system

Abstract

The system according to the embodiment aims to enable a user to efficiently grasp a large amount of information. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects user behavioral history. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts information based on the analysis results obtained by the analysis unit. The summarization unit summarizes the information extracted by the extraction unit. The provision unit provides the information summarized by the summarization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques make it difficult for users to efficiently grasp large amounts of information, and can lead to information overload fatigue.

[0005] The system according to the embodiment aims to enable a user to efficiently grasp a large amount of information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects user behavioral histories. The analysis unit analyzes the data collected by the collection unit. The extraction unit extracts information based on the analysis results obtained by the analysis unit. The summarization unit summarizes the information extracted by the extraction unit. The provision unit provides the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to efficiently grasp a large amount of information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention uses AI to perform personalized analysis of a user's behavioral history and interests, automatically summarizing and providing information likely to be of interest to the user. This information provision system uses AI to analyze a user's behavioral history and interests, extract information likely to be of interest to the user based on the analysis results, and then summarizes the extracted information and provides it to the user. For example, the information provision system uses AI to analyze the user's frequently visited websites, app usage history, and social media posts. This allows the system to understand the user's interests. Next, the AI ​​extracts information likely to be of interest to the user based on the analysis results. For example, if the user is interested in sports, the AI ​​extracts the latest sports news and game results. Similarly, if the user is interested in technology, the AI ​​extracts the latest technology trends and gadget information. Finally, the AI ​​summarizes the extracted information and provides it to the user. For example, the AI ​​may summarize a long news article or consolidate information from multiple sources. This allows the user to grasp the information they need in a short amount of time. This system allows users to efficiently grasp the information they need without being overwhelmed by a large amount of information. Furthermore, since information is provided based on the user's interests, user satisfaction is improved. For example, a busy businessman can quickly get up to speed on the latest industry news, and a student can efficiently gather study materials. This allows the information provision system to automatically summarize and provide information that is likely to be of interest to the user based on the user's behavioral history.

[0029] An information provision system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects a user's behavioral history. The user's behavioral history includes, but is not limited to, website browsing history, app usage history, and social networking site (SNS) postings. The collection unit records, for example, URLs of websites frequently visited by the user and the browsing time. The collection unit can also collect app usage history and record the number of times the app is launched and the usage time. The collection unit can also collect social networking site postings and record the text, images, comments, and the like of the posts. For example, the collection unit collects data on websites frequently visited by the user to understand the user's interests. The collection unit can also collect app usage history and understand which apps the user frequently uses. The collection unit can also collect social networking site postings and understand what topics the user is interested in. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit statistically analyzes the collected data to understand the user's interests and concerns. The analysis unit can also analyze the data using a machine learning algorithm to understand the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of posts on SNS using text mining technology to understand the user's emotions and interests. For example, the analysis unit clusters the collected data to classify the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to predict the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of posts on SNS using text mining technology to estimate the user's emotions. The extraction unit extracts information based on the analysis results obtained by the analysis unit. Information extraction can be performed by, for example, keyword extraction or selection of highly relevant information, but is not limited to these examples. For example, the extraction unit extracts related keywords based on the user's interests. Furthermore, the extraction unit can select highly relevant information and determine the information to provide to the user.The extraction unit can also filter information based on the user's interests and extract only necessary information. For example, if the user is interested in sports, the extraction unit can extract the latest sports news and game results. If the user is interested in technology, the extraction unit can extract the latest technology trends and gadget information. The extraction unit can also filter information based on the user's interests and extract only necessary information. The summarization unit summarizes the information extracted by the extraction unit. Summarization can be performed based on, for example, but not limited to, the length of the text or the importance of the information to be summarized. For example, the summarization unit can shorten a long news article. The summarization unit can also combine information from multiple sources into one. The summarization unit can extract important information and provide a concise summary. For example, the summarization unit can shorten a long news article and provide it to the user. The summarization unit can also combine information from multiple sources and provide it to the user. The summarization unit can extract important information and provide a concise summary. The providing unit provides the information summarized by the summarization unit to the user. The information may be provided by, for example, a notification, an email, a dashboard display, or the like, but is not limited to these examples. For example, the providing unit provides the summarized information to the user as a notification. The providing unit may also send the summarized information to the user by email. Furthermore, the providing unit may display the summarized information on a dashboard so that the user can access it at any time. For example, the providing unit may provide the summarized information to the user as a notification so that the user can immediately check the information. The providing unit may also send the summarized information to the user by email so that the user can check it later. Furthermore, the providing unit may display the summarized information on a dashboard so that the user can access it at any time. In this way, the information providing system according to the embodiment can automatically summarize and provide information that is likely to be of interest to the user based on the user's behavior history.

[0030] The collection unit can collect the user's frequently visited website or app usage history and the content posted on social media. For example, the collection unit can record the URLs of the user's frequently visited websites and the browsing time. The collection unit can also collect app usage history and record the number of times the app is launched and the usage time. The collection unit can also collect the content posted on social media and record the text, images, comments, etc. of the posts. For example, the collection unit can collect data on the websites the user frequently visits to understand the user's interests. The collection unit can also collect app usage history and understand which apps the user frequently uses. The collection unit can also collect the content posted on social media to understand the topics the user is interested in. This makes it possible to collect data for understanding the user's interests. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's website browsing history into AI, which can identify topics of interest.

[0031] The analysis unit analyzes the data collected by the collection unit to understand the user's interests. For example, the analysis unit statistically analyzes the collected data to understand the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to understand the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of social media posts using text mining technology to understand the user's emotions and interests. For example, the analysis unit clusters the collected data to classify the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to predict the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of social media posts using text mining technology to infer the user's emotions. This makes it possible to understand the user's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which can identify the user's interests.

[0032] The extraction unit can extract information based on the user's interests. For example, the extraction unit extracts related keywords based on the user's interests. The extraction unit can also select highly relevant information and determine the information to provide to the user. Furthermore, the extraction unit can filter information based on the user's interests to extract only necessary information. For example, if the user is interested in sports, the extraction unit can extract the latest sports news and game results. If the user is interested in technology, the extraction unit can extract the latest technology trends and gadget information. Furthermore, the extraction unit can filter information based on the user's interests to extract only necessary information. This makes it possible to extract information that is likely to be of interest to the user. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input related keywords based on the user's interests into AI, which can then extract highly relevant information.

