System

Abstract

An object of the system according to the embodiment is to centrally manage information on recommended idols and contents and to appropriately provide the information based on a user's preference.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a recommendation unit, a reminder unit, and a training unit. The collection unit collects information from online content or digital media. The analysis unit analyzes the information collected by the collection unit. The providing unit provides information based on the information analyzed by the analyzing unit. The recommendation unit recommends the recommendation based on the user's preference or taste. The reminding unit performs reminding of distribution or ticket reservation. The fostering unit provides advice or activity proposal for fostering from LightFan to CoreFan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to centrally manage information about favorite idols and content and provide it appropriately based on users' preferences.

[0005] The system according to the embodiment aims to centrally manage information about favorite idols and content, and provide it appropriately based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a recommendation unit, a reminder unit, and a training unit. The collection unit collects information from online content or digital media. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the information analyzed by the analysis unit. The recommendation unit recommends favorites based on the user's preferences and hobbies. The reminder unit reminds users to make streams or ticket reservations. The training unit provides advice or suggests activities to develop Light Fans into Core Fans. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage information about favorite idols and content, and provide it appropriately based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support activity management system according to an embodiment of the present invention centrally manages information about a favorite idol and provides users with optimal information and advice. The support activity management system collects and analyzes information from online content and digital media and provides it to users. It also recommends favorite idols based on the user's preferences and hobbies, and provides reminders for streaming and ticket reservations. It also provides advice and activity suggestions to develop the user from a light fan to a core fan. For example, in the support activity management system, a user registers information about their favorite idol. The support activity management system then uses a generation AI to collect and analyze information from online content and digital media. This information is provided every two hours, three times a day, or twice a day, depending on the user's settings. The generation AI also uses the information it provides to provide advice and activity suggestions to develop the user from a light fan to a core fan. It also provides information on iconic light fan and core fan activities (models). Furthermore, as a support activity concierge, it also provides reminders for streaming and ticket reservations. Finally, for users who want to increase their number of favorite idols, the generation AI will recommend the latest favorites based on the user's preferences and hobbies. This allows the Oshikatsu Management System to centrally manage information about favorite idols and provide users with the most appropriate information and advice, making their Oshikatsu activities more fulfilling. This allows the Oshikatsu Management System to provide users with the most appropriate information and advice. For example, users can find new favorite idols, broadening the scope of their Oshikatsu activities. The Oshikatsu Management System also supports users' Oshikatsu activities, making them more fulfilling.

[0029] A support activity management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a recommendation unit, a reminder unit, and a development unit. The collection unit collects information from online content or digital media. For example, the collection unit collects information using web scraping technology. The collection unit can also acquire data using an API. The collection unit can also collect information using an RSS feed. For example, the collection unit periodically collects information from a specific website and stores it in a database. When using an API, the collection unit acquires data from a specific service and stores it for analysis. When using an RSS feed, the collection unit subscribes to the feed and acquires data whenever new information is added. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the information using text analysis technology. The analysis unit can also analyze the information using image analysis technology. The analysis unit can also analyze the information using data mining technology. For example, the analysis unit analyzes the collected text data using natural language processing technology to extract important information. When image analysis technology is used, the analysis unit analyzes collected image data and extracts information from the images. When data mining technology is used, the analysis unit discovers patterns and trends from large amounts of data. The provision unit provides information based on the information analyzed by the analysis unit. The provision unit provides the information using, for example, a notification function. The provision unit can also provide the information in report format. The provision unit can also provide the information using a dashboard. For example, the provision unit sends push notifications to the user to notify them of important information. When providing the information in report format, the provision unit periodically generates reports and sends them to the user. When using a dashboard, the provision unit allows the user to check the information in real time. The recommendation unit recommends a favorite based on the user's preferences and hobbies. The recommendation unit recommends a favorite based on the user's preferences, hobbies, and tastes, for example. The recommendation unit recommends a favorite based on, for example, a recommendation algorithm. The recommendation unit can also recommend a favorite based on the user's past behavioral data. The recommendation unit can also recommend a favorite based on social media data.For example, the recommendation unit may recommend related favorites based on content that the user has previously rated highly. When using a recommendation algorithm, the recommendation unit selects the most suitable favorite based on the user's preferences and hobbies. When using social media data, the recommendation unit analyzes the accounts the user follows and the posts the user has "liked" to recommend related favorites. The reminder unit provides reminders for streams or ticket reservations. For example, the reminder unit may provide reminders using email notifications. The reminder unit may also provide reminders using in-app notifications. The reminder unit may also provide reminders using SMS. For example, the reminder unit may send an email notification to the user before the stream starts so that the user does not miss the stream. When using in-app notifications, the reminder unit displays a reminder when the app is opened. When using SMS, the reminder unit sends a reminder message to the user's mobile phone. The development unit provides advice and suggests activities to develop Light Fans into Core Fans. The development unit may provide development through the provision of benefits, for example. The training unit can also train users through event information. Furthermore, the training unit can train users through the provision of personalized content. For example, the training unit can provide benefits to users who meet certain conditions, encouraging them to become Core Fans. When training users through event information, the training unit provides users with event information about their favorite idols and encourages them to participate. When training users through the provision of personalized content, the training unit provides content customized based on the user's preferences and hobbies. This allows the support activity management system according to the embodiment to provide users with optimal information and advice.

[0030] The collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the collection unit can prioritize collecting information from sites frequently visited by the user. For example, the collection unit can analyze the user's browsing history and collect information from specific sites. The collection unit can also collect related information based on content that the user has previously rated highly. For example, the collection unit can analyze user rating data and collect related information. Furthermore, the collection unit can adjust the type of information to be collected during a specific time period based on the user's browsing history. For example, the collection unit can analyze the type of content the user views during a specific time period and collect information appropriate for that time period. This enables optimal information collection based on the user's past browsing history. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's browsing history data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting information, the collection unit can filter the information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information about idols in which the user is currently interested. For example, the collection unit analyzes the user's search history and collects information about the idols of interest. The collection unit can also collect information related to keywords recently searched by the user. For example, the collection unit collects related information based on the user's search keywords. Furthermore, the collection unit can prioritize collecting posts from social media accounts followed by the user. For example, the collection unit analyzes posts from accounts followed by the user and collects related information. This allows for filtering information based on the user's current areas of interest, thereby providing more relevant information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's search history data into a generation AI and cause the generation AI to perform filtering based on the user's areas of interest.

[0032] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. For example, the collection unit analyzes the text entered by the user and collects related information. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit analyzes images uploaded by the user and collects related information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects event information related to that area. For example, the collection unit acquires the user's geographical location information and collects event information held in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination. For example, the collection unit collects tourist spot and restaurant information for the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting nearby event information. For example, the collection unit collects nearby event information based on the user's geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information data into a generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting posts from accounts the user follows. For example, the collection unit analyzes the user's social media accounts and collects posts from the followed accounts. The collection unit can also collect information related to posts the user has "liked." For example, the collection unit analyzes the user's "like" history and collects related information. Furthermore, the collection unit can also collect information related to posts on which the user has commented. For example, the collection unit analyzes the user's comment history and collects related information. This makes it possible to provide highly relevant information based on the user's social media activities. 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 social media data into a generation AI and cause the generation AI to collect related information.

