction of a current trend.

[0108] The analysis unit can apply different analysis methods to each information category during trend analysis. For example, the analysis unit can apply a news-specific analysis method to the news category. The analysis unit can also apply a social media-specific analysis method to social media posts. Furthermore, the analysis unit can apply a multimedia-specific analysis method to video and audio. This makes it possible to apply an optimal analysis method according to the information category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the information category to AI and cause the AI ​​to apply an appropriate analysis method.

[0109] The analysis unit can estimate the user's emotions and adjust the importance of trends based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit can prioritize analyzing only important trends. In addition, when the user is relaxed, the analysis unit can also analyze many trends and provide a wide range of topics. Furthermore, when the user is in a hurry, the analysis unit can prioritize analyzing important trends in real time. This enables trend importance adjustment based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the importance of trends.

[0110] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. For example, the analysis unit can prioritize analysis of the latest information and grasp changes in trends. The analysis unit can also postpone analysis of information submitted earlier. Furthermore, the analysis unit can predict changes in trends based on the time of submission. This makes it possible to analyze changes in trends based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of information submission to AI and cause AI to analyze changes in trends.

[0111] During trend analysis, the analysis unit can analyze the trend by referring to the relevant market data of the information. For example, the analysis unit can analyze changes in the trend based on the relevant market data. The analysis unit can also predict the impact of the trend by referring to the related market data. Furthermore, the analysis unit can evaluate the importance of the trend based on the related market data. This enables trend analysis based on the related market data of the information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the related market data to AI and cause the AI ​​to perform trend analysis.

[0112] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. For example, the prediction unit can provide a simple and to the point prediction when the user is stressed. The prediction unit can also provide a prediction including detailed information when the user is relaxed. Furthermore, the prediction unit can provide a prediction that can be understood in a short time when the user is in a hurry. This enables the adjustment of the prediction method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the prediction method.

[0113] At the time of prediction, the prediction unit can select the optimal prediction method by analyzing the user's past search history. For example, the prediction unit can select a highly relevant prediction method based on the user's past search history. The prediction unit can also analyze the user's past search history and provide the most suitable prediction method. Furthermore, the prediction unit can refer to the user's past search history to predict topics that the user is likely to be interested in in the future. This makes it possible to select the optimal prediction method based on the user's past search history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's search history into AI and cause the AI ​​to select the optimal prediction method.

[0114] The prediction unit can customize the means of prediction based on the user's current field of interest at the time of prediction. For example, the prediction unit can provide predictions related to topics in which the user is currently interested. The prediction unit can also customize the means of prediction based on the user's current field of interest. Furthermore, the prediction unit can predict new information related to the user's current field of interest. This enables customization of the prediction means based on the user's current field of interest. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input the user's field of interest into AI and cause AI to perform customization of the means of prediction.

[0115] The prediction unit can estimate the user's emotion and determine the priority of predictions based on the estimated user's emotion. For example, the prediction unit can provide only important predictions preferentially when the user is stressed. The prediction unit can also provide many predictions and provide a wide range of topics when the user is relaxed. Furthermore, the prediction unit can also provide important predictions preferentially in real time when the user is in a hurry. This enables the priority of predictions to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the prediction priority.

[0116] The prediction unit can provide a highly relevant prediction based on the user's geographical location information at the time of prediction. For example, the prediction unit can provide a prediction related to the user's current location. The prediction unit can also provide a region-limited prediction based on the user's geographical location information. Furthermore, the prediction unit can also provide a highly relevant prediction based on the user's geographical location information. This makes it possible to provide a highly relevant prediction based on the user's geographical location information. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's geographical location information to the AI ​​and cause the AI ​​to provide a highly relevant prediction.

