system

Abstract

The system according to this embodiment aims to provide appropriate information about one's favorite idol regardless of changes in lifestyle, and to support efficient fan activities. [Solution] The system according to the embodiment comprises a collection unit, an information collection unit, a reminder unit, and a suggestion unit. The collection unit collects lifestyle information. The information collection unit collects information related to favorites based on the lifestyle information collected by the collection unit. The reminder unit makes reminders based on the information collected by the information collection unit. The suggestion unit proposes online participation and purchase options based on the information reminded by the reminder unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems such as missing information about a favorite person or being unable to participate in an event due to changes in lifestyle.

[0005] The system according to the embodiment aims to appropriately provide information about a favorite person regardless of changes in lifestyle and support efficient fan activities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an information collection unit, a reminder unit, and a suggestion unit. The collection unit collects lifestyle information. The information collection unit collects information related to favorites based on the lifestyle information collected by the collection unit. The reminder unit makes reminders based on the information collected by the information collection unit. The suggestion unit proposes online participation and purchase options based on the information reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately provide information about one's favorite idol regardless of changes in lifestyle, and can support efficient fan activities. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The support platform according to an embodiment of the present invention is a system that provides support for continuing "oshi-katsu" (fan activities related to one's favorite idol / celebrity), a market with an estimated size of 800 billion yen, regardless of changes in work or life stage. This support platform analyzes the user's lifestyle and provides reminders to ensure that the user does not miss out on the latest information or events related to their favorite idol / celebrity. It also supports efficient fan activities by suggesting online participation and purchasing options. This enables a fulfilling fan life while maintaining a connection with one's favorite idol / celebrity, no matter how busy one is. For example, the user provides lifestyle information to the AI ​​through the app. For example, the user inputs their work schedule and family situation. This information is analyzed by the AI, and an oshi-katsu plan based on the user's lifestyle is generated. Next, the AI ​​collects the latest information and event information related to the idol / celebrity. For example, it collects information related to the idol / celebrity from SNS and news sites and summarizes and organizes it based on the user's interests. This allows the user to efficiently catch up on the latest information without missing anything. Furthermore, the AI ​​provides reminders to the user. For example, it sends notifications when an event related to the idol / celebrity is approaching to prevent the user from missing out. It also supports the user in efficiently engaging in fan activities by suggesting online participation and purchasing options. This system allows users to maintain a connection with their favorite idols and enjoy a fulfilling fan life, no matter how busy they are. For example, even busy working users can efficiently engage in fan activities as the AI ​​suggests the optimal plan for their favorite idols. Furthermore, by recommending activities that fit their budget, users can enjoy fan activities while reducing their financial burden. In addition, the AI ​​analyzes user behavior data and generates optimal event participation and merchandise purchase plans. For example, based on past behavior data, it recommends events and merchandise that the user is likely to be interested in. As a result, users receive a fan activity plan that is perfectly suited to them. In this way, the present invention utilizes AI to provide fan activity plans tailored to the user's lifestyle, supporting efficient and effective fan activities.This allows users to maintain a connection with their favorite idols and enjoy a fulfilling fan life, no matter how busy they are. The support platform can then efficiently support fan activities by collecting information about the idols based on the user's lifestyle and providing reminders and suggestions.

[0029] The support platform according to the embodiment comprises a collection unit, an information collection unit, a reminder unit, and a suggestion unit. The collection unit collects user lifestyle information. For example, the collection unit collects information such as the user's work schedule and family situation provided through the app. The collection unit can also collect information such as the user's behavior patterns, hobbies, and interests. The information collection unit collects information about the user's favorite idol based on the lifestyle information collected by the collection unit. For example, the information collection unit collects information about the idol from social media and news sites and summarizes and organizes it based on the user's interests. The information collection unit can also collect information such as event information, merchandise information, and social media posts related to the idol. The reminder unit provides reminders based on the information collected by the information collection unit. For example, the reminder unit sends a notification when an event related to the idol is approaching. The reminder unit can send notifications in the form of email, push notifications, SMS, etc. The suggestion unit proposes online participation and purchase options based on the information reminded by the reminder unit. For example, the suggestion unit proposes specific content and methods of proposing online participation and purchase options. The proposal section can, for example, suggest how to participate in an event or the types of merchandise available for purchase. This allows the support platform according to the embodiment to collect information about the user's favorite idol or idol based on their lifestyle, and to provide reminders and suggestions, thereby supporting efficient fan activities.