[0033] The summarization unit can summarize the extracted information. For example, the summarization unit can summarize a long news article in a short form. The summarization unit can also combine information from multiple information sources into one. Furthermore, the summarization unit can extract important information and provide a concise summary. For example, the summarization unit can summarize a long news article in a short form and provide it to a user. The summarization unit can also combine information from multiple information sources into one and provide it to a user. Furthermore, the summarization unit can extract important information and provide a concise summary. In this way, the extracted information can be summarized. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input the extracted information into AI, which then generates a summary.

[0034] The providing unit can provide the summarized information to the user. For example, the providing unit can provide the summarized information to the user as a notification. The providing unit can also send the summarized information to the user by email. Furthermore, the providing unit can display the summarized information on a dashboard so that the user can access it at any time. For example, the providing unit can provide the summarized information to the user as a notification so that the user can immediately check the information. The providing unit can also send the summarized information to the user by email so that the user can check it later. Furthermore, the providing unit can display the summarized information on a dashboard so that the user can access it at any time. In this way, the summarized information can be provided to the user. Some or all of the above-described 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 summarized information to AI, which can select the optimal method of providing the information.

[0035] The collection unit can analyze the user's past behavioral history and select an appropriate collection timing. For example, the collection unit identifies a time period during which the user often collects information and collects data according to that time period. If the user prefers a specific type of content on a specific day of the week, the collection unit can also collect data according to that day of the week. If the user tends to collect information during a specific event or situation, the collection unit can also collect data according to that event or situation. For example, if the user tends to collect information on weekday evenings, the collection unit collects data according to that time period. If the user prefers a specific type of content on weekends, the collection unit can also collect data according to that day of the week. If the user tends to collect information when attending a specific event (e.g., a sports game or a concert), the collection unit can also collect data according to that event. This makes it possible to select the optimal collection timing based on the user's behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's behavioral history data into AI, which can then select the optimal collection timing.

[0036] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, if the user is at work, the collection unit prioritizes collecting work-related information. Furthermore, if the user is on vacation, the collection unit can also collect information related to travel and leisure. Furthermore, if the user is working on a specific project, the collection unit can collect information related to the project. For example, the collection unit collects news and materials related to the user's work while the user is at work. Furthermore, the collection unit can collect tourist information and restaurant information for travel destinations while the user is on vacation. Furthermore, if the user is working on a specific project, the collection unit can collect technical information and reference materials related to the project. This allows data to be filtered based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's activity status data into AI, which then performs the filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, if the user is traveling, the collection unit can also collect tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the collection unit can also collect local news and event information related to that city. For example, if the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, if the user is traveling, the collection unit can also collect tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the collection unit can also collect local news and event information related to that city. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can then prioritize collecting highly relevant data.

[0038] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, if a user frequently posts about a particular topic, the collection unit can collect information related to the topic. Furthermore, if a user frequently uses a particular hashtag, the collection unit can also collect information related to the hashtag. Furthermore, if a user follows a particular account, the collection unit can also collect information related to the account. For example, if a user frequently posts about a particular topic, the collection unit can collect information related to the topic. Furthermore, if a user frequently uses a particular hashtag, the collection unit can also collect information related to the hashtag. Furthermore, if a user follows a particular account, the collection unit can also collect information related to the account. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-described 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 social media activity data into AI, which then collects related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit evaluates the importance based on the frequency of occurrence and relevance of the data, and performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance and an appropriate level of detail on data of medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI, which can adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can use text mining to analyze news data. The analysis unit can also use sentiment analysis to analyze social media data. Furthermore, the analysis unit can also use image recognition technology to analyze video data. For example, the analysis unit can use text mining to analyze news data and extract important information. The analysis unit can also use sentiment analysis to understand user emotions in social media data. Furthermore, the analysis unit can also use image recognition technology to extract important scenes in video data. This makes it possible to apply an appropriate analysis method depending on the data 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 data category into AI, which then applies an appropriate analysis method.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. Furthermore, the analysis unit can also analyze data that has been submitted recently with a moderate priority. For example, the analysis unit can determine the priority based on the date and time of data submission, and prioritize analysis of the most recent data. The analysis unit can also postpone analysis of older data, and analyze data that has been submitted recently with a moderate priority. In this way, the priority of analysis can be determined based on the time of data 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 data submission into AI, and the AI ​​can determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. For example, the analysis unit evaluates relevance based on the co-occurrence frequency and correlation of the data, and prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance and analyze data with moderate relevance in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0043] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit groups and extracts highly related data. The extraction unit can also exclude and extract data with low relevance. Furthermore, the extraction unit can analyze the interrelationships between data and extract the most related information. For example, the extraction unit evaluates the relevance based on the co-occurrence frequency and correlation of data, and groups and extracts highly related data. The extraction unit can also exclude and extract data with low relevance, and analyze the interrelationships between data to extract the most related information. This can improve the accuracy of extraction by taking into account the interrelationships between data. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the interrelationships between data into AI, which can improve the accuracy of extraction.

[0044] The extraction unit may perform extraction while taking into consideration attribute information of the data submitter. For example, the extraction unit may preferentially extract data from highly reliable submitters. The extraction unit may also exclude data from less reliable submitters. The extraction unit may also extract the most relevant information based on the submitter's expertise and experience. For example, the extraction unit may preferentially extract data from highly reliable submitters while taking into consideration attribute information such as the submitter's age, gender, and occupation. The extraction unit may also exclude data from less reliable submitters and extract the most relevant information based on the submitter's expertise and experience. This allows for improved extraction accuracy based on the attribute information of the data submitter. Some or all of the above-described processing by the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input the submitter's attribute information into AI, which then performs extraction.