[0117] At the time of prediction, the prediction unit can analyze the user's social media activity and provide a related prediction. For example, the prediction unit can provide a related prediction based on posts of accounts followed by the user. The prediction unit can also provide a related prediction based on the user's social media activity. Furthermore, the prediction unit can also suggest a means of prediction based on the user's social media topics of interest. This makes it possible to provide a related prediction based on the user's social media activity. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input the user's social media activity data into AI and cause the AI ​​to provide a related prediction.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The collection unit can preferentially collect information related to a specific area based on the geographical location information of the user. For example, the collection unit can preferentially collect news and event information in the area where the user is currently located. The collection unit can also collect information related to areas that the user has visited in the past. Furthermore, the collection unit can also collect information related to areas that the user plans to visit in the future. This makes it possible to collect 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 using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to an AI and cause the AI ​​to collect related information.

[0120] The summarizing unit can evaluate the reliability of the collected information and eliminate information with low reliability. For example, the summarizing unit can eliminate information with low reliability based on the reliability of the information source. The summarizing unit can also analyze the content of the information and identify information with low reliability. Furthermore, the summarizing unit can eliminate information sources with low reliability based on past performance. This makes it possible to summarize only information with high reliability. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can cause AI to perform a reliability evaluation of the collected information and eliminate information with low reliability.

[0121] The providing unit can estimate the user's emotion and adjust the timing of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can reduce the frequency of information provision and provide only important information. Also, when the user is relaxed, the providing unit can increase the frequency of information provision and provide a lot of information. Furthermore, when the user is in a hurry, the providing unit can preferentially provide important information in real time. This makes it possible to adjust the timing of information provision based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the timing of information provision.

[0122] The analysis unit can apply different analysis methods to each category of collected information. For example, the analysis unit can apply a news-specific analysis method to the news category. The analysis unit can also apply a social media-specific analysis method to social media posts. Furthermore, the analysis unit can apply a multimedia-specific analysis method to video and audio. This makes it possible to apply an optimal analysis method according to the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to AI and cause AI to apply an appropriate analysis method.

[0123] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. For example, the prediction unit can provide a simple and to the point prediction when the user is stressed. The prediction unit can also provide a prediction including detailed information when the user is relaxed. Furthermore, the prediction unit can provide a prediction that can be understood in a short time when the user is in a hurry. This enables the adjustment of the prediction method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the prediction method.

[0124] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can preferentially collect posts from accounts that the user follows. The collection unit can also collect related information based on the user's social media activities. Furthermore, the collection unit can also collect information based on the user's social media topics of interest. This makes it possible to collect related information based on the user's social media activities. Some 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 into AI and cause AI to collect related information.

[0125] The summarizing unit can estimate the user's emotion and adjust the way of expressing the summary based on the estimated emotion of the user. For example, when the user is stressed, the summarizing unit can provide a simple summary that covers the main points. In addition, when the user is relaxed, the summarizing unit can provide a summary that includes detailed information. Furthermore, when the user is in a hurry, the summarizing unit can provide a summary that can be understood in a short time. This makes it possible to adjust the way of expressing the summary based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, 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. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can input the user's emotion data into an AI and cause the AI ​​to adjust the way of expressing the summary.

[0126] The providing unit can provide highly relevant information based on the geographical location information of the user when providing information. For example, the providing unit can preferentially provide news and information related to the user's current location. The providing unit can also provide area-limited information based on the geographical location information of the user. Furthermore, the providing unit can also provide highly relevant information based on the geographical location information of the user. This makes it possible to provide highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to provide highly relevant information.

[0127] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. For example, the analysis unit can prioritize analysis of the latest information and grasp changes in trends. The analysis unit can also postpone analysis of information submitted earlier. Furthermore, the analysis unit can predict changes in trends based on the time of submission. This makes it possible to analyze changes in trends based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of information submission to AI and cause AI to analyze changes in trends.