[0030] The data collection unit collects user lifestyle information. Specifically, it collects information such as work schedules and family circumstances that users provide through the app. For example, it can collect calendar information, task management information, and family events and appointments that users enter into the app. Furthermore, the data collection unit can also collect information such as users' behavior patterns, hobbies, and interests. This includes the user's operation history, browsing history, and search history within the app. For example, if a user frequently views news articles of a particular genre, it can be determined that they have a high level of interest in that genre. It can also collect the user's participation history in specific events and purchase history of specific products. As a result, the data collection unit can collect detailed data on users' lifestyles and interests, building a foundation for providing optimal information and service suggestions to individual users. Furthermore, the data collection unit can centrally manage this data and link it with other departments and systems as needed. For example, collected data can be stored on a cloud server and made accessible to the information collection unit and the reminder unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions can be made. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Information Gathering Department collects information about users' favorite idols / characters based on lifestyle information gathered by the main collection department. Specifically, it collects information about these idols / characters from social media and news sites, summarizing and organizing it based on the user's interests. For example, if a user is interested in a particular artist or character, the department can collect the latest news, event information, and merchandise information related to that artist or character. The Information Gathering Department uses AI to efficiently collect large amounts of information and extract important information based on the user's interests. For example, it can use natural language processing technology to analyze social media posts and grasp trends and topics related to the idols / characters. It can also collect official announcements and media reports about the idols / characters from news sites, providing reliable information. Furthermore, the Information Gathering Department summarizes and organizes the collected information based on the user's interests and provides it in a format that the user can easily understand. For example, it can display event information about the idols / characters in a calendar format or organize merchandise information in a list format. This allows the Information Gathering Department to help users efficiently obtain information about their idols / characters and support their fan activities. In addition, the Information Gathering Department regularly updates the collected information, always providing the latest information. This allows users to always stay informed and follow the latest developments regarding their idols / characters.

[0032] The Reminders Unit sends reminders based on information collected by the Information Gathering Unit. Specifically, it sends notifications when an event related to a favorite idol is approaching. For example, it can send reminder notifications to users when the date of a concert, autograph session, or online event related to their favorite idol is approaching. The Reminders Unit can send notifications in various formats, including email, push notifications, and SMS. For example, if a user has the app installed, a push notification can be used to send an immediate reminder. Furthermore, by using email or SMS, notifications can be reliably delivered to users who do not use the app. The Reminders Unit can send notifications at the optimal time based on the user's schedule and behavioral patterns. For example, the timing of notifications can be adjusted so that users receive them during work breaks or commutes. In addition, the Reminders Unit can customize the content of notifications to match the user's interests. For example, it can notify users not only about their favorite idol's event but also about related merchandise and news articles. This allows the Reminders Unit to support users in efficiently engaging in fan activities without missing important information. Furthermore, the Reminders Unit can collect user feedback and continuously improve the accuracy of notification content and timing. This allows the reminder function to provide users with optimal reminders and support their fan activities.