[0045] The extraction unit can extract data taking into consideration the geographical distribution of the data. For example, if a user is in a specific region, the extraction unit can prioritize extracting information related to that region. Furthermore, if a user is traveling, the extraction unit can also extract tourist information and restaurant information for the travel destination. Furthermore, if a user lives in a specific city, the extraction unit can also extract local news and event information related to that city. For example, if a user is in a specific region, the extraction unit can prioritize extracting information related to that region. Furthermore, if a user is traveling, the extraction unit can also extract tourist information and restaurant information for the travel destination. Furthermore, if a user lives in a specific city, the extraction unit can also extract local news and event information related to that city. This makes it possible to extract highly relevant information based on the geographical distribution of the data. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the geographical distribution of the data into AI, which then performs the extraction.

[0046] During extraction, the extraction unit can improve the accuracy of extraction by referring to related literature of the data. The extraction unit, for example, refers to related literature and extracts the most relevant information. The extraction unit can also refer to related literature and extract highly reliable information. Furthermore, the extraction unit can also refer to related literature and extract the latest information. For example, the extraction unit refers to related literature and extracts the most relevant information. The extraction unit can also refer to related literature and extract highly reliable information. Furthermore, the extraction unit can also refer to related literature and extract the latest information. In this way, by referring to related literature of the data, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input related literature into AI, which can improve the accuracy of extraction.

[0047] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. For example, the summarization unit provides a detailed summary for information of high importance. The summarization unit can also provide a simple summary for information of low importance. Furthermore, the summarization unit can provide a summary with an appropriate level of detail for information of medium importance. For example, the summarization unit evaluates the importance of the information based on the frequency of appearance or relevance of the information, and provides a detailed summary for information of high importance. The summarization unit can also provide a simple summary for information of low importance and a summary with an appropriate level of detail for information of medium importance. In this way, the level of detail of the summary can be adjusted based on the importance of the information. Some or all of the above-described processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input the importance of the information into AI, which can adjust the level of detail of the summary.

[0048] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, for news information, the summarization unit can generate a summary using text mining. For social media information, the summarization unit can also generate a summary using sentiment analysis. For video information, the summarization unit can also generate a summary using image recognition technology. For example, for news information, the summarization unit can use text mining to generate a summary and extract important information. For social media information, the summarization unit can also grasp user emotions using sentiment analysis. For video information, the summarization unit can also extract important scenes using image recognition technology. This allows an appropriate summarization algorithm to be applied depending on the category of information. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the category of information into AI, which then applies an appropriate summarization algorithm.

[0049] When generating summaries, the summarizing unit can determine the priority of summaries based on the submission date of the information. For example, the summarizing unit can prioritize generating summaries for the most recent information. Also, the summarizing unit can postpone generating summaries for older information. Furthermore, the summarizing unit can generate summaries with a moderate priority for information that has been submitted recently. For example, the summarizing unit can determine the priority based on the date and time of submission of the information, and prioritize generating summaries for the most recent information. Also, the summarizing unit can postpone generating summaries for older information, and generate summaries with a moderate priority for information that has been submitted recently. In this way, the priority of summaries can be determined based on the submission date of the information. 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 input the submission date of the information into AI, and the AI ​​can determine the priority of summaries.

[0050] 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 generating summaries for information with high relevance. The summarization unit can also postpone generating summaries for information with low relevance. Furthermore, the summarization unit can generate summaries in an appropriate order for information with medium relevance. For example, the summarization unit can evaluate the relevance based on the co-occurrence frequency or correlation of information, and prioritize generating summaries for information with high relevance. The summarization unit can also postpone generating summaries for information with low relevance, and generate summaries in an appropriate order for information with medium 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 using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the relevance of information into AI, and the AI ​​can adjust the order of summaries.

[0051] When providing the display method, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can prioritize and provide display methods that the user has previously preferred. The providing unit can also exclude display methods that the user has previously avoided. Furthermore, the providing unit can analyze the user's past browsing history and provide the most optimal display method. For example, the providing unit can prioritize and provide display methods that the user has previously preferred. The providing unit can also exclude display methods that the user has previously avoided and provide the most optimal display method by analyzing the user's past browsing history. This makes it possible to select the optimal display method based on the user's past browsing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past browsing history into AI, which can select the optimal display method.

[0052] The providing unit can customize the method of providing information based on the user's current activity status when providing the information. For example, if the user is at work, the providing unit can prioritize providing work-related information. Furthermore, if the user is on vacation, the providing unit can also provide travel and leisure-related information. Furthermore, if the user is working on a specific project, the providing unit can also provide information related to the project. For example, the providing unit can provide work-related news and materials while the user is at work. Furthermore, the providing unit can provide tourist information and restaurant information for travel destinations while the user is on vacation. Furthermore, if the user is working on a specific project, the providing unit can also provide technical information and reference materials related to the project. This allows the method of providing information to be customized based on the user's current activity status. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then customize the method of providing information.

[0053] The providing unit can select the optimal information provision method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can also provide tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the providing unit can also provide local news and event information related to that city. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can also provide tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the providing unit can also provide local news and event information related to that city. This makes it possible to select the optimal information provision method based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can select the optimal information provision method.

[0054] The providing unit can analyze the user's social media activity and suggest a method of providing information at the time of providing the information. For example, if the user frequently posts about a specific topic, the providing unit can prioritize providing information related to the topic. Also, if the user frequently uses a specific hashtag, the providing unit can provide information related to the hashtag. Furthermore, if the user follows a specific account, the providing unit can provide information related to the account. For example, if the user frequently posts about a specific topic, the providing unit can prioritize providing information related to the topic. Also, if the user frequently uses a specific hashtag, the providing unit can provide information related to the hashtag. Furthermore, if the user follows a specific account, the providing unit can provide information related to the account. This makes it possible to suggest a method of providing information based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can then suggest a method of providing information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The information provision system can also collect health data from the user and provide information based on the user's health condition. For example, the collection unit collects heart rate, step count, and sleep data from the user's fitness tracker or smartwatch. The collection unit can also collect calorie intake and nutrient data from the user's food recording app. The analysis unit analyzes this data to understand the user's health condition. For example, if the user is not getting enough exercise, the analysis unit can provide health-related articles and exercise programs. If the user is feeling stressed, the analysis unit can provide information on relaxation methods and stress management. This makes it possible to provide information based on the user's health condition, contributing to the maintenance and improvement of the user's health.