[0128] The prediction unit can estimate the user's emotion and determine the priority of predictions based on the estimated user's emotion. For example, the prediction unit can provide only important predictions preferentially when the user is stressed. The prediction unit can also provide many predictions and provide a wide range of topics when the user is relaxed. Furthermore, the prediction unit can also provide important predictions preferentially in real time when the user is in a hurry. This enables the priority of predictions to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the prediction priority.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The collection unit collects new information from multiple sources. For example, the collection unit collects information in real time from social media platforms and news sites. For example, the collection unit can collect new information from X (formerly Twitter), Facebook, news sites, etc. Step 2: The summarization unit summarizes the new information collected by the collection unit according to the user's attributes. For example, the summarization unit learns the user's past browsing history and interests, and picks out and summarizes only topics and news of interest. For example, the summarization unit can prioritize summarizing new information related to information the user has previously viewed. The summarization unit can also integrate information collected from different sources and summarize by eliminating duplicate information. Furthermore, the summarization unit can evaluate the reliability of information and prioritize summarizing highly reliable information. Step 3: The providing unit provides the information summarized by the summarizing unit. The providing unit can provide, for example, information that is of interest to the user from among the summarized information. The providing unit can also provide information based on a language or region desired by the user. For example, if the user desires information in Japanese, information in Japanese can be provided. Step 4: The analysis unit analyzes trends based on the new information collected by the collection unit. The analysis unit may, for example, analyze the collected information to understand current topics and trending topics. For example, the analysis unit may analyze news and information from around the world to understand current topics and trending topics. Step 5: The prediction unit predicts areas of interest to the user based on the trends analyzed by the analysis unit. The prediction unit can predict topics that the user is likely to be interested in in the future based on the user's past search history, for example, and provide the predicted information. For example, the prediction unit can predict and provide new information related to topics that the user has searched for in the past.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] For example, the collection unit can collect information using the camera 42 and microphone 38B of the smart device 14. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the collected information according to the attributes of the user. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the summarized information to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes trends based on the collected information. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the field in which the user is interested. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] For example, the collection unit can collect information using the camera 42 and microphone 238 of the smart glasses 214. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the collected information according to the attributes of the user. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the summarized information to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes trends based on the collected information. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the field in which the user is interested. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] For example, the collection unit can collect information using the camera 42 and microphone 238 of the headset type terminal 314. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the collected information according to the attributes of the user. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the summarized information to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes trends based on the collected information. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the field in which the user is interested. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] For example, the collecting unit can collect information using the camera 42 and microphone 238 of the robot 414. The summarizing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and summarizes the collected information according to the attributes of the user. The providing unit is realized, for example, by the control unit 46A of the robot 414, and provides the summarized information to the user. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes trends based on the collected information. The predicting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the field in which the user is interested. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0184] 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.

[0185] 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.