[0035] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit may prioritize collecting information from information sources that the user has rated highly. For example, the collection unit may analyze the user's feedback and collect information from information sources that the user has rated highly. The collection unit may also collect information by avoiding information sources that the user has rated poorly. For example, the collection unit may collect information by excluding information sources that the user has rated poorly based on the user's feedback. Furthermore, the collection unit may adjust the type of information to be collected based on the user's feedback. For example, the collection unit may analyze the user's feedback and prioritize collecting information in a specific category. This enables the provision of more appropriate information by customizing the collection method based on the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of highly important information to provide deep insights. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis if it determines that the importance is high. The analysis unit can also perform a concise analysis of low-importance information to provide only the main points. For example, the analysis unit evaluates the importance of the information and performs a concise analysis if it determines that the importance is low. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for information of medium importance. For example, the analysis unit evaluates the importance of the information and performs an analysis with a moderate level of detail if it determines that the importance is medium. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described 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 information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a sentiment analysis algorithm to social media posts. For example, the analysis unit analyzes the social media posts and applies a sentiment analysis algorithm to estimate emotions. The analysis unit can also apply a content analysis algorithm to web articles. For example, the analysis unit analyzes the web articles and applies a content analysis algorithm to extract important information. The analysis unit can also apply a viewer reaction analysis algorithm to live broadcasts. For example, the analysis unit analyzes comments and reactions of viewers of the live broadcast and applies a reaction analysis algorithm to evaluate the viewer reactions. This allows for applying an appropriate analysis algorithm depending on the category of information, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information category data into a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit applies a similar analysis method based on analysis results that the user previously rated highly. For example, the analysis unit analyzes the user's past analysis results and reapplies the analysis method that the user previously rated highly. The analysis unit can also apply a different analysis method, avoiding analysis results that the user previously rated poorly. For example, the analysis unit analyzes the user's past analysis results and avoids the analysis method that the user previously rated poorly. Furthermore, the analysis unit can learn from the user's past analysis results and optimize the analysis algorithm. For example, the analysis unit tunes the analysis algorithm based on the user's past analysis results to improve accuracy. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on when the information was collected. The analysis unit, for example, prioritizes analyzing the most recent information and provides it immediately. For example, the analysis unit evaluates when the information was collected and prioritizes analyzing the most recent information. The analysis unit can also determine the analysis priority for past information based on its importance. For example, the analysis unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the analysis unit can collectively analyze information collected during a specific period and identify trends. For example, the analysis unit analyzes information collected during a specific period and identifies trends. This allows for determining the analysis priority based on when the information was collected, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information that is of high interest to the user. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information that is of high interest to the user. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit evaluates the relevance of the information and postpones analysis of less relevant information. Furthermore, the analysis unit can group highly relevant information and analyze it all at once. For example, the analysis unit groups highly relevant information and analyzes it all at once. This allows for adjusting the order of analysis based on the relevance of the information, thereby providing more appropriate analysis results. 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 information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit evaluates the user's level of expertise and, if it determines that the user has technical expertise, provides analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide concise analysis results that avoid technical terminology. For example, the analysis unit evaluates the user's level of expertise and, if it determines that the user is a beginner, provides analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the analysis unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described 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 user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] When providing information, the providing unit can adjust the level of detail of the information provided based on the importance of the information. For example, the providing unit provides a detailed explanation for information of high importance. For example, the providing unit evaluates the importance of the information and provides a detailed explanation if it determines that the importance is high. The providing unit can also provide a concise explanation for information of low importance. For example, the providing unit evaluates the importance of the information and provides a concise explanation if it determines that the importance is low. Furthermore, the providing unit can provide information of medium importance with an appropriate level of detail. For example, the providing unit evaluates the importance of the information and provides the information with an appropriate level of detail if it determines that the importance is medium. This enables more appropriate information to be provided by adjusting the level of detail of the information provided based on the importance of the 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 information importance data to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0043] When providing information, the providing unit can apply different providing algorithms depending on the category of the information. For example, for social media posts, the providing unit provides information based on sentiment analysis results. For example, the providing unit analyzes the social media posts and provides information based on sentiment analysis results. The providing unit can also provide information for web articles based on content analysis results. For example, the providing unit analyzes the web articles and provides information based on content analysis results. Furthermore, for live broadcasts, the providing unit can also provide information based on viewer reaction analysis results. For example, the providing unit analyzes comments and reactions of viewers of the live broadcast and provides information based on the reaction analysis results. This enables more accurate information provision by applying an appropriate providing algorithm depending on the category of the information. Some or all of the above-mentioned 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 information category data to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0044] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past provision results. For example, the providing unit provides information in a similar manner based on an information provision method that the user has previously rated highly. For example, the providing unit analyzes the user's past provision results and reapplies the information provision method that the user has previously rated highly. The providing unit can also provide information in a different manner, avoiding information provision methods that the user has previously rated poorly. For example, the providing unit analyzes the user's past provision results and avoids information provision methods that the user has previously rated poorly. Furthermore, the providing unit can learn the user's past provision results and optimize the provision algorithm. For example, the providing unit tunes the provision algorithm based on the user's past provision results and improves accuracy. This improves the accuracy of the information provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0045] When providing information, the providing unit can determine the priority of provision based on the time when the information was collected. For example, the providing unit can prioritize providing the most recent information. For example, the providing unit can evaluate the time when the information was collected and prioritize providing the most recent information. The providing unit can also determine the priority of provision for past information based on its importance. For example, the providing unit can evaluate the time when the information was collected and its importance and determine the priority. Furthermore, the providing unit can provide information collected over a specific period in a consolidated manner to grasp trends. For example, the providing unit can provide information collected over a specific period to grasp trends. This enables more appropriate information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information collection time data to the generation AI and have the generation AI determine the priority of provision.

[0046] When providing information, the providing unit can adjust the order of providing information based on the relevance of the information. For example, the providing unit prioritizes providing information that is of high interest to the user. For example, the providing unit evaluates the relevance of information and prioritizes providing information that is of high interest to the user. The providing unit can also postpone providing information with low relevance. For example, the providing unit evaluates the relevance of information and postpones the provision of information with low relevance. Furthermore, the providing unit can group highly relevant information and provide it all at once. For example, the providing unit groups highly relevant information and provides it all at once. This enables more appropriate information to be provided by adjusting the order of providing information based on the relevance of the 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 relevance data of information to a generation AI and cause the generation AI to adjust the order of providing.

[0047] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. For example, the providing unit evaluates the user's level of expertise and, if it determines that the user has technical expertise, provides information that uses a lot of technical terminology. Furthermore, if the user is a beginner, the providing unit can provide concise information that avoids technical terminology. For example, if the providing unit evaluates the user's level of expertise and determines that the user is a beginner, it provides information that avoids technical terminology. Furthermore, the providing unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the providing unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This enables more appropriate information to be provided by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the information when making a recommendation. The recommendation unit, for example, provides a detailed explanation for information of high importance. For example, the recommendation unit evaluates the importance of the information and provides a detailed explanation if it determines that the importance is high. The recommendation unit can also provide a concise explanation for information of low importance. For example, the recommendation unit evaluates the importance of the information and provides a concise explanation if it determines that the importance is low. The recommendation unit can also provide information of medium importance with a moderate level of detail. For example, the recommendation unit evaluates the importance of the information and provides a moderate level of detail if it determines that the importance is medium. This enables more appropriate recommendations by adjusting the level of detail of the recommendation based on the importance of the information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information importance data to a generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0049] The recommendation unit can apply different recommendation algorithms depending on the category of information when making a recommendation. For example, for social media posts, the recommendation unit makes recommendations based on sentiment analysis results. For example, the recommendation unit analyzes social media posts and makes recommendations based on sentiment analysis results. The recommendation unit can also make recommendations for web articles based on content analysis results. For example, the recommendation unit analyzes web articles and makes recommendations based on content analysis results. Furthermore, the recommendation unit can also make recommendations for live broadcasts based on viewer reaction analysis results. For example, the recommendation unit analyzes comments and reactions of viewers of the live broadcast and makes recommendations based on the reaction analysis results. This enables more accurate recommendations by applying an appropriate recommendation algorithm depending on the category of information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information category data into a generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0050] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit makes similar recommendations based on recommendation results that the user has previously given high ratings. For example, the recommendation unit analyzes the user's past recommendation results and re-applies the highly rated recommendation results. The recommendation unit can also make different recommendations by avoiding recommendation results that the user has previously given low ratings. For example, the recommendation unit analyzes the user's past recommendation results and avoids the lowly rated recommendation results. Furthermore, the recommendation unit can learn the user's past recommendation results and optimize the recommendation algorithm. For example, the recommendation unit tunes the recommendation algorithm based on the user's past recommendation results and improves accuracy. As a result, the accuracy of recommendations is improved by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0051] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on when the information was collected. For example, the recommendation unit prioritizes recommending the most recent information. For example, the recommendation unit evaluates when the information was collected and prioritizes recommending the most recent information. The recommendation unit can also determine the priority of recommendations for past information based on its importance. For example, the recommendation unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the recommendation unit can collectively recommend information collected during a specific period and identify trends. For example, the recommendation unit recommends information collected during a specific period and identifies trends. This enables more appropriate recommendations by determining the priority of recommendations based on when the information was collected. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information collection time data to a generation AI and have the generation AI determine the recommendation priority.

[0052] The recommendation unit can adjust the order of recommendations based on the relevance of the information when making a recommendation. For example, the recommendation unit prioritizes recommending information that is of high interest to the user. For example, the recommendation unit evaluates the relevance of the information and prioritizes recommending information that is of high interest to the user. The recommendation unit can also postpone recommending information with low relevance. For example, the recommendation unit evaluates the relevance of the information and postpones the recommendation of information with low relevance. Furthermore, the recommendation unit can group highly relevant information and recommend it all at once. For example, the recommendation unit groups highly relevant information and recommends it all at once. This enables more appropriate recommendations by adjusting the order of recommendations based on the relevance of the information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of recommendations.