[0057] The collection unit can also collect a user's purchasing history and provide information based on the user's purchasing trends. For example, the collection unit collects data on products purchased by the user on an online shopping site. The collection unit can also collect receipt data on products purchased by the user in stores. The analysis unit analyzes this data to understand the user's purchasing trends. For example, if the user frequently purchases products from a particular brand or category, the analysis unit can provide new product information and sale information related to that brand or category. Also, if the user tends to purchase specific products during a particular season, the analysis unit can provide product information tailored to that season. This makes it possible to provide personalized information based on the user's purchasing trends.

[0058] The extraction unit can customize information based on the user's hobbies and preferences. For example, if the user is interested in music, the latest music news and new release information can be provided. If the user is interested in movies, the latest movie reviews and screening schedules can be provided. Furthermore, if the user is interested in cooking, information on new recipes and cooking tips can be provided. This makes it possible to provide personalized information based on the user's hobbies and preferences. The extraction unit can analyze the user's behavioral history and the content of posts on social media to understand the user's hobbies and preferences. This makes it possible to provide the user with the information that is most interesting to them.

[0059] The summarizing unit can summarize information based on the user's learning style. For example, if the user is a visual learner, it can provide a summary using graphs and charts. If the user is an auditory learner, it can provide an audio summary. Furthermore, if the user has good reading comprehension skills, it can provide a detailed text summary. This allows the system to provide an optimal summary according to the user's learning style. The summarizing unit can analyze the user's past learning history and feedback to understand the user's learning style. This allows the system to provide the most effective summary for the user.

[0060] The collection unit can collect the user's device usage status and select the optimal information provision method. For example, if the user frequently uses a smartphone, information can be provided in a mobile-friendly format. Also, if the user uses a tablet, information can be provided in a layout suitable for a large screen. Furthermore, if the user uses a desktop, information can be provided in a layout including detailed information. This makes it possible to select the optimal information provision method based on the user's device usage status. The collection unit can analyze the user's device usage history and determine which device the user uses most frequently. This makes it possible to provide information in a format that is most user-friendly for the user.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects the user's behavioral history. The user's behavioral history includes website browsing history, app usage history, and content posted on social media. For example, the collection unit records the URLs of websites frequently visited by the user and the browsing time, collects app usage history, and records the number of times the app is launched and the usage time. Furthermore, the collection unit collects content posted on social media and records the text, images, comments, etc. of the post. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and text mining technology. For example, the analysis unit performs statistical analysis on the collected data to understand the user's interests and concerns. It can also analyze the data using machine learning algorithms to understand the user's behavioral patterns. It can also use text mining technology to analyze the content of social media posts to understand the user's emotions and concerns. Step 3: The extraction unit extracts information based on the analysis results obtained by the analysis unit. Information extraction is performed by methods such as keyword extraction and selection of highly relevant information. For example, the extraction unit extracts relevant keywords based on the user's interests and selects highly relevant information. It can also filter information based on the user's interests to extract only the necessary information. Step 4: The Summarizer summarizes the information extracted by the Extractor. Summarization is based on the length of the text and the importance of the information being summarized. For example, the Summarizer can summarize a long news article in a short form or combine information from multiple sources. It can also extract important information and summarize it succinctly. Step 5: The providing unit provides the information summarized by the summarizing unit to the user. The information is provided by a notification, an email, a dashboard display, or the like. For example, the providing unit may provide the summarized information to the user as a notification and send it by email. Furthermore, the summarized information may be displayed on a dashboard so that the user can access it at any time.

[0063] (Example 2) An information provision system according to an embodiment of the present invention uses AI to perform personalized analysis of a user's behavioral history and interests, automatically summarizing and providing information likely to be of interest to the user. This information provision system uses AI to analyze a user's behavioral history and interests, extract information likely to be of interest to the user based on the analysis results, and then summarizes the extracted information and provides it to the user. For example, the information provision system uses AI to analyze the user's frequently visited websites, app usage history, and social media posts. This allows the system to understand the user's interests. Next, the AI ​​extracts information likely to be of interest to the user based on the analysis results. For example, if the user is interested in sports, the AI ​​extracts the latest sports news and game results. Similarly, if the user is interested in technology, the AI ​​extracts the latest technology trends and gadget information. Finally, the AI ​​summarizes the extracted information and provides it to the user. For example, the AI ​​may summarize a long news article or consolidate information from multiple sources. This allows the user to grasp the information they need in a short amount of time. This system allows users to efficiently grasp the information they need without being overwhelmed by a large amount of information. Furthermore, since information is provided based on the user's interests, user satisfaction is improved. For example, a busy businessman can quickly get up to speed on the latest industry news, and a student can efficiently gather study materials. This allows the information provision system to automatically summarize and provide information that is likely to be of interest to the user based on the user's behavioral history.