[0033] The suggestion department proposes online participation and purchasing options based on information reminded by the reminder department. Specifically, it proposes the specific content and methods of suggesting online participation and purchasing options. For example, it can suggest ways to participate in events featuring a favorite artist online, how to purchase event tickets, and purchasing options for related merchandise. The suggestion department can make optimal suggestions based on the user's interests. For example, if a user is a fan of a particular artist, it can suggest purchasing options for that artist's latest album or concert tickets. Also, if a user is interested in a particular character, it can suggest merchandise and event information related to that character. The suggestion department uses AI to analyze the user's interests and make optimal suggestions. For example, based on the user's past purchase and browsing history, it can predict and suggest products and services that the user might be interested in. Furthermore, the suggestion department can customize the suggestions to match the user's lifestyle. For example, if a user is busy, it can suggest options that can be easily purchased online, or if a user lives in a specific region, it can suggest event information held in that region. In this way, the suggestion department can make optimal suggestions to users and support efficient fan activities. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide users with the best possible suggestions and support their fan activities.

[0034] The data collection unit can collect user lifestyle information. For example, it can collect information such as work schedules and family circumstances provided by the user through the app. The data collection unit can also collect information such as the user's behavioral patterns, hobbies, and interests. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information provided by the user through the app into the AI, which can then analyze and collect lifestyle information. By collecting user lifestyle information, it becomes possible to provide individually optimized activity plans.

[0035] The information gathering unit can collect information about a user's favorite idol from social media and news sites, and summarize and organize it based on the user's interests. For example, the information gathering unit can collect information about a user's favorite idol from social media and news sites, and summarize and organize it based on the user's interests. The information gathering unit can also collect information about events, merchandise, and social media posts related to the idol. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the information gathering unit can input information collected from social media and news sites into a generative AI, which can then summarize and organize it. This allows for efficient information provision by summarizing and organizing information about the idol based on the user's interests.

[0036] The reminder function can send notifications when an event featuring the user's favorite artist is approaching. For example, the reminder function can send notifications when an event featuring the user's favorite artist is approaching. The reminder function can send notifications in various formats, such as email, push notifications, and SMS. Some or all of the above-described processes in the reminder function may be performed using, for example, a generative AI, or not. For example, the reminder function can use a generative AI to send notifications when an event featuring the user's favorite artist is approaching. This prevents the user from missing the event by sending notifications when the event is approaching.

[0037] The proposal department can suggest online participation and purchasing options. For example, the proposal department can suggest specific details and methods for suggesting online participation and purchasing options. For example, the proposal department can suggest how to participate in an event and the types of merchandise available for purchase. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input suggestions for online participation and purchasing options into an AI, which can then make the most appropriate suggestions. This supports users in efficiently engaging in fan activities by suggesting online participation and purchasing options.

[0038] The suggestion department can recommend fan activities that fit within a budget. For example, the suggestion department can recommend specific content and methods of suggesting fan activities that fit within a budget. For example, the suggestion department can recommend budget ranges and types of fan activities. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input recommendations for fan activities that fit within a budget into an AI, and the AI ​​can make the optimal recommendation. This allows fans to enjoy their fan activities while reducing their financial burden by recommending fan activities that fit within a budget.

[0039] The information gathering unit can analyze user behavior data and generate optimal event participation and merchandise purchase plans. For example, the information gathering unit can analyze user behavior data and generate optimal event participation and merchandise purchase plans. For example, the information gathering unit can recommend events and merchandise that users are likely to be interested in based on past behavior data. Some or all of the above processing in the information gathering unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the information gathering unit can input user behavior data into a generating AI, and the generating AI can generate optimal event participation and merchandise purchase plans. In this way, by analyzing user behavior data, optimal event participation and merchandise purchase plans can be provided.

[0040] The data collection unit can analyze the user's past lifestyle information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on the user's past lifestyle information. For example, the data collection unit can analyze the user's past behavioral patterns and determine the optimal data collection timing. For example, the data collection unit can determine the priority of information to collect based on the user's past lifestyle information. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past lifestyle information into AI, which can then select the optimal data collection method. This allows for the selection of the optimal data collection method by analyzing the user's past lifestyle information.