[0053] The recommendation unit can adjust the use of technical terminology in recommendations according to the user's level of expertise when making recommendations. For example, if the user has technical expertise, the recommendation unit makes recommendations that use a lot of technical terminology. For example, the recommendation unit evaluates the user's level of expertise and determines that the user has technical expertise, and makes recommendations that use a lot of technical terminology. Furthermore, if the user is a beginner, the recommendation unit can make concise recommendations that avoid technical terminology. For example, the recommendation unit evaluates the user's level of expertise and determines that the user is a beginner, and makes recommendations that avoid technical terminology. Furthermore, the recommendation unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the recommendation unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This enables more appropriate recommendations by adjusting the use of technical terminology in recommendations according to the user's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The reminding unit can adjust the level of detail of the reminder based on the importance of the information when reminding. For example, the reminding unit provides a detailed reminder for information of high importance. For example, the reminding unit evaluates the importance of the information and provides a detailed reminder if it is determined to be high. The reminding unit can also provide a brief reminder for information of low importance. For example, the reminding unit evaluates the importance of the information and provides a brief reminder if it is determined to be low. Furthermore, the reminding unit can also provide a reminder with an appropriate level of detail for information of medium importance. For example, the reminding unit evaluates the importance of the information and provides a reminder with an appropriate level of detail if it is determined to be medium. This allows for more appropriate reminders by adjusting the level of detail of the reminder based on the importance of the information. Some or all of the above-described processing in the reminding unit may be performed using, or without, AI. For example, the reminding unit can input information importance data into the generation AI and cause the generation AI to adjust the level of detail of the reminder.

[0055] The reminding unit can apply different reminding algorithms depending on the category of information when reminding. For example, for a broadcast, the reminding unit can issue a reminder before the broadcast starts. For example, the reminding unit can evaluate the start time of the broadcast and issue a reminder before it starts. Furthermore, for a ticket reservation, the reminding unit can issue a reminder before the reservation starts. For example, the reminding unit can evaluate the start time of the ticket reservation and issue a reminder before it starts. Furthermore, for an event, the reminding unit can issue a reminder before the event starts. For example, the reminding unit can evaluate the start time of the event and issue a reminder before it starts. This allows for more accurate reminders by applying an appropriate reminding algorithm depending on the category of information. Some or all of the above-mentioned processing in the reminding unit may be performed using AI, for example, or without AI. For example, the reminding unit can input information category data into the generation AI and cause the generation AI to apply an appropriate reminding algorithm.

[0056] The reminding unit can improve the accuracy of reminding by referring to the user's past reminding results. For example, the reminding unit performs reminding using a similar method based on a reminding method that the user has previously rated highly. For example, the reminding unit analyzes the user's past reminding results and reapplies the highly rated reminding method. The reminding unit can also perform reminding using a different method, avoiding reminding methods that the user has previously rated poorly. For example, the reminding unit analyzes the user's past reminding results and avoids reminding methods that the user has previously rated poorly. Furthermore, the reminding unit can learn the user's past reminding results and optimize the reminding algorithm. For example, the reminding unit tunes the reminding algorithm based on the user's past reminding results to improve accuracy. As a result, the accuracy of reminding is improved by referring to the user's past reminding results. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's past reminder result data into the generation AI and have the generation AI improve the accuracy of reminders.

[0057] The reminding unit can determine the priority of reminders based on when the information was collected. For example, the reminding unit prioritizes reminders of the most recent information. For example, the reminding unit evaluates when the information was collected and prioritizes reminders of the most recent information. The reminding unit can also determine the priority of reminders for past information based on its importance. For example, the reminding unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the reminding unit can collectively remind information collected during a specific period and grasp trends. For example, the reminding unit reminds information collected during a specific period and grasps trends. This enables more appropriate reminders by determining the priority of reminders based on when the information was collected. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without AI. For example, the reminding unit can input information collection time data into the generation AI and have the generation AI determine the priority of reminders.

[0058] The reminding unit can adjust the order of reminders based on the relevance of the information when reminding. For example, the reminding unit prioritizes reminding of information that is of high interest to the user. For example, the reminding unit evaluates the relevance of the information and prioritizes reminding of information that is of high interest to the user. The reminding unit can also postpone reminding of less relevant information. For example, the reminding unit evaluates the relevance of the information and postpones less relevant information. Furthermore, the reminding unit can group highly relevant information and remind the user all at once. For example, the reminding unit groups highly relevant information and reminds the user all at once. This allows for more appropriate reminders by adjusting the order of reminders based on the relevance of the information. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of reminders.

[0059] During training, the training unit can adjust the level of detail of the training based on the importance of the information. For example, the training unit provides a detailed explanation for information of high importance. For example, the training unit evaluates the importance of the information and provides a detailed explanation if it is determined to be high. The training unit can also provide a concise explanation for information of low importance. For example, the training unit evaluates the importance of the information and provides a concise explanation if it is determined to be low. Furthermore, the training unit can provide information of medium importance with an appropriate level of detail. For example, the training unit evaluates the importance of the information and provides an appropriate level of detail if it is determined to be medium. This enables more appropriate training by adjusting the level of detail of the training based on the importance of the information. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the training.

[0060] The training unit can apply different training algorithms depending on the category of information during training. For example, for social media posts, the training unit performs training based on emotion analysis results. For example, the training unit analyzes the social media posts and performs training based on emotion analysis results. The training unit can also perform training based on content analysis results for web articles. For example, the training unit analyzes the web articles and performs training based on content analysis results. Furthermore, for live broadcasts, the training unit can also perform training based on viewer reaction analysis results. For example, the training unit analyzes comments and reactions from viewers of the live broadcast and performs training based on reaction analysis results. This enables more accurate training by applying an appropriate training algorithm depending on the category of information. Some or all of the above-mentioned processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input information category data into the generation AI and cause the generation AI to apply an appropriate training algorithm.

[0061] During training, the training unit can improve the accuracy of training by referring to the user's past training results. For example, the training unit performs training using a similar method based on a training method that the user previously rated highly. For example, the training unit analyzes the user's past training results and reapplies the highly rated training method. The training unit can also perform training using a different method, avoiding training methods that the user previously rated poorly. For example, the training unit analyzes the user's past training results and avoids training methods that the user previously rated poorly. Furthermore, the training unit can learn the user's past training results and optimize the training algorithm. For example, the training unit tunes the training algorithm based on the user's past training results to improve accuracy. This improves the accuracy of training by referring to the user's past training results. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit can input the user's past training result data into the generation AI and cause the generation AI to improve the accuracy of training.

[0062] During training, the training unit can determine training priorities based on the time when information was collected. For example, the training unit prioritizes the most recent information for training. For example, the training unit evaluates the time when information was collected and prioritizes the most recent information for training. The training unit can also determine training priorities for past information based on its importance. For example, the training unit evaluates the time when information was collected and its importance and determines the priority. Furthermore, the training unit can compile information collected over a specific period and use it for training to understand trends. For example, the training unit uses information collected over a specific period for training to understand trends. This enables more appropriate training by determining training priorities based on the time when information was collected. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input information collection time data into the generation AI and have the generation AI determine the training priorities.

[0063] During training, the training unit can adjust the order of training based on the relevance of the information. For example, the training unit prioritizes the use of information that is of high interest to the user for training. For example, the training unit evaluates the relevance of the information and prioritizes the use of information that is of high interest to the user for training. The training unit can also postpone the use of less relevant information for training. For example, the training unit evaluates the relevance of the information and postpones the less relevant information. Furthermore, the training unit can group highly relevant information and use it collectively for training. For example, the training unit groups highly relevant information and uses it collectively for training. This enables more appropriate training by adjusting the order of training based on the relevance of the information. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of training.

[0064] During training, the training unit can adjust the use of technical terminology in the training according to the user's level of expertise. For example, if the user has technical expertise, the training unit may use a lot of technical terminology in the training. For example, if the training unit evaluates the user's level of expertise and determines that the user has technical expertise, the training unit may use a lot of technical terminology in the training. Furthermore, if the user is a beginner, the training unit may avoid technical terminology and provide concise training. For example, if the training unit evaluates the user's level of expertise and determines that the user is a beginner, the training unit may avoid technical terminology in the training. Furthermore, the training unit may adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the training unit may evaluate the user's level of knowledge and adjust the use of appropriate technical terminology. This allows for more appropriate training by adjusting the use of technical terminology in the training according to the user's level of expertise. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The idol activity management system can further include a learning management unit that manages the user's learning progress. The learning management unit can monitor the content and progress of the user's studies and suggest idol activities related to the study. For example, if the user is studying a specific language, the learning management unit can recommend idol content related to that language. Also, if the user has an exam coming up, the learning management unit can suggest relaxing content to improve concentration. Furthermore, the learning management unit can adjust the frequency of idol activities according to the user's learning progress. This makes it possible to balance study and idol activities.

[0067] The oshikatsu management system can further include a feedback collection unit that collects user feedback. The feedback collection unit can collect feedback provided by users and reflect it in oshikatsu suggestions. For example, if a user gives a high rating to a particular piece of content, the feedback collection unit can recommend related content based on that information. Also, if a user gives a low rating, the feedback collection unit can adjust the recommendations based on that information. Furthermore, the feedback collection unit can analyze user feedback and optimize oshikatsu suggestions. This makes it possible to suggest oshikatsu that meet the user's preferences.