[0064] An information provision system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, a summarization unit, and a provision unit. The collection unit collects a user's behavioral history. The user's behavioral history includes, but is not limited to, website browsing history, app usage history, and social networking site (SNS) postings. The collection unit records, for example, URLs of websites frequently visited by the user and the browsing time. The collection unit can also collect app usage history and record the number of times the app is launched and the usage time. The collection unit can also collect social networking site postings and record the text, images, comments, and the like of the posts. For example, the collection unit collects data on websites frequently visited by the user to understand the user's interests. The collection unit can also collect app usage history and understand which apps the user frequently uses. The collection unit can also collect social networking site postings and understand what topics the user is interested in. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit statistically analyzes the collected data to understand the user's interests and concerns. The analysis unit can also analyze the data using a machine learning algorithm to understand the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of posts on SNS using text mining technology to understand the user's emotions and interests. For example, the analysis unit clusters the collected data to classify the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to predict the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of posts on SNS using text mining technology to estimate the user's emotions. The extraction unit extracts information based on the analysis results obtained by the analysis unit. Information extraction can be performed by, for example, keyword extraction or selection of highly relevant information, but is not limited to these examples. For example, the extraction unit extracts related keywords based on the user's interests. Furthermore, the extraction unit can select highly relevant information and determine the information to provide to the user.The extraction unit can also filter information based on the user's interests and extract only necessary information. For example, if the user is interested in sports, the extraction unit can extract the latest sports news and game results. If the user is interested in technology, the extraction unit can extract the latest technology trends and gadget information. The extraction unit can also filter information based on the user's interests and extract only necessary information. The summarization unit summarizes the information extracted by the extraction unit. Summarization can be performed based on, for example, but not limited to, the length of the text or the importance of the information to be summarized. For example, the summarization unit can shorten a long news article. The summarization unit can also combine information from multiple sources into one. The summarization unit can extract important information and provide a concise summary. For example, the summarization unit can shorten a long news article and provide it to the user. The summarization unit can also combine information from multiple sources and provide it to the user. The summarization unit can extract important information and provide a concise summary. The providing unit provides the information summarized by the summarization unit to the user. The information may be provided by, for example, a notification, an email, a dashboard display, or the like, but is not limited to these examples. For example, the providing unit provides the summarized information to the user as a notification. The providing unit may also send the summarized information to the user by email. Furthermore, the providing unit may display the summarized information on a dashboard so that the user can access it at any time. For example, the providing unit may provide the summarized information to the user as a notification so that the user can immediately check the information. The providing unit may also send the summarized information to the user by email so that the user can check it later. Furthermore, the providing unit may display the summarized information on a dashboard so that the user can access it at any time. In this way, the information providing system according to the embodiment can automatically summarize and provide information that is likely to be of interest to the user based on the user's behavior history.

[0065] The collection unit can collect the user's frequently visited website or app usage history and the content posted on social media. For example, the collection unit can record the URLs of the user's frequently visited websites and the browsing time. The collection unit can also collect app usage history and record the number of times the app is launched and the usage time. The collection unit can also collect the content posted on social media and record the text, images, comments, etc. of the posts. For example, the collection unit can collect data on the websites the user frequently visits to understand the user's interests. The collection unit can also collect app usage history and understand which apps the user frequently uses. The collection unit can also collect the content posted on social media to understand the topics the user is interested in. This makes it possible to collect data for understanding the user's interests. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's website browsing history into AI, which can identify topics of interest.

[0066] The analysis unit analyzes the data collected by the collection unit to understand the user's interests. For example, the analysis unit statistically analyzes the collected data to understand the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to understand the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of social media posts using text mining technology to understand the user's emotions and interests. For example, the analysis unit clusters the collected data to classify the user's interests. The analysis unit can also analyze the data using a machine learning algorithm to predict the user's behavioral patterns. Furthermore, the analysis unit can analyze the content of social media posts using text mining technology to infer the user's emotions. This makes it possible to understand the user's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which can identify the user's interests.

[0067] The extraction unit can extract information based on the user's interests. For example, the extraction unit extracts related keywords based on the user's interests. The extraction unit can also select highly relevant information and determine the information to provide to the user. Furthermore, the extraction unit can filter information based on the user's interests to extract only necessary information. For example, if the user is interested in sports, the extraction unit can extract the latest sports news and game results. If the user is interested in technology, the extraction unit can extract the latest technology trends and gadget information. Furthermore, the extraction unit can filter information based on the user's interests to extract only necessary information. This makes it possible to extract information that is likely to be of interest to the user. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input related keywords based on the user's interests into AI, which can then extract highly relevant information.

[0068] The summarization unit can summarize the extracted information. For example, the summarization unit can summarize a long news article in a short form. The summarization unit can also combine information from multiple information sources into one. Furthermore, the summarization unit can extract important information and provide a concise summary. For example, the summarization unit can summarize a long news article in a short form and provide it to a user. The summarization unit can also combine information from multiple information sources into one and provide it to a user. Furthermore, the summarization unit can extract important information and provide a concise summary. In this way, the extracted information can be summarized. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input the extracted information into AI, which then generates a summary.

[0069] The providing unit can provide the summarized information to the user. For example, the providing unit can provide the summarized information to the user as a notification. The providing unit can also send the summarized information to the user by email. Furthermore, the providing unit can display the summarized information on a dashboard so that the user can access it at any time. For example, the providing unit can provide the summarized information to the user as a notification so that the user can immediately check the information. The providing unit can also send the summarized information to the user by email so that the user can check it later. Furthermore, the providing unit can display the summarized information on a dashboard so that the user can access it at any time. In this way, the summarized information can be provided to the user. Some or all of the above-described 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 summarized information to AI, which can select the optimal method of providing the information.

[0070] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing content. Furthermore, if the user is excited, the collection unit can collect entertaining content. Furthermore, if the user is tired, the collection unit can collect content that can be consumed in a short time. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the type of data to be collected to be adjusted depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the AI ​​and adjust the type of data collected by the AI.

[0071] The collection unit can analyze the user's past behavioral history and select an appropriate collection timing. For example, the collection unit identifies a time period during which the user often collects information and collects data according to that time period. If the user prefers a specific type of content on a specific day of the week, the collection unit can also collect data according to that day of the week. If the user tends to collect information during a specific event or situation, the collection unit can also collect data according to that event or situation. For example, if the user tends to collect information on weekday evenings, the collection unit collects data according to that time period. If the user prefers a specific type of content on weekends, the collection unit can also collect data according to that day of the week. If the user tends to collect information when attending a specific event (e.g., a sports game or a concert), the collection unit can also collect data according to that event. This makes it possible to select the optimal collection timing based on the user's behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's behavioral history data into AI, which can then select the optimal collection timing.

[0072] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, if the user is at work, the collection unit prioritizes collecting work-related information. Furthermore, if the user is on vacation, the collection unit can also collect information related to travel and leisure. Furthermore, if the user is working on a specific project, the collection unit can collect information related to the project. For example, the collection unit collects news and materials related to the user's work while the user is at work. Furthermore, the collection unit can collect tourist information and restaurant information for travel destinations while the user is on vacation. Furthermore, if the user is working on a specific project, the collection unit can collect technical information and reference materials related to the project. This allows data to be filtered based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's activity status data into AI, which then performs the filtering.