[0186] These emotions are distributed in the three 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (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 according to a user's attributes; a providing unit that provides the information summarized by the summarizing unit; an analysis unit that analyzes trend data based on new information collected by the collection unit; A prediction unit that predicts a field in which the user is interested based on the trend data analyzed by the analysis unit. A system characterized by: (Appendix 2) The providing unit is Providing information of interest to the user among the information summarized by the summarizing unit 2. The system of claim 1, (Appendix 3) The providing unit is Providing information based on a language or region designated by the user out of the information summarized by the summarizing unit 2. The system of claim 1, (Appendix 4) The collecting unit includes: The multiple sources of information include specific social media platforms and news sites that collect information in real time. 2. The system of claim 1, (Appendix 5) The summary portion comprises: Automatically changing the level of detail of the summary based on the user's level of interest. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Prioritize collection of new information related to information previously viewed by the user 2. The system of claim 1, (Appendix 7) The summary portion comprises: Integrate information collected from different sources, eliminate duplicate information, and summarize 2. The system of claim 1, (Appendix 8) The summary portion comprises: Evaluating the reliability of the information collected by the collection unit and prioritizing and summarizing the highly reliable information. 2. The system of claim 1, (Appendix 9) The prediction unit is Based on the user's past search history, predict topics that may be of interest to the user in the future and provide the predicted information 2. The system of claim 1, (Appendix 10) The summary portion comprises: Prioritizing summaries based on the importance of the information collected by the collection unit 2. The system of claim 1, (Appendix 11) The collecting unit includes: Prioritizing collection of posts from celebrities and influencers followed by the user 2. The system of claim 1, (Appendix 12) The providing unit is Similar information is provided preferentially based on information previously evaluated by the user. 2. The system of claim 1, (Appendix 13) 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 14) The collecting unit includes: Analyze users' past browsing history and select the appropriate information collection method 2. The system of claim 1, (Appendix 15) 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 16) 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 17) The collecting unit includes: When collecting information, prioritize collection of information that is highly relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 18) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 19) 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 20) The summary portion comprises: During summary generation, prioritize summaries based on when information was submitted 2. The system of claim 1, (Appendix 21) 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 22) The providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing information, the most appropriate information is selected by referring to the user's past evaluation history. 2. The system of claim 1, (Appendix 24) The providing unit is Customize how information is presented to you based on your current areas of interest as it is provided to you 2. The system of claim 1, (Appendix 25) The providing unit is Estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, provide more relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 27) The providing unit is When you provide information, analyze your social media activity and provide you with relevant information 2. The system of claim 1, (Appendix 28) The analysis unit includes: To estimate a user's sentiment and adjust a trend analysis method based on the estimated user's sentiment. 2. The system of claim 1, (Appendix 29) The analysis unit includes: During trend analysis, historical trend data is referenced to predict current trends. 2. The system of claim 1, (Appendix 30) The analysis unit includes: When analyzing trends, apply different analytical techniques to different categories of information 2. The system of claim 1, (Appendix 31) The analysis unit includes: Infer user sentiment and adjust trend importance based on the inferred user sentiment 2. The system of claim 1, (Appendix 32) The analysis unit includes: During trend analysis, analyze changes in trends based on when information was submitted 2. The system of claim 1, (Appendix 33) The analysis unit includes: During trend analysis, the information will refer to relevant market data to analyze the trend. 2. The system of claim 1, (Appendix 34) The prediction unit is Infer user sentiment and adjust prediction methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 35) The prediction unit is When making predictions, the system analyzes the user's past search history to select the optimal prediction method. 2. The system of claim 1, (Appendix 36) The prediction unit is At the time of prediction, customize the prediction method based on the user's current areas of interest 2. The system of claim 1, (Appendix 37) The prediction unit is Infer user sentiment and prioritize predictions based on the inferred user sentiment 2. The system of claim 1, (Appendix 38) The prediction unit is At the time of prediction, provide more relevant predictions based on the user's geographic location 2. The system of claim 1, (Appendix 39) The prediction unit is At the time of prediction, it analyzes the user's social media activity and provides relevant predictions 2. The system of claim 1, [Explanation of symbols]

[0203] 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 summarizing unit that summarizes the new information collected by the collecting unit according to a user's attributes; a providing unit that provides the information summarized by the summarizing unit; an analysis unit that analyzes trend data based on new information collected by the collection unit; A prediction unit that predicts a field in which the user is interested based on the trend data analyzed by the analysis unit. A system characterized by:

2. The providing unit is Providing information of interest to the user among the information summarized by the summarizing unit 2. The system of claim 1 .

3. The providing unit is Providing information based on a language or region designated by the user out of the information summarized by the summarizing unit 2. The system of claim 1 .

4. The collecting unit includes: The multiple sources of information include specific social media platforms and news sites that collect information in real time.

2. The system of claim 1 .

5. The summary portion comprises: Automatically changing the level of detail of the summary based on the user's level of interest.

2. The system of claim 1 .

6. The collecting unit includes: Prioritize collection of new information related to information previously viewed by the user 2. The system of claim 1 .

7. The summary portion comprises: Integrate information collected from different sources, eliminate duplicate information, and summarize 2. The system of claim 1 .

8. The summary portion comprises: Evaluating the reliability of the information collected by the collection unit and prioritizing and summarizing the highly reliable information.

2. The system of claim 1 .

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