[0068] The idol-supporting management system can further include a social activity management unit that monitors the user's social activities. The social activity management unit can monitor the user's social media activities and event participation status, and make idol-supporting suggestions based on the user's social activities. For example, if the user participates in a particular event, the social activity management unit can recommend content related to that event. Also, if the user posts frequently about a particular idol on social media, information related to that idol can be provided preferentially. Furthermore, the social activity management unit can adjust the frequency of idol-supporting activities according to the user's social activities. This makes it possible to make idol-supporting suggestions that take the user's social activities into consideration.

[0069] The idol-loving activity management system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit can make suggestions for idol-loving activities based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the hobby analysis unit can recommend content about idols related to that sport. Also, if the user is interested in a particular music genre, the hobby analysis unit can provide information about idols related to that genre. Furthermore, the hobby analysis unit can adjust the frequency of idol-loving activities according to the user's hobbies and interests. This makes it possible to suggest idol-loving activities that take the user's hobbies and interests into consideration.

[0070] The oshikatsu management system can further include a lifestyle rhythm management unit that monitors the user's lifestyle rhythm. The lifestyle rhythm management unit can make oshikatsu suggestions based on the user's lifestyle rhythm. For example, if the user is a nocturnal person, it can recommend content that can be enjoyed at night. Also, if the user is a morning person, it can provide content that is suitable for the morning. Furthermore, the lifestyle rhythm management unit can adjust the frequency of oshikatsu according to the user's lifestyle rhythm. This makes it possible to make oshikatsu suggestions that take the user's lifestyle rhythm into consideration.

[0071] The oshikatsu management system can further include a fitness management unit that monitors the user's fitness activities. The fitness management unit can make oshikatsu suggestions based on the user's fitness activities. For example, if the user is exercising, the fitness management unit can recommend content that can be enjoyed while exercising. Also, if the user wants to relax after exercising, the fitness management unit can provide relaxing content. Furthermore, the fitness management unit can adjust the frequency of oshikatsu according to the user's fitness activities. This makes it possible to make oshikatsu suggestions that take the user's fitness activities into consideration.

[0072] The idol activity management system can further include a dietary management unit that monitors the user's eating patterns. The dietary management unit can make idol activity suggestions based on the user's eating patterns. For example, if the user enjoys a particular meal, it can recommend content related to that meal. Also, if the user eats a healthy diet, it can provide health-related idol information. Furthermore, the dietary management unit can adjust the frequency of idol activity according to the user's eating patterns. This makes it possible to suggest idol activities that take the user's eating patterns into consideration.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects information from online content or digital media. For example, the collection unit may use web scraping technology, APIs, or RSS feeds to collect information. Information may be periodically collected from specific websites and stored in a database, or data may be obtained from specific services and stored for analysis. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using text analysis technology, image analysis technology, and data mining technology. For example, it can use natural language processing technology to analyze text data and extract important information, or it can analyze image data and extract information within the image. Step 3: The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides information using a notification function, a report format, or a dashboard. For example, the providing unit can send push notifications to the user to notify them of important information, or generate periodic reports and send them to the user. Step 4: The recommendation unit recommends favorites based on the user's preferences and hobbies. The recommendation unit recommends favorites based on a recommendation algorithm, the user's past behavioral data, and social media data. For example, it can recommend related favorites based on content that the user has previously rated highly. Step 5: The reminder section will remind the user to stream or book tickets. The reminder section will use email notifications, in-app notifications, and SMS to remind the user. For example, it can send an email notification to the user before the stream starts so that the user does not miss the stream. Step 6: The Nurturing Department provides advice and suggests activities to develop Light Fans into Core Fans. The Nurturing Department cultivates Light Fans by offering rewards, event information, and personalized content. For example, rewards can be offered to users who meet certain conditions to help them develop into Core Fans.

[0075] (Example 2) The support activity management system according to an embodiment of the present invention centrally manages information about a favorite idol and provides users with optimal information and advice. The support activity management system collects and analyzes information from online content and digital media and provides it to users. It also recommends favorite idols based on the user's preferences and hobbies, and provides reminders for streaming and ticket reservations. It also provides advice and activity suggestions to develop the user from a light fan to a core fan. For example, in the support activity management system, a user registers information about their favorite idol. The support activity management system then uses a generation AI to collect and analyze information from online content and digital media. This information is provided every two hours, three times a day, or twice a day, depending on the user's settings. The generation AI also uses the information it provides to provide advice and activity suggestions to develop the user from a light fan to a core fan. It also provides information on iconic light fan and core fan activities (models). Furthermore, as a support activity concierge, it also provides reminders for streaming and ticket reservations. Finally, for users who want to increase their number of favorite idols, the generation AI will recommend the latest favorites based on the user's preferences and hobbies. This allows the Oshikatsu Management System to centrally manage information about favorite idols and provide users with the most appropriate information and advice, making their Oshikatsu activities more fulfilling. This allows the Oshikatsu Management System to provide users with the most appropriate information and advice. For example, users can find new favorite idols, broadening the scope of their Oshikatsu activities. The Oshikatsu Management System also supports users' Oshikatsu activities, making them more fulfilling.

[0076] A support activity management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a recommendation unit, a reminder unit, and a development unit. The collection unit collects information from online content or digital media. For example, the collection unit collects information using web scraping technology. The collection unit can also acquire data using an API. The collection unit can also collect information using an RSS feed. For example, the collection unit periodically collects information from a specific website and stores it in a database. When using an API, the collection unit acquires data from a specific service and stores it for analysis. When using an RSS feed, the collection unit subscribes to the feed and acquires data whenever new information is added. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the information using text analysis technology. The analysis unit can also analyze the information using image analysis technology. The analysis unit can also analyze the information using data mining technology. For example, the analysis unit analyzes the collected text data using natural language processing technology to extract important information. When image analysis technology is used, the analysis unit analyzes collected image data and extracts information from the images. When data mining technology is used, the analysis unit discovers patterns and trends from large amounts of data. The provision unit provides information based on the information analyzed by the analysis unit. The provision unit provides the information using, for example, a notification function. The provision unit can also provide the information in report format. The provision unit can also provide the information using a dashboard. For example, the provision unit sends push notifications to the user to notify them of important information. When providing the information in report format, the provision unit periodically generates reports and sends them to the user. When using a dashboard, the provision unit allows the user to check the information in real time. The recommendation unit recommends a favorite based on the user's preferences and hobbies. The recommendation unit recommends a favorite based on the user's preferences, hobbies, and tastes, for example. The recommendation unit recommends a favorite based on, for example, a recommendation algorithm. The recommendation unit can also recommend a favorite based on the user's past behavioral data. The recommendation unit can also recommend a favorite based on social media data.For example, the recommendation unit may recommend related favorites based on content that the user has previously rated highly. When using a recommendation algorithm, the recommendation unit selects the most suitable favorite based on the user's preferences and hobbies. When using social media data, the recommendation unit analyzes the accounts the user follows and the posts the user has "liked" to recommend related favorites. The reminder unit provides reminders for streams or ticket reservations. For example, the reminder unit may provide reminders using email notifications. The reminder unit may also provide reminders using in-app notifications. The reminder unit may also provide reminders using SMS. For example, the reminder unit may send an email notification to the user before the stream starts so that the user does not miss the stream. When using in-app notifications, the reminder unit displays a reminder when the app is opened. When using SMS, the reminder unit sends a reminder message to the user's mobile phone. The development unit provides advice and suggests activities to develop Light Fans into Core Fans. The development unit may provide development through the provision of benefits, for example. The training unit can also train users through event information. Furthermore, the training unit can train users through the provision of personalized content. For example, the training unit can provide benefits to users who meet certain conditions, encouraging them to become Core Fans. When training users through event information, the training unit provides users with event information about their favorite idols and encourages them to participate. When training users through the provision of personalized content, the training unit provides content customized based on the user's preferences and hobbies. This allows the support activity management system according to the embodiment to provide users with optimal information and advice.

[0077] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit records the user's voice and estimates the emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotions. This allows the collection unit to adjust the timing of information collection according to the user's emotions. For example, if the user is excited, the collection unit can collect information in real time and provide it immediately. Also, if the user is relaxed, the collection unit can collect information every two hours and provide it all at once. Furthermore, if the user is stressed, the collection unit can reduce the frequency of information collection and provide it once a day. This allows more appropriate information to be provided by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0078] The collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the collection unit can prioritize collecting information from sites frequently visited by the user. For example, the collection unit can analyze the user's browsing history and collect information from specific sites. The collection unit can also collect related information based on content that the user has previously rated highly. For example, the collection unit can analyze user rating data and collect related information. Furthermore, the collection unit can adjust the type of information to be collected during a specific time period based on the user's browsing history. For example, the collection unit can analyze the type of content the user views during a specific time period and collect information appropriate for that time period. This enables optimal information collection based on the user's past browsing history. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's browsing history data into a generation AI and have the generation AI select the optimal collection method.