[0073] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing content. Furthermore, if the user is excited, the collection unit can prioritize collecting entertaining content. Furthermore, if the user is tired, the collection unit can prioritize collecting content that can be consumed in a short time. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of data to be collected to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and determine the priority of the data to be collected by the AI.

[0074] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, if the user is traveling, the collection unit can also collect tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the collection unit can also collect local news and event information related to that city. For example, if the user is in a specific area, the collection unit can collect news and event information related to that area. Furthermore, if the user is traveling, the collection unit can also collect tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the collection unit can also collect local news and event information related to that city. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can then prioritize collecting highly relevant data.

[0075] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, if a user frequently posts about a particular topic, the collection unit can collect information related to the topic. Furthermore, if a user frequently uses a particular hashtag, the collection unit can also collect information related to the hashtag. Furthermore, if a user follows a particular account, the collection unit can also collect information related to the account. For example, if a user frequently posts about a particular topic, the collection unit can collect information related to the topic. Furthermore, if a user frequently uses a particular hashtag, the collection unit can also collect information related to the hashtag. Furthermore, if a user follows a particular account, the collection unit can also collect information related to the account. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-described 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 social media activity data into AI, which then collects related data.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing relaxing content. Furthermore, if the user is excited, the analysis unit can prioritize analyzing entertaining content. Furthermore, if the user is tired, the analysis unit can prioritize analyzing content that can be consumed in a short time. For example, the analysis unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may then adjust the analysis algorithm.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit evaluates the importance based on the frequency of occurrence and relevance of the data, and performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance and an appropriate level of detail on data of medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI, which can adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can use text mining to analyze news data. The analysis unit can also use sentiment analysis to analyze social media data. Furthermore, the analysis unit can also use image recognition technology to analyze video data. For example, the analysis unit can use text mining to analyze news data and extract important information. The analysis unit can also use sentiment analysis to understand user emotions in social media data. Furthermore, the analysis unit can also use image recognition technology to extract important scenes in video data. This makes it possible to apply an appropriate analysis method depending on the data 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 data category into AI, which then applies an appropriate analysis method.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may then adjust the display method of the analysis results.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. Furthermore, the analysis unit can also analyze data that has been submitted recently with a moderate priority. For example, the analysis unit can determine the priority based on the date and time of data submission, and prioritize analysis of the most recent data. The analysis unit can also postpone analysis of older data, and analyze data that has been submitted recently with a moderate priority. In this way, the priority of analysis can be determined based on the time of data 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 data submission into AI, and the AI ​​can determine the priority of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. For example, the analysis unit evaluates relevance based on the co-occurrence frequency and correlation of the data, and prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance and analyze data with moderate relevance in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0082] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. For example, if the user is stressed, the extraction unit can prioritize extracting relaxing information. Furthermore, if the user is excited, the extraction unit can prioritize extracting entertaining information. Furthermore, if the user is tired, the extraction unit can prioritize extracting information that can be consumed in a short time. For example, the extraction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The extraction unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the extraction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of information to be extracted to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input user emotion data into AI and determine the priority of information to be extracted by the AI.

[0083] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between data during extraction. For example, the extraction unit groups and extracts highly related data. The extraction unit can also exclude and extract data with low relevance. Furthermore, the extraction unit can analyze the interrelationships between data and extract the most related information. For example, the extraction unit evaluates the relevance based on the co-occurrence frequency and correlation of data, and groups and extracts highly related data. The extraction unit can also exclude and extract data with low relevance, and analyze the interrelationships between data to extract the most related information. This can improve the accuracy of extraction by taking into account the interrelationships between data. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the interrelationships between data into AI, which can improve the accuracy of extraction.

[0084] The extraction unit may perform extraction while taking into consideration attribute information of the data submitter. For example, the extraction unit may preferentially extract data from highly reliable submitters. The extraction unit may also exclude data from less reliable submitters. The extraction unit may also extract the most relevant information based on the submitter's expertise and experience. For example, the extraction unit may preferentially extract data from highly reliable submitters while taking into consideration attribute information such as the submitter's age, gender, and occupation. The extraction unit may also exclude data from less reliable submitters and extract the most relevant information based on the submitter's expertise and experience. This allows for improved extraction accuracy based on the attribute information of the data submitter. Some or all of the above-described processing by the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input the submitter's attribute information into AI, which then performs extraction.

[0085] The extraction unit can estimate the user's emotion and adjust the display method of the extracted information based on the estimated user emotion. For example, if the user is stressed, the extraction unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. For example, the extraction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The extraction unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the extraction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the display method of information to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input user emotion data into AI, which may then adjust the way information is displayed.

[0086] The extraction unit can extract data taking into consideration the geographical distribution of the data. For example, if a user is in a specific region, the extraction unit can prioritize extracting information related to that region. Furthermore, if a user is traveling, the extraction unit can also extract tourist information and restaurant information for the travel destination. Furthermore, if a user lives in a specific city, the extraction unit can also extract local news and event information related to that city. For example, if a user is in a specific region, the extraction unit can prioritize extracting information related to that region. Furthermore, if a user is traveling, the extraction unit can also extract tourist information and restaurant information for the travel destination. Furthermore, if a user lives in a specific city, the extraction unit can also extract local news and event information related to that city. This makes it possible to extract highly relevant information based on the geographical distribution of the data. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the geographical distribution of the data into AI, which then performs the extraction.

[0087] During extraction, the extraction unit can improve the accuracy of extraction by referring to related literature of the data. The extraction unit, for example, refers to related literature and extracts the most relevant information. The extraction unit can also refer to related literature and extract highly reliable information. Furthermore, the extraction unit can also refer to related literature and extract the latest information. For example, the extraction unit refers to related literature and extracts the most relevant information. The extraction unit can also refer to related literature and extract highly reliable information. Furthermore, the extraction unit can also refer to related literature and extract the latest information. In this way, by referring to related literature of the data, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input related literature into AI, which can improve the accuracy of extraction.

[0088] The summarization unit can estimate the user's emotions and adjust the presentation style of the summary based on the estimated user emotions. For example, if the user is feeling stressed, the summarization unit can provide a simple, highly visible summary. Furthermore, if the user is relaxed, the summarization unit can provide a summary with detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a summary that focuses on the main points. For example, the summarization unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The summarization unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the summarization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the summary presentation style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit may input user emotion data into AI, which may then adjust the way the summary is presented.