[0041] The data collection unit can filter the collected lifestyle information based on the user's current living situation and areas of interest. For example, the data collection unit can filter the information to be collected based on the user's current living situation. For example, the data collection unit can filter the information to be collected based on the user's areas of interest. For example, the data collection unit can filter the information to be collected considering both the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's current living situation and areas of interest into the AI, which can then perform the filtering. This allows for the collection of more relevant information by filtering the information based on the user's living situation and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting lifestyle information. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. For example, the data collection unit can prioritize the collection of region-specific benefit information based on the user's geographical location. For example, the data collection unit can prioritize the collection of highly relevant information by referring to the user's travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information. This allows for the priority collection of highly relevant information by considering the user's geographical location.

[0043] The data collection unit can analyze a user's social media activity and collect relevant information when collecting lifestyle information. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant information. For example, the data collection unit can analyze the activity of a user's followers and the accounts they follow and collect relevant information. For example, the data collection unit can analyze a user's social media trends and collect relevant information. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant information. This allows the data collection unit to collect relevant information by analyzing the user's social media activity.

[0044] The information gathering unit can adjust the level of detail of information collected based on its importance when gathering information about a favorite subject. For example, the information gathering unit can collect important event information in detail. For example, the information gathering unit can collect general news information concisely. For example, the information gathering unit can adjust the level of detail of information based on the user's level of interest. Some or all of the above processing in the information gathering unit may be performed using AI, or not. For example, the information gathering unit can input the importance of the information into the AI, and the AI ​​can adjust the level of detail of the collection. This allows for efficient information collection by adjusting the level of detail of the collection based on the importance of the information.

[0045] The information gathering unit can apply different collection algorithms depending on the information category when gathering information about a favorite subject. For example, the information gathering unit can apply a real-time collection algorithm to event information. For example, the information gathering unit can apply a periodic collection algorithm to news information. For example, the information gathering unit can apply a trend collection algorithm to social media information. Some or all of the above processing in the information gathering unit may be performed using AI, or not using AI. For example, the information gathering unit can input the information category into the AI, and the AI ​​can apply an appropriate collection algorithm. This allows for efficient information collection by applying a collection algorithm according to the information category.

[0046] The information gathering unit can determine the priority of information collection based on when the information was submitted when gathering information about a favorite celebrity. For example, the information gathering unit can prioritize collecting information about recent events. For example, the information gathering unit can prioritize collecting the latest news information. For example, the information gathering unit can determine the priority of information based on the user's level of interest. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the information gathering unit can input the information submission dates into a generative AI, and the generative AI can determine the priority of collection. This allows for efficient information collection by determining the priority of collection based on when the information was submitted.

[0047] The information gathering unit can adjust the order of information collection based on the relevance of the information when gathering information about a favorite subject. For example, the information gathering unit can adjust the order of information collection based on the user's level of interest. For example, the information gathering unit can adjust the order of information collection based on the importance of the information. For example, the information gathering unit can adjust the order of information collection based on the category of the information. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information gathering unit can input the relevance of the information into a generative AI, and the generative AI can adjust the order of collection. This allows for efficient information collection by adjusting the order of collection based on the relevance of the information.

[0048] The reminder unit can adjust the level of detail in reminders based on the importance of the event. For example, the reminder unit can provide detailed reminders for important events, and concise reminders for general events. The reminder unit can also adjust the level of detail in reminders based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the importance of the event into the generative AI, which can then adjust the level of detail in the reminder. This allows for efficient reminder delivery by adjusting the level of detail based on the importance of the event.

[0049] The reminder unit can apply different reminder algorithms depending on the event category when a reminder is sent. For example, the reminder unit can apply a detailed reminder algorithm to important events. For example, the reminder unit can apply a concise reminder algorithm to general events. For example, the reminder unit can adjust the reminder algorithm based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the event category into a generative AI, which can then apply an appropriate reminder algorithm. This allows for efficient reminders by applying a reminder algorithm according to the event category.