[0079] When collecting information, the collection unit can filter the information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information about idols in which the user is currently interested. For example, the collection unit analyzes the user's search history and collects information about the idols of interest. The collection unit can also collect information related to keywords recently searched by the user. For example, the collection unit collects related information based on the user's search keywords. Furthermore, the collection unit can prioritize collecting posts from social media accounts followed by the user. For example, the collection unit analyzes posts from accounts followed by the user and collects related information. This allows for filtering information based on the user's current areas of interest, thereby providing more relevant information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's search history data into a generation AI and cause the generation AI to perform filtering based on the user's areas of interest.

[0080] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit records the user's voice and converts it into text data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. For example, the collection unit analyzes the text entered by the user and collects related information. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit analyzes images uploaded by the user and collects related information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0081] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest information. For example, if the collection unit estimates the user's emotions and determines that the user is excited, it collects the latest news and trending information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting past popular articles. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it collects articles that have received high ratings in the past. Furthermore, if the user is stressed, the collection unit can prioritize collecting relaxing content. For example, if the collection unit estimates the user's emotions and determines that the user is stressed, it collects relaxing music and videos. This enables more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the information.

[0082] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects event information related to that area. For example, the collection unit acquires the user's geographical location information and collects event information held in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination. For example, the collection unit collects tourist spot and restaurant information for the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting nearby event information. For example, the collection unit collects nearby event information based on the user's geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information data into a generation AI and cause the generation AI to collect highly relevant information.

[0083] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting posts from accounts the user follows. For example, the collection unit analyzes the user's social media accounts and collects posts from the followed accounts. The collection unit can also collect information related to posts the user has "liked." For example, the collection unit analyzes the user's "like" history and collects related information. Furthermore, the collection unit can also collect information related to posts on which the user has commented. For example, the collection unit analyzes the user's comment history and collects related information. This makes it possible to provide highly relevant information based on the user's social media activities. 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 social media data into a generation AI and cause the generation AI to collect related information.

[0084] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit may prioritize collecting information from information sources that the user has rated highly. For example, the collection unit may analyze the user's feedback and collect information from information sources that the user has rated highly. The collection unit may also collect information by avoiding information sources that the user has rated poorly. For example, the collection unit may collect information by excluding information sources that the user has rated poorly based on the user's feedback. Furthermore, the collection unit may adjust the type of information to be collected based on the user's feedback. For example, the collection unit may analyze the user's feedback and prioritize collecting information in a specific category. This enables the provision of more appropriate information by customizing the collection method based on the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit displays the analysis results using visually stimulating graphics. For example, if the analysis unit estimates the user's emotions and determines that the user is excited, it displays the analysis results using colorful, dynamic graphics. Furthermore, if the user is relaxed, the analysis unit can display the analysis results using graphics in subdued colors. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it displays the analysis results using graphics in subdued colors. Furthermore, if the user is stressed, the analysis unit can display the analysis results using simple, highly visible graphics. For example, if the analysis unit estimates the user's emotions and determines that the user is stressed, it displays the analysis results using simple, highly visible graphics. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. 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 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 the generation AI and have the generation AI adjust the method of expression of the analysis.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of highly important information to provide deep insights. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis if it determines that the importance is high. The analysis unit can also perform a concise analysis of low-importance information to provide only the main points. For example, the analysis unit evaluates the importance of the information and performs a concise analysis if it determines that the importance is low. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for information of medium importance. For example, the analysis unit evaluates the importance of the information and performs an analysis with a moderate level of detail if it determines that the importance is medium. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described 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 information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a sentiment analysis algorithm to social media posts. For example, the analysis unit analyzes the social media posts and applies a sentiment analysis algorithm to estimate emotions. The analysis unit can also apply a content analysis algorithm to web articles. For example, the analysis unit analyzes the web articles and applies a content analysis algorithm to extract important information. The analysis unit can also apply a viewer reaction analysis algorithm to live broadcasts. For example, the analysis unit analyzes comments and reactions of viewers of the live broadcast and applies a reaction analysis algorithm to evaluate the viewer reactions. This allows for applying an appropriate analysis algorithm depending on the category of information, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information category data into a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit applies a similar analysis method based on analysis results that the user previously rated highly. For example, the analysis unit analyzes the user's past analysis results and reapplies the analysis method that the user previously rated highly. The analysis unit can also apply a different analysis method, avoiding analysis results that the user previously rated poorly. For example, the analysis unit analyzes the user's past analysis results and avoids the analysis method that the user previously rated poorly. Furthermore, the analysis unit can learn from the user's past analysis results and optimize the analysis algorithm. For example, the analysis unit tunes the analysis algorithm based on the user's past analysis results to improve accuracy. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed analysis result. Furthermore, the analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the analysis unit estimates the user's emotions and determines that the user is excited, it provides a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide a more appropriate analysis result. 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 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 to the generation AI and have the generation AI adjust the length of the analysis.

[0090] During analysis, the analysis unit can determine the analysis priority based on when the information was collected. The analysis unit, for example, prioritizes analyzing the most recent information and provides it immediately. For example, the analysis unit evaluates when the information was collected and prioritizes analyzing the most recent information. The analysis unit can also determine the analysis priority for past information based on its importance. For example, the analysis unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the analysis unit can collectively analyze information collected during a specific period and identify trends. For example, the analysis unit analyzes information collected during a specific period and identifies trends. This allows for determining the analysis priority based on when the information was collected, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI determine the analysis priority.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information that is of high interest to the user. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information that is of high interest to the user. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit evaluates the relevance of the information and postpones analysis of less relevant information. Furthermore, the analysis unit can group highly relevant information and analyze it all at once. For example, the analysis unit groups highly relevant information and analyzes it all at once. This allows for adjusting the order of analysis based on the relevance of the information, thereby providing more appropriate analysis results. 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 information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit evaluates the user's level of expertise and, if it determines that the user has technical expertise, provides analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide concise analysis results that avoid technical terminology. For example, the analysis unit evaluates the user's level of expertise and, if it determines that the user is a beginner, provides analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the analysis unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described 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 user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0093] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit provides information in a visually stimulating manner. For example, if the providing unit estimates the user's emotions and determines that the user is excited, it provides information using colorful and dynamic graphics. The providing unit can also provide information in a calming manner if the user is relaxed. For example, if the providing unit estimates the user's emotions and determines that the user is relaxed, it provides information using graphics in subdued colors. Furthermore, if the user is feeling stressed, the providing unit can also provide information in a simple, highly visible manner. For example, if the providing unit estimates the user's emotions and determines that the user is feeling stressed, it provides information using simple, highly visible graphics. This allows the method of providing information to be adjusted according to the user's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is realized 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 to the generating AI and cause the generating AI to adjust the method of providing information.

[0094] When providing information, the providing unit can adjust the level of detail of the information provided based on the importance of the information. For example, the providing unit provides a detailed explanation for information of high importance. For example, the providing unit evaluates the importance of the information and provides a detailed explanation if it determines that the importance is high. The providing unit can also provide a concise explanation for information of low importance. For example, the providing unit evaluates the importance of the information and provides a concise explanation if it determines that the importance is low. Furthermore, the providing unit can provide information of medium importance with an appropriate level of detail. For example, the providing unit evaluates the importance of the information and provides the information with an appropriate level of detail if it determines that the importance is medium. This enables more appropriate information to be provided by adjusting the level of detail of the information provided based on the importance of the 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 information importance data to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0095] When providing information, the providing unit can apply different providing algorithms depending on the category of the information. For example, for social media posts, the providing unit provides information based on sentiment analysis results. For example, the providing unit analyzes the social media posts and provides information based on sentiment analysis results. The providing unit can also provide information for web articles based on content analysis results. For example, the providing unit analyzes the web articles and provides information based on content analysis results. Furthermore, for live broadcasts, the providing unit can also provide information based on viewer reaction analysis results. For example, the providing unit analyzes comments and reactions of viewers of the live broadcast and provides information based on the reaction analysis results. This enables more accurate information provision by applying an appropriate providing algorithm depending on the category of the information. Some or all of the above-mentioned 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 information category data to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0096] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past provision results. For example, the providing unit provides information in a similar manner based on an information provision method that the user has previously rated highly. For example, the providing unit analyzes the user's past provision results and reapplies the information provision method that the user has previously rated highly. The providing unit can also provide information in a different manner, avoiding information provision methods that the user has previously rated poorly. For example, the providing unit analyzes the user's past provision results and avoids information provision methods that the user has previously rated poorly. Furthermore, the providing unit can learn the user's past provision results and optimize the provision algorithm. For example, the providing unit tunes the provision algorithm based on the user's past provision results and improves accuracy. This improves the accuracy of the information provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0097] The providing unit can estimate the user's emotions and adjust the length of information provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit provides short, concise information. For example, if the providing unit estimates the user's emotions and determines that the user is in a hurry, it provides short, concise information. The providing unit can also provide detailed information if the user is relaxed. For example, if the providing unit estimates the user's emotions and determines that the user is relaxed, it provides detailed information. Furthermore, the providing unit can also provide visually stimulating information if the user is excited. For example, if the providing unit estimates the user's emotions and determines that the user is excited, it provides visually stimulating information. This allows for adjusting the length of information provided according to the user's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the length of information provided.