[0089] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. For example, the summarization unit provides a detailed summary for information of high importance. The summarization unit can also provide a simple summary for information of low importance. Furthermore, the summarization unit can provide a summary with an appropriate level of detail for information of medium importance. For example, the summarization unit evaluates the importance of the information based on the frequency of appearance or relevance of the information, and provides a detailed summary for information of high importance. The summarization unit can also provide a simple summary for information of low importance and a summary with an appropriate level of detail for information of medium importance. In this way, the level of detail of the summary can be adjusted based on the importance of the information. Some or all of the above-described processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input the importance of the information into AI, which can adjust the level of detail of the summary.

[0090] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, for news information, the summarization unit can generate a summary using text mining. For social media information, the summarization unit can also generate a summary using sentiment analysis. For video information, the summarization unit can also generate a summary using image recognition technology. For example, for news information, the summarization unit can use text mining to generate a summary and extract important information. For social media information, the summarization unit can also grasp user emotions using sentiment analysis. For video information, the summarization unit can also extract important scenes using image recognition technology. This allows an appropriate summarization algorithm to be applied depending on the category of information. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the category of information into AI, which then applies an appropriate summarization algorithm.

[0091] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is stressed, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed information. If the user is in a hurry, the summarization unit can provide a concise, short summary. For example, the summarization unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The summarization unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the summarization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of the summary to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit may input user emotion data into AI, which may then adjust the length of the summary.

[0092] When generating summaries, the summarizing unit can determine the priority of summaries based on the submission date of the information. For example, the summarizing unit can prioritize generating summaries for the most recent information. Also, the summarizing unit can postpone generating summaries for older information. Furthermore, the summarizing unit can generate summaries with a moderate priority for information that has been submitted recently. For example, the summarizing unit can determine the priority based on the date and time of submission of the information, and prioritize generating summaries for the most recent information. Also, the summarizing unit can postpone generating summaries for older information, and generate summaries with a moderate priority for information that has been submitted recently. In this way, the priority of summaries can be determined based on the submission date of the information. 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 input the submission date of the information into AI, and the AI ​​can determine the priority of summaries.

[0093] 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 generating summaries for information with high relevance. The summarization unit can also postpone generating summaries for information with low relevance. Furthermore, the summarization unit can generate summaries in an appropriate order for information with medium relevance. For example, the summarization unit can evaluate the relevance based on the co-occurrence frequency or correlation of information, and prioritize generating summaries for information with high relevance. The summarization unit can also postpone generating summaries for information with low relevance, and generate summaries in an appropriate order for information with medium 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 using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the relevance of information into AI, and the AI ​​can adjust the order of summaries.

[0094] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the display method of information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI, which may then adjust the way information is displayed.

[0095] When providing the display method, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can prioritize and provide display methods that the user has previously preferred. The providing unit can also exclude display methods that the user has previously avoided. Furthermore, the providing unit can analyze the user's past browsing history and provide the most optimal display method. For example, the providing unit can prioritize and provide display methods that the user has previously preferred. The providing unit can also exclude display methods that the user has previously avoided and provide the most optimal display method by analyzing the user's past browsing history. This makes it possible to select the optimal display method based on the user's past browsing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past browsing history into AI, which can select the optimal display method.

[0096] The providing unit can customize the method of providing information based on the user's current activity status when providing the information. For example, if the user is at work, the providing unit can prioritize providing work-related information. Furthermore, if the user is on vacation, the providing unit can also provide travel and leisure-related information. Furthermore, if the user is working on a specific project, the providing unit can also provide information related to the project. For example, the providing unit can provide work-related news and materials while the user is at work. Furthermore, the providing unit can provide tourist information and restaurant information for travel destinations while the user is on vacation. Furthermore, if the user is working on a specific project, the providing unit can also provide technical information and reference materials related to the project. This allows the method of providing information to be customized based on the user's current activity status. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then customize the method of providing information.

[0097] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing information. Furthermore, if the user is excited, the providing unit can prioritize providing entertaining information. Furthermore, if the user is tired, the providing unit can prioritize providing information that can be consumed in a short time. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of information to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI, which may then determine the priority of the information.

[0098] The providing unit can select the optimal information provision method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can also provide tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the providing unit can also provide local news and event information related to that city. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can also provide tourist information and restaurant information for the travel destination. Furthermore, if the user lives in a specific city, the providing unit can also provide local news and event information related to that city. This makes it possible to select the optimal information provision method based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can select the optimal information provision method.

[0099] The providing unit can analyze the user's social media activity and suggest a method of providing information at the time of providing the information. For example, if the user frequently posts about a specific topic, the providing unit can prioritize providing information related to the topic. Also, if the user frequently uses a specific hashtag, the providing unit can provide information related to the hashtag. Furthermore, if the user follows a specific account, the providing unit can provide information related to the account. For example, if the user frequently posts about a specific topic, the providing unit can prioritize providing information related to the topic. Also, if the user frequently uses a specific hashtag, the providing unit can provide information related to the hashtag. Furthermore, if the user follows a specific account, the providing unit can provide information related to the account. This makes it possible to suggest a method of providing information based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can then suggest a method of providing information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a user's behavior history using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The extraction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, extracts information based on the analysis results. The summarization unit, realized, for example, by the specific processing unit 290 of the data processing device 12, summarizes the extracted information. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the summarized information to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a user's behavior history using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts information based on the analysis results. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the extracted information. 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. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, extraction unit, summarization unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects a user's behavior history using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the history to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts information based on the analysis results. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the extracted information. 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. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, extraction unit, summarization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's behavior history using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts information based on the analysis results. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the extracted information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the summarized information to the user.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The information provision system can also collect health data from the user and provide information based on the user's health condition. For example, the collection unit collects heart rate, step count, and sleep data from the user's fitness tracker or smartwatch. The collection unit can also collect calorie intake and nutrient data from the user's food recording app. The analysis unit analyzes this data to understand the user's health condition. For example, if the user is not getting enough exercise, the analysis unit can provide health-related articles and exercise programs. If the user is feeling stressed, the analysis unit can provide information on relaxation methods and stress management. This makes it possible to provide information based on the user's health condition, contributing to the maintenance and improvement of the user's health.