[0050] The reminder unit can determine the priority of reminders based on the event submission date. For example, the reminder unit may prioritize reminders for the most recent events. For example, the reminder unit may prioritize reminders for important events. For example, the reminder unit may determine the priority of reminders based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the event submission date into a generative AI, which can then determine the priority of reminders. This allows for efficient reminder execution by determining the priority of reminders based on the event submission date.

[0051] The reminder unit can adjust the order of reminders based on the relevance of events. For example, the reminder unit can adjust the order of reminders based on the user's level of interest. For example, the reminder unit can adjust the order of reminders based on the importance of events. For example, the reminder unit can adjust the order of reminders based on the category of events. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the relevance of events into a generative AI, which can then adjust the order of reminders. This allows for efficient reminder delivery by adjusting the order of reminders based on the relevance of events.

[0052] The proposal unit can adjust the level of detail of its proposals based on the importance of the options. For example, it can provide detailed proposals for important options, and concise proposals for general options. The proposal unit can also adjust the level of detail of its proposals based on the user's level of interest. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the options into the AI, which can then adjust the level of detail of the proposals. This allows for efficient proposal generation by adjusting the level of detail of proposals based on the importance of the options.

[0053] The proposal unit can apply different proposal algorithms depending on the option category when making a proposal. For example, the proposal unit can apply a detailed proposal algorithm to important options. For example, the proposal unit can apply a concise proposal algorithm to general options. For example, the proposal unit can adjust the proposal algorithm based on the user's level of interest. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input the option categories into the AI, and the AI ​​can apply an appropriate proposal algorithm. This allows for efficient proposals by applying proposal algorithms according to the option categories.

[0054] The proposal department can determine the priority of proposals based on the timing of option submissions. For example, the proposal department may prioritize the most recent options. For example, the proposal department may prioritize important options. For example, the proposal department may determine the priority of proposals based on user interest. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the option submission timings into the AI, and the AI ​​can determine the priority of proposals. This allows for efficient proposal creation by determining the priority of proposals based on the option submission timings.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the options when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the user's level of interest. For example, the suggestion unit can adjust the order of suggestions based on the importance of the options. For example, the suggestion unit can adjust the order of suggestions based on the category of the options. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the relevance of the options into the AI, and the AI ​​can adjust the order of the suggestions. This allows for efficient suggestion by adjusting the order of suggestions based on the relevance of the options.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite idol / celebrity, a reminder unit that sends reminders, a suggestion unit that proposes online participation and purchasing options, and a geographic information unit that adjusts the idol / celebrity activity plan considering the user's geographic location. For example, the geographic information unit can prioritize collecting information about nearby events based on the user's current location. It can also provide highly relevant information based on the user's travel history. Furthermore, it can collect and provide information on region-specific benefits to the user. This allows for the provision of an optimal idol / celebrity activity plan based on the user's geographic location.

[0058] The support platform can include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite idol / celebrity, a reminder unit that sends reminders, a suggestion unit that proposes online participation and purchasing options, and a social media analysis unit that analyzes the user's social media activity and collects relevant information. For example, the social media analysis unit can analyze the user's posts and collect relevant information. It can also analyze the activity of the user's followers and the accounts they follow and provide relevant information. Furthermore, it can analyze social media trends and provide this information to the user. This allows the platform to provide the optimal idol / celebrity activity plan based on the user's social media activity.

[0059] The support platform can include a data collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that sends reminders, a proposal unit that suggests online participation and purchasing options, and a historical data analysis unit that analyzes the user's past lifestyle information and selects the optimal data collection method. For example, the historical data analysis unit can analyze the user's past behavior patterns and determine the optimal timing for data collection. It can also determine the priority of information to collect based on past lifestyle information. Furthermore, it can select the most efficient data collection method based on past lifestyle information. This allows the platform to provide the optimal data collection method based on the user's past lifestyle information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user lifestyle information. For example, it collects information such as the user's work schedule, family situation, behavioral patterns, hobbies, and interests, which the user provides through the app. Step 2: The Information Gathering Department collects information about the idol / celebrity based on the lifestyle information collected by the Information Gathering Department. For example, they gather information about the idol / celebrity from social media and news sites, and summarize and organize it based on the user's interests. They can also collect information about events, merchandise, and social media posts related to the idol / celebrity. Step 3: The reminder unit sends reminders based on the information collected by the information gathering unit. For example, it sends a notification when an event featuring a favorite artist is approaching. The reminder unit can send notifications in various formats such as email, push notifications, and SMS. Step 4: The proposal team proposes online participation and purchasing options based on the information reminded by the reminder team. For example, they propose specific details and methods for proposing online participation and purchasing options. This could include proposing how to participate in the event or the types of merchandise available for purchase.