[0098] When providing information, the providing unit can determine the priority of provision based on the time when the information was collected. For example, the providing unit can prioritize providing the most recent information. For example, the providing unit can evaluate the time when the information was collected and prioritize providing the most recent information. The providing unit can also determine the priority of provision for past information based on its importance. For example, the providing unit can evaluate the time when the information was collected and its importance and determine the priority. Furthermore, the providing unit can provide information collected over a specific period in a consolidated manner to grasp trends. For example, the providing unit can provide information collected over a specific period to grasp trends. This enables more appropriate information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information collection time data to the generation AI and have the generation AI determine the priority of provision.

[0099] When providing information, the providing unit can adjust the order of providing information based on the relevance of the information. For example, the providing unit prioritizes providing information that is of high interest to the user. For example, the providing unit evaluates the relevance of information and prioritizes providing information that is of high interest to the user. The providing unit can also postpone providing information with low relevance. For example, the providing unit evaluates the relevance of information and postpones the provision of information with low relevance. Furthermore, the providing unit can group highly relevant information and provide it all at once. For example, the providing unit groups highly relevant information and provides it all at once. This enables more appropriate information to be provided by adjusting the order of providing information based on the relevance of the 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 relevance data of information to a generation AI and cause the generation AI to adjust the order of providing.

[0100] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. For example, the providing unit evaluates the user's level of expertise and, if it determines that the user has technical expertise, provides information that uses a lot of technical terminology. Furthermore, if the user is a beginner, the providing unit can provide concise information that avoids technical terminology. For example, if the providing unit evaluates the user's level of expertise and determines that the user is a beginner, it provides information that avoids technical terminology. Furthermore, the providing unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the providing unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This enables more appropriate information to be provided by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0101] The recommendation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user emotions. For example, if the user is excited, the recommendation unit makes recommendations in a visually stimulating manner. For example, if the recommendation unit estimates the user's emotions and determines that the user is excited, it makes recommendations using colorful and dynamic graphics. The recommendation unit can also make recommendations in a calming manner if the user is relaxed. For example, if the recommendation unit estimates the user's emotions and determines that the user is relaxed, it makes recommendations using graphics in subdued colors. Furthermore, if the user is stressed, the recommendation unit can make recommendations in a simple and highly visible manner. For example, if the recommendation unit estimates the user's emotions and determines that the user is stressed, it makes recommendations using simple and highly visible graphics. This enables more appropriate recommendations by adjusting the way recommendations are expressed based on the user's emotions. Emotion estimation is realized 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 recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input user emotion data to the generation AI and cause the generation AI to adjust the way the recommendation is expressed.

[0102] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the information when making a recommendation. The recommendation unit, for example, provides a detailed explanation for information of high importance. For example, the recommendation unit evaluates the importance of the information and provides a detailed explanation if it determines that the importance is high. The recommendation unit can also provide a concise explanation for information of low importance. For example, the recommendation unit evaluates the importance of the information and provides a concise explanation if it determines that the importance is low. The recommendation unit can also provide information of medium importance with a moderate level of detail. For example, the recommendation unit evaluates the importance of the information and provides a moderate level of detail if it determines that the importance is medium. This enables more appropriate recommendations by adjusting the level of detail of the recommendation based on the importance of the information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information importance data to a generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0103] The recommendation unit can apply different recommendation algorithms depending on the category of information when making a recommendation. For example, for social media posts, the recommendation unit makes recommendations based on sentiment analysis results. For example, the recommendation unit analyzes social media posts and makes recommendations based on sentiment analysis results. The recommendation unit can also make recommendations for web articles based on content analysis results. For example, the recommendation unit analyzes web articles and makes recommendations based on content analysis results. Furthermore, the recommendation unit can also make recommendations for live broadcasts based on viewer reaction analysis results. For example, the recommendation unit analyzes comments and reactions of viewers of the live broadcast and makes recommendations based on the reaction analysis results. This enables more accurate recommendations by applying an appropriate recommendation algorithm depending on the category of information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information category data into a generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0104] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results. For example, the recommendation unit makes similar recommendations based on recommendation results that the user has previously given high ratings. For example, the recommendation unit analyzes the user's past recommendation results and re-applies the highly rated recommendation results. The recommendation unit can also make different recommendations by avoiding recommendation results that the user has previously given low ratings. For example, the recommendation unit analyzes the user's past recommendation results and avoids the lowly rated recommendation results. Furthermore, the recommendation unit can learn the user's past recommendation results and optimize the recommendation algorithm. For example, the recommendation unit tunes the recommendation algorithm based on the user's past recommendation results and improves accuracy. As a result, the accuracy of recommendations is improved by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0105] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. For example, if the user is in a hurry, the recommendation unit can provide a short and to-the-point recommendation. For example, if the recommendation unit estimates the user's emotions and determines that the user is in a hurry, the recommendation unit can provide a short and to-the-point recommendation. The recommendation unit can also provide a detailed recommendation if the user is relaxed. For example, if the recommendation unit estimates the user's emotions and determines that the user is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, the recommendation unit can provide a visually stimulating recommendation if the user is excited. For example, if the recommendation unit estimates the user's emotions and determines that the user is excited, the recommendation unit can provide a visually stimulating recommendation. This allows for adjusting the length of the recommendation according to the user's emotions, thereby enabling more appropriate recommendations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the recommendation.

[0106] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on when the information was collected. For example, the recommendation unit prioritizes recommending the most recent information. For example, the recommendation unit evaluates when the information was collected and prioritizes recommending the most recent information. The recommendation unit can also determine the priority of recommendations for past information based on its importance. For example, the recommendation unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the recommendation unit can collectively recommend information collected during a specific period and identify trends. For example, the recommendation unit recommends information collected during a specific period and identifies trends. This enables more appropriate recommendations by determining the priority of recommendations based on when the information was collected. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information collection time data to a generation AI and have the generation AI determine the recommendation priority.

[0107] The recommendation unit can adjust the order of recommendations based on the relevance of the information when making a recommendation. For example, the recommendation unit prioritizes recommending information that is of high interest to the user. For example, the recommendation unit evaluates the relevance of the information and prioritizes recommending information that is of high interest to the user. The recommendation unit can also postpone recommending information with low relevance. For example, the recommendation unit evaluates the relevance of the information and postpones the recommendation of information with low relevance. Furthermore, the recommendation unit can group highly relevant information and recommend it all at once. For example, the recommendation unit groups highly relevant information and recommends it all at once. This enables more appropriate recommendations by adjusting the order of recommendations based on the relevance of the information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of recommendations.

[0108] The recommendation unit can adjust the use of technical terminology in recommendations according to the user's level of expertise when making recommendations. For example, if the user has technical expertise, the recommendation unit makes recommendations that use a lot of technical terminology. For example, the recommendation unit evaluates the user's level of expertise and determines that the user has technical expertise, and makes recommendations that use a lot of technical terminology. Furthermore, if the user is a beginner, the recommendation unit can make concise recommendations that avoid technical terminology. For example, the recommendation unit evaluates the user's level of expertise and determines that the user is a beginner, and makes recommendations that avoid technical terminology. Furthermore, the recommendation unit can adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the recommendation unit evaluates the user's level of knowledge and adjusts the use of appropriate technical terminology. This enables more appropriate recommendations by adjusting the use of technical terminology in recommendations according to the user's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0109] The reminding unit can estimate the user's emotions and adjust the reminding method based on the estimated user emotions. For example, if the user is excited, the reminding unit can remind the user in a visually stimulating manner. For example, if the reminding unit estimates the user's emotions and determines that the user is excited, it can remind the user using colorful and dynamic graphics. Furthermore, if the user is relaxed, the reminding unit can remind the user in a calming manner. For example, if the reminding unit estimates the user's emotions and determines that the user is relaxed, it can remind the user using graphics in calm colors. Furthermore, if the user is stressed, the reminding unit can remind the user in a simple and highly visible manner. For example, if the reminding unit estimates the user's emotions and determines that the user is stressed, it can remind the user using simple and highly visible graphics. This allows for more appropriate reminders by adjusting the reminding method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit may input user emotion data into the generating AI and cause the generating AI to adjust the reminding method.

[0110] The reminding unit can adjust the level of detail of the reminder based on the importance of the information when reminding. For example, the reminding unit provides a detailed reminder for information of high importance. For example, the reminding unit evaluates the importance of the information and provides a detailed reminder if it is determined to be high. The reminding unit can also provide a brief reminder for information of low importance. For example, the reminding unit evaluates the importance of the information and provides a brief reminder if it is determined to be low. Furthermore, the reminding unit can also provide a reminder with an appropriate level of detail for information of medium importance. For example, the reminding unit evaluates the importance of the information and provides a reminder with an appropriate level of detail if it is determined to be medium. This allows for more appropriate reminders by adjusting the level of detail of the reminder based on the importance of the information. Some or all of the above-described processing in the reminding unit may be performed using, or without, AI. For example, the reminding unit can input information importance data into the generation AI and cause the generation AI to adjust the level of detail of the reminder.