[0102] The collection unit can also collect a user's purchasing history and provide information based on the user's purchasing trends. For example, the collection unit collects data on products purchased by the user on an online shopping site. The collection unit can also collect receipt data on products purchased by the user in stores. The analysis unit analyzes this data to understand the user's purchasing trends. For example, if the user frequently purchases products from a particular brand or category, the analysis unit can provide new product information and sale information related to that brand or category. Also, if the user tends to purchase specific products during a particular season, the analysis unit can provide product information tailored to that season. This makes it possible to provide personalized information based on the user's purchasing trends.

[0103] The analysis unit can estimate the user's emotions and evaluate the reliability of information based on the estimated user emotions. For example, if the user is feeling anxious, information from a more reliable source can be provided preferentially. Also, if the user is relaxed, highly entertaining information can be provided. Furthermore, if the user is excited, the latest news and trend information can be provided. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to evaluate the reliability of information according to the user's emotions and provide appropriate information.

[0104] The extraction unit can customize information based on the user's hobbies and preferences. For example, if the user is interested in music, the latest music news and new release information can be provided. If the user is interested in movies, the latest movie reviews and screening schedules can be provided. Furthermore, if the user is interested in cooking, information on new recipes and cooking tips can be provided. This makes it possible to provide personalized information based on the user's hobbies and preferences. The extraction unit can analyze the user's behavioral history and the content of posts on social media to understand the user's hobbies and preferences. This makes it possible to provide the user with the information that is most interesting to them.

[0105] The summarizing unit can summarize information based on the user's learning style. For example, if the user is a visual learner, it can provide a summary using graphs and charts. If the user is an auditory learner, it can provide an audio summary. Furthermore, if the user has good reading comprehension skills, it can provide a detailed text summary. This allows the system to provide an optimal summary according to the user's learning style. The summarizing unit can analyze the user's past learning history and feedback to understand the user's learning style. This allows the system to provide the most effective summary for the user.

[0106] The providing unit can estimate the user's emotions and adjust the frequency of information provision based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of information provision can be reduced, allowing more time for relaxation. Also, if the user is excited, the frequency of information provision can be increased, quickly providing the latest news and trend information. Furthermore, if the user is tired, information that can be consumed in a short time can be provided. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to adjust the frequency of information provision according to the user's emotions.

[0107] The collection unit can collect the user's device usage status and select the optimal information provision method. For example, if the user frequently uses a smartphone, information can be provided in a mobile-friendly format. Also, if the user uses a tablet, information can be provided in a layout suitable for a large screen. Furthermore, if the user uses a desktop, information can be provided in a layout including detailed information. This makes it possible to select the optimal information provision method based on the user's device usage status. The collection unit can analyze the user's device usage history and determine which device the user uses most frequently. This makes it possible to provide information in a format that is most user-friendly for the user.

[0108] The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize collecting relaxing content. Also, if the user is excited, it can collect highly entertaining content. Furthermore, if the user is tired, it can collect content that can be consumed in a short time. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to adjust the information collection method according to the user's emotions.

[0109] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple and highly visible notification method can be provided. If the user is relaxed, a notification method including detailed information can be provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points can be provided. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to adjust the notification method of the analysis results according to the user's emotions.

[0110] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user emotions. For example, if the user is feeling stressed, information can be provided at a time when the user is able to relax. Also, if the user is excited, information can be provided immediately. Furthermore, if the user is tired, information can be provided after the user has rested. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to adjust the timing of providing information according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects the user's behavioral history. The user's behavioral history includes website browsing history, app usage history, and content posted on social media. For example, the collection unit records the URLs of websites frequently visited by the user and the browsing time, collects app usage history, and records the number of times the app is launched and the usage time. Furthermore, the collection unit collects content posted on social media and records the text, images, comments, etc. of the post. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and text mining technology. For example, the analysis unit performs statistical analysis on the collected data to understand the user's interests and concerns. It can also analyze the data using machine learning algorithms to understand the user's behavioral patterns. It can also use text mining technology to analyze the content of social media posts to understand the user's emotions and concerns. Step 3: The extraction unit extracts information based on the analysis results obtained by the analysis unit. Information extraction is performed by methods such as keyword extraction and selection of highly relevant information. For example, the extraction unit extracts relevant keywords based on the user's interests and selects highly relevant information. It can also filter information based on the user's interests to extract only the necessary information. Step 4: The Summarizer summarizes the information extracted by the Extractor. Summarization is based on the length of the text and the importance of the information being summarized. For example, the Summarizer can summarize a long news article in a short form or combine information from multiple sources. It can also extract important information and summarize it succinctly. Step 5: The providing unit provides the information summarized by the summarizing unit to the user. The information is provided by a notification, an email, a dashboard display, or the like. For example, the providing unit may provide the summarized information to the user as a notification and send it by email. Furthermore, the summarized information may be displayed on a dashboard so that the user can access it at any time.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] 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.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user behavior history; an analysis unit that analyzes the data collected by the collection unit; an extraction unit that extracts information based on the analysis result obtained by the analysis unit; a summarizing unit that summarizes the information extracted by the extracting unit; a providing unit that provides the information summarized by the summarizing unit. A system characterized by:

2. The collecting unit Collecting the user's frequently visited websites or app usage history, and the content posted on social media 2. The system of claim 1.

3. The analysis unit Analyzing the data collected by the collection unit to understand the user's interests and concerns 2. The system of claim 1.

4. The extraction unit Extract information based on user interests 2. The system of claim 1.

5. The summary section Summarize the extracted information 2. The system of claim 1.

6. The providing unit Providing summarized information to users 2. The system of claim 1.

7. The collecting unit Inferring user sentiment and adjusting the type of data collected based on the estimated user sentiment 2. The system of claim 1.

8. The collecting unit Analyze users' past behavioral history and select the appropriate collection timing 2. The system of claim 1.

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