[0062] (Example of form 2) The support platform according to an embodiment of the present invention is a system that provides support for continuing "oshi-katsu" (fan activities related to one's favorite idol / celebrity), a market with an estimated size of 800 billion yen, regardless of changes in work or life stage. This support platform analyzes the user's lifestyle and provides reminders to ensure that the user does not miss out on the latest information or events related to their favorite idol / celebrity. It also supports efficient fan activities by suggesting online participation and purchasing options. This enables a fulfilling fan life while maintaining a connection with one's favorite idol / celebrity, no matter how busy one is. For example, the user provides lifestyle information to the AI ​​through the app. For example, the user inputs their work schedule and family situation. This information is analyzed by the AI, and an oshi-katsu plan based on the user's lifestyle is generated. Next, the AI ​​collects the latest information and event information related to the idol / celebrity. For example, it collects information related to the idol / celebrity from SNS and news sites and summarizes and organizes it based on the user's interests. This allows the user to efficiently catch up on the latest information without missing anything. Furthermore, the AI ​​provides reminders to the user. For example, it sends notifications when an event related to the idol / celebrity is approaching to prevent the user from missing out. It also supports the user in efficiently engaging in fan activities by suggesting online participation and purchasing options. This system allows users to maintain a connection with their favorite idols and enjoy a fulfilling fan life, no matter how busy they are. For example, even busy working users can efficiently engage in fan activities as the AI ​​suggests the optimal plan for their favorite idols. Furthermore, by recommending activities that fit their budget, users can enjoy fan activities while reducing their financial burden. In addition, the AI ​​analyzes user behavior data and generates optimal event participation and merchandise purchase plans. For example, based on past behavior data, it recommends events and merchandise that the user is likely to be interested in. As a result, users receive a fan activity plan that is perfectly suited to them. In this way, the present invention utilizes AI to provide fan activity plans tailored to the user's lifestyle, supporting efficient and effective fan activities.This allows users to maintain a connection with their favorite idols and enjoy a fulfilling fan life, no matter how busy they are. The support platform can then efficiently support fan activities by collecting information about the idols based on the user's lifestyle and providing reminders and suggestions.

[0063] The support platform according to the embodiment comprises a collection unit, an information collection unit, a reminder unit, and a suggestion unit. The collection unit collects user lifestyle information. For example, the collection unit collects information such as the user's work schedule and family situation provided through the app. The collection unit can also collect information such as the user's behavior patterns, hobbies, and interests. The information collection unit collects information about the user's favorite idol based on the lifestyle information collected by the collection unit. For example, the information collection unit collects information about the idol from social media and news sites and summarizes and organizes it based on the user's interests. The information collection unit can also collect information such as event information, merchandise information, and social media posts related to the idol. The reminder unit provides reminders based on the information collected by the information collection unit. For example, the reminder unit sends a notification when an event related to the idol is approaching. The reminder unit can send notifications in the form of email, push notifications, SMS, etc. The suggestion unit proposes online participation and purchase options based on the information reminded by the reminder unit. For example, the suggestion unit proposes specific content and methods of proposing online participation and purchase options. The proposal section can, for example, suggest how to participate in an event or the types of merchandise available for purchase. This allows the support platform according to the embodiment to collect information about the user's favorite idol or idol based on their lifestyle, and to provide reminders and suggestions, thereby supporting efficient fan activities.