[0111] The reminding unit can apply different reminding algorithms depending on the category of information when reminding. For example, for a broadcast, the reminding unit can issue a reminder before the broadcast starts. For example, the reminding unit can evaluate the start time of the broadcast and issue a reminder before it starts. Furthermore, for a ticket reservation, the reminding unit can issue a reminder before the reservation starts. For example, the reminding unit can evaluate the start time of the ticket reservation and issue a reminder before it starts. Furthermore, for an event, the reminding unit can issue a reminder before the event starts. For example, the reminding unit can evaluate the start time of the event and issue a reminder before it starts. This allows for more accurate reminders by applying an appropriate reminding algorithm depending on the category of information. Some or all of the above-mentioned processing in the reminding unit may be performed using AI, for example, or without AI. For example, the reminding unit can input information category data into the generation AI and cause the generation AI to apply an appropriate reminding algorithm.

[0112] The reminding unit can improve the accuracy of reminding by referring to the user's past reminding results. For example, the reminding unit performs reminding using a similar method based on a reminding method that the user has previously rated highly. For example, the reminding unit analyzes the user's past reminding results and reapplies the highly rated reminding method. The reminding unit can also perform reminding using a different method, avoiding reminding methods that the user has previously rated poorly. For example, the reminding unit analyzes the user's past reminding results and avoids reminding methods that the user has previously rated poorly. Furthermore, the reminding unit can learn the user's past reminding results and optimize the reminding algorithm. For example, the reminding unit tunes the reminding algorithm based on the user's past reminding results to improve accuracy. As a result, the accuracy of reminding is improved by referring to the user's past reminding results. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's past reminder result data into the generation AI and have the generation AI improve the accuracy of reminders.

[0113] The reminder unit can estimate the user's emotions and adjust the length of the reminder based on the estimated user's emotions. For example, if the user is in a hurry, the reminder unit provides a short and to-the-point reminder. For example, if the reminder unit estimates the user's emotions and determines that the user is in a hurry, it provides a short and to-the-point reminder. The reminder unit can also provide a detailed reminder if the user is relaxed. For example, if the reminder unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed reminder. Furthermore, the reminder unit can provide a visually stimulating reminder if the user is excited. For example, if the reminder unit estimates the user's emotions and determines that the user is excited, it provides a visually stimulating reminder. This allows for more appropriate reminders by adjusting the length of the reminder according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit may input user emotion data into the generating AI and cause the generating AI to adjust the length of the reminder.

[0114] The reminding unit can determine the priority of reminders based on when the information was collected. For example, the reminding unit prioritizes reminders of the most recent information. For example, the reminding unit evaluates when the information was collected and prioritizes reminders of the most recent information. The reminding unit can also determine the priority of reminders for past information based on its importance. For example, the reminding unit evaluates when the information was collected and its importance and determines the priority. Furthermore, the reminding unit can collectively remind information collected during a specific period and grasp trends. For example, the reminding unit reminds information collected during a specific period and grasps trends. This enables more appropriate reminders by determining the priority of reminders based on when the information was collected. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without AI. For example, the reminding unit can input information collection time data into the generation AI and have the generation AI determine the priority of reminders.

[0115] The reminding unit can adjust the order of reminders based on the relevance of the information when reminding. For example, the reminding unit prioritizes reminding of information that is of high interest to the user. For example, the reminding unit evaluates the relevance of the information and prioritizes reminding of information that is of high interest to the user. The reminding unit can also postpone reminding of less relevant information. For example, the reminding unit evaluates the relevance of the information and postpones less relevant information. Furthermore, the reminding unit can group highly relevant information and remind the user all at once. For example, the reminding unit groups highly relevant information and reminds the user all at once. This allows for more appropriate reminders by adjusting the order of reminders based on the relevance of the information. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of reminders.

[0116] The training unit can estimate the user's emotions and adjust the training method based on the estimated user's emotions. For example, if the user is excited, the training unit suggests an active activity. For example, if the training unit estimates the user's emotions and determines that the user is excited, it suggests an active activity. The training unit can also suggest a calm activity if the user is relaxed. For example, if the training unit estimates the user's emotions and determines that the user is relaxed, it suggests a calm activity. Furthermore, if the user is feeling stressed, the training unit can suggest a relaxing activity. For example, if the training unit estimates the user's emotions and determines that the user is stressed, it suggests a relaxing activity. This enables more appropriate training by adjusting the training method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or without AI. For example, the training unit can input the user's emotional data into the generation AI and have the generation AI adjust the training method.

[0117] During training, the training unit can adjust the level of detail of the training based on the importance of the information. For example, the training unit provides a detailed explanation for information of high importance. For example, the training unit evaluates the importance of the information and provides a detailed explanation if it is determined to be high. The training unit can also provide a concise explanation for information of low importance. For example, the training unit evaluates the importance of the information and provides a concise explanation if it is determined to be low. Furthermore, the training unit can provide information of medium importance with an appropriate level of detail. For example, the training unit evaluates the importance of the information and provides an appropriate level of detail if it is determined to be medium. This enables more appropriate training by adjusting the level of detail of the training based on the importance of the information. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the training.

[0118] The training unit can apply different training algorithms depending on the category of information during training. For example, for social media posts, the training unit performs training based on emotion analysis results. For example, the training unit analyzes the social media posts and performs training based on emotion analysis results. The training unit can also perform training based on content analysis results for web articles. For example, the training unit analyzes the web articles and performs training based on content analysis results. Furthermore, for live broadcasts, the training unit can also perform training based on viewer reaction analysis results. For example, the training unit analyzes comments and reactions from viewers of the live broadcast and performs training based on reaction analysis results. This enables more accurate training by applying an appropriate training algorithm depending on the category of information. Some or all of the above-mentioned processing in the training unit may be performed using AI, for example, or without AI. For example, the training unit can input information category data into the generation AI and cause the generation AI to apply an appropriate training algorithm.

[0119] During training, the training unit can improve the accuracy of training by referring to the user's past training results. For example, the training unit performs training using a similar method based on a training method that the user previously rated highly. For example, the training unit analyzes the user's past training results and reapplies the highly rated training method. The training unit can also perform training using a different method, avoiding training methods that the user previously rated poorly. For example, the training unit analyzes the user's past training results and avoids training methods that the user previously rated poorly. Furthermore, the training unit can learn the user's past training results and optimize the training algorithm. For example, the training unit tunes the training algorithm based on the user's past training results to improve accuracy. This improves the accuracy of training by referring to the user's past training results. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit can input the user's past training result data into the generation AI and cause the generation AI to improve the accuracy of training.

[0120] The training unit can estimate the user's emotions and adjust the length of the training based on the estimated user's emotions. For example, if the user is in a hurry, the training unit provides short, concise training. For example, if the training unit estimates the user's emotions and determines that the user is in a hurry, the training unit provides short, concise training. The training unit can also provide detailed training if the user is relaxed. For example, if the training unit estimates the user's emotions and determines that the user is relaxed, the training unit provides detailed training. Furthermore, if the user is excited, the training unit can provide visually stimulating training. For example, if the training unit estimates the user's emotions and determines that the user is excited, the training unit provides visually stimulating training. This allows for more appropriate training by adjusting the length of the training according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the training unit can be performed using, for example, an AI, or without an AI. For example, the training unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the training.

[0121] During training, the training unit can determine training priorities based on the time when information was collected. For example, the training unit prioritizes the most recent information for training. For example, the training unit evaluates the time when information was collected and prioritizes the most recent information for training. The training unit can also determine training priorities for past information based on its importance. For example, the training unit evaluates the time when information was collected and its importance and determines the priority. Furthermore, the training unit can compile information collected over a specific period and use it for training to understand trends. For example, the training unit uses information collected over a specific period for training to understand trends. This enables more appropriate training by determining training priorities based on the time when information was collected. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input information collection time data into the generation AI and have the generation AI determine the training priorities.

[0122] During training, the training unit can adjust the order of training based on the relevance of the information. For example, the training unit prioritizes the use of information that is of high interest to the user for training. For example, the training unit evaluates the relevance of the information and prioritizes the use of information that is of high interest to the user for training. The training unit can also postpone the use of less relevant information for training. For example, the training unit evaluates the relevance of the information and postpones the less relevant information. Furthermore, the training unit can group highly relevant information and use it collectively for training. For example, the training unit groups highly relevant information and uses it collectively for training. This enables more appropriate training by adjusting the order of training based on the relevance of the information. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of training.

[0123] During training, the training unit can adjust the use of technical terminology in the training according to the user's level of expertise. For example, if the user has technical expertise, the training unit may use a lot of technical terminology in the training. For example, if the training unit evaluates the user's level of expertise and determines that the user has technical expertise, the training unit may use a lot of technical terminology in the training. Furthermore, if the user is a beginner, the training unit may avoid technical terminology and provide concise training. For example, if the training unit evaluates the user's level of expertise and determines that the user is a beginner, the training unit may avoid technical terminology in the training. Furthermore, the training unit may adjust the use of appropriate technical terminology according to the user's level of knowledge. For example, the training unit may evaluate the user's level of knowledge and adjust the use of appropriate technical terminology. This allows for more appropriate training by adjusting the use of technical terminology in the training according to the user's level of expertise. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, recommendation unit, remind unit, and development unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information from online content and digital media using the camera 42 and microphone 38B of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the analyzed information to the user. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and recommends a favorite artist based on the user's preferences and hobbies. The remind unit is implemented, for example, by the control unit 46A of the smart device 14 and provides reminders for broadcasts and ticket reservations. The development unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides advice and activity suggestions to develop a Light Fan into a Core Fan. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, recommendation unit, remind unit, and development unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information from online content and digital media using the camera 42 and microphone 238 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the analyzed information to the user. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a favorite item based on the user's preferences and hobbies. The reminder unit is realized, for example, by the control unit 46A of the smart glasses 214 and performs distribution and ticket reservation reminders. The development unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice and suggests activities to develop a Light Fan into a Core Fan. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, recommendation unit, remind unit, and development 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 information from online content and digital media using the camera 42 and microphone 238 of the headset-type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the analyzed information to the user. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a favorite based on the user's preferences and hobbies. The reminder unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and performs distribution and ticket reservation reminders. The development unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides advice and suggests activities to develop a Light Fan into a Core Fan. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, recommendation unit, remind unit, and training 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 information from online content and digital media using the camera 42 and microphone 238 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the analyzed information to the user. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends a favorite idol based on the user's preferences and hobbies. The remind unit is realized, for example, by the control unit 46A of the robot 414 and reminds the user to stream or book tickets. The training unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice and activity suggestions to develop the user from a Light Fan to a Core Fan.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The oshikatsu management system can further include a health management unit that monitors the user's health condition. The health management unit can monitor the user's heart rate and sleep patterns and adjust oshikatsu suggestions based on the user's health condition. For example, if the user is tired, the health management unit can recommend relaxing content. Also, if the user is energetic, it can suggest active activities. Furthermore, the health management unit can adjust the frequency of oshikatsu based on the user's health condition. This makes it possible to suggest oshikatsu that take the user's health into consideration.

[0126] The idol activity management system can further include a learning management unit that manages the user's learning progress. The learning management unit can monitor the content and progress of the user's studies and suggest idol activities related to the study. For example, if the user is studying a specific language, the learning management unit can recommend idol content related to that language. Also, if the user has an exam coming up, the learning management unit can suggest relaxing content to improve concentration. Furthermore, the learning management unit can adjust the frequency of idol activities according to the user's learning progress. This makes it possible to balance study and idol activities.

[0127] The oshikatsu management system can further include a feedback collection unit that collects user feedback. The feedback collection unit can collect feedback provided by users and reflect it in oshikatsu suggestions. For example, if a user gives a high rating to a particular piece of content, the feedback collection unit can recommend related content based on that information. Also, if a user gives a low rating, the feedback collection unit can adjust the recommendations based on that information. Furthermore, the feedback collection unit can analyze user feedback and optimize oshikatsu suggestions. This makes it possible to suggest oshikatsu that meet the user's preferences.

[0128] The idol-supporting management system can further include a social activity management unit that monitors the user's social activities. The social activity management unit can monitor the user's social media activities and event participation status, and make idol-supporting suggestions based on the user's social activities. For example, if the user participates in a particular event, the social activity management unit can recommend content related to that event. Also, if the user posts frequently about a particular idol on social media, information related to that idol can be provided preferentially. Furthermore, the social activity management unit can adjust the frequency of idol-supporting activities according to the user's social activities. This makes it possible to make idol-supporting suggestions that take the user's social activities into consideration.

[0129] The idol-loving activity management system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit can make suggestions for idol-loving activities based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the hobby analysis unit can recommend content about idols related to that sport. Also, if the user is interested in a particular music genre, the hobby analysis unit can provide information about idols related to that genre. Furthermore, the hobby analysis unit can adjust the frequency of idol-loving activities according to the user's hobbies and interests. This makes it possible to suggest idol-loving activities that take the user's hobbies and interests into consideration.

[0130] The oshikatsu management system can further include an emotion management unit that estimates the user's emotions and adjusts oshikatsu suggestions based on the estimated user emotions. The emotion management unit can monitor the user's emotions in real time and make oshikatsu suggestions based on the emotions. For example, if the user is sad, the emotion management unit can recommend content to cheer them up. Also, if the user is happy, the emotion management unit can provide content to further enhance those emotions. Furthermore, the emotion management unit can adjust the frequency of oshikatsu according to the user's emotions. This makes it possible to suggest oshikatsu that take the user's emotions into consideration.

[0131] The oshikatsu management system can further include a lifestyle rhythm management unit that monitors the user's lifestyle rhythm. The lifestyle rhythm management unit can make oshikatsu suggestions based on the user's lifestyle rhythm. For example, if the user is a nocturnal person, it can recommend content that can be enjoyed at night. Also, if the user is a morning person, it can provide content that is suitable for the morning. Furthermore, the lifestyle rhythm management unit can adjust the frequency of oshikatsu according to the user's lifestyle rhythm. This makes it possible to make oshikatsu suggestions that take the user's lifestyle rhythm into consideration.

[0132] The oshikatsu management system can further include a stress management unit that monitors the user's stress level. The stress management unit can make oshikatsu suggestions based on the user's stress level. For example, if the user is feeling high stress, it can recommend relaxing content to reduce stress. Also, if the user's stress level is low, it can provide more stimulating content. Furthermore, the stress management unit can adjust the frequency of oshikatsu according to the user's stress level. This makes it possible to make oshikatsu suggestions that take the user's stress level into consideration.

[0133] The oshikatsu management system can further include a fitness management unit that monitors the user's fitness activities. The fitness management unit can make oshikatsu suggestions based on the user's fitness activities. For example, if the user is exercising, the fitness management unit can recommend content that can be enjoyed while exercising. Also, if the user wants to relax after exercising, the fitness management unit can provide relaxing content. Furthermore, the fitness management unit can adjust the frequency of oshikatsu according to the user's fitness activities. This makes it possible to make oshikatsu suggestions that take the user's fitness activities into consideration.

[0134] The idol activity management system can further include a dietary management unit that monitors the user's eating patterns. The dietary management unit can make idol activity suggestions based on the user's eating patterns. For example, if the user enjoys a particular meal, it can recommend content related to that meal. Also, if the user eats a healthy diet, it can provide health-related idol information. Furthermore, the dietary management unit can adjust the frequency of idol activity according to the user's eating patterns. This makes it possible to suggest idol activities that take the user's eating patterns into consideration.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects information from online content or digital media. For example, the collection unit may use web scraping technology, APIs, or RSS feeds to collect information. Information may be periodically collected from specific websites and stored in a database, or data may be obtained from specific services and stored for analysis. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using text analysis technology, image analysis technology, and data mining technology. For example, it can use natural language processing technology to analyze text data and extract important information, or it can analyze image data and extract information within the image. Step 3: The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides information using a notification function, a report format, or a dashboard. For example, the providing unit can send push notifications to the user to notify them of important information, or generate periodic reports and send them to the user. Step 4: The recommendation unit recommends favorites based on the user's preferences and hobbies. The recommendation unit recommends favorites based on a recommendation algorithm, the user's past behavioral data, and social media data. For example, it can recommend related favorites based on content that the user has previously rated highly. Step 5: The reminder section will remind the user to stream or book tickets. The reminder section will use email notifications, in-app notifications, and SMS to remind the user. For example, it can send an email notification to the user before the stream starts so that the user does not miss the stream. Step 6: The Nurturing Department provides advice and suggests activities to develop Light Fans into Core Fans. The Nurturing Department cultivates Light Fans by offering rewards, event information, and personalized content. For example, rewards can be offered to users who meet certain conditions to help them develop into Core Fans.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

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

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

[0167] In the 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.

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

[0169] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 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.

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

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 information from online content or digital media; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides information based on the information analyzed by the analyzing unit; a recommendation unit that recommends a favorite based on the user's preferences and hobbies; A reminder section that reminds users to make streaming or ticket reservations; The organization has a development department that provides advice and suggests activities to develop Light Fans into Core Fans. A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past browsing history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit As information is collected, it is filtered based on the user's current interests.

2. The system of claim 1.

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

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting information, prioritize collection of highly relevant information based on the user's geographic location information.

2. The system of claim 1.

8. The collecting unit When collecting information, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

Images