[0064] The data collection unit collects user lifestyle information. Specifically, it collects information such as work schedules and family circumstances that users provide through the app. For example, it can collect calendar information, task management information, and family events and appointments that users enter into the app. Furthermore, the data collection unit can also collect information such as users' behavior patterns, hobbies, and interests. This includes the user's operation history, browsing history, and search history within the app. For example, if a user frequently views news articles of a particular genre, it can be determined that they have a high level of interest in that genre. It can also collect the user's participation history in specific events and purchase history of specific products. As a result, the data collection unit can collect detailed data on users' lifestyles and interests, building a foundation for providing optimal information and service suggestions to individual users. Furthermore, the data collection unit can centrally manage this data and link it with other departments and systems as needed. For example, collected data can be stored on a cloud server and made accessible to the information collection unit and the reminder unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions can be made. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The Information Gathering Department collects information about users' favorite idols / characters based on lifestyle information gathered by the main collection department. Specifically, it collects information about these idols / characters from social media and news sites, summarizing and organizing it based on the user's interests. For example, if a user is interested in a particular artist or character, the department can collect the latest news, event information, and merchandise information related to that artist or character. The Information Gathering Department uses AI to efficiently collect large amounts of information and extract important information based on the user's interests. For example, it can use natural language processing technology to analyze social media posts and grasp trends and topics related to the idols / characters. It can also collect official announcements and media reports about the idols / characters from news sites, providing reliable information. Furthermore, the Information Gathering Department summarizes and organizes the collected information based on the user's interests and provides it in a format that the user can easily understand. For example, it can display event information about the idols / characters in a calendar format or organize merchandise information in a list format. This allows the Information Gathering Department to help users efficiently obtain information about their idols / characters and support their fan activities. In addition, the Information Gathering Department regularly updates the collected information, always providing the latest information. This allows users to always stay informed and follow the latest developments regarding their idols / characters.

[0066] The Reminders Unit sends reminders based on information collected by the Information Gathering Unit. Specifically, it sends notifications when an event related to a favorite idol is approaching. For example, it can send reminder notifications to users when the date of a concert, autograph session, or online event related to their favorite idol is approaching. The Reminders Unit can send notifications in various formats, including email, push notifications, and SMS. For example, if a user has the app installed, a push notification can be used to send an immediate reminder. Furthermore, by using email or SMS, notifications can be reliably delivered to users who do not use the app. The Reminders Unit can send notifications at the optimal time based on the user's schedule and behavioral patterns. For example, the timing of notifications can be adjusted so that users receive them during work breaks or commutes. In addition, the Reminders Unit can customize the content of notifications to match the user's interests. For example, it can notify users not only about their favorite idol's event but also about related merchandise and news articles. This allows the Reminders Unit to support users in efficiently engaging in fan activities without missing important information. Furthermore, the Reminders Unit can collect user feedback and continuously improve the accuracy of notification content and timing. This allows the reminder function to provide users with optimal reminders and support their fan activities.

[0067] The suggestion department proposes online participation and purchasing options based on information reminded by the reminder department. Specifically, it proposes the specific content and methods of suggesting online participation and purchasing options. For example, it can suggest ways to participate in events featuring a favorite artist online, how to purchase event tickets, and purchasing options for related merchandise. The suggestion department can make optimal suggestions based on the user's interests. For example, if a user is a fan of a particular artist, it can suggest purchasing options for that artist's latest album or concert tickets. Also, if a user is interested in a particular character, it can suggest merchandise and event information related to that character. The suggestion department uses AI to analyze the user's interests and make optimal suggestions. For example, based on the user's past purchase and browsing history, it can predict and suggest products and services that the user might be interested in. Furthermore, the suggestion department can customize the suggestions to match the user's lifestyle. For example, if a user is busy, it can suggest options that can be easily purchased online, or if a user lives in a specific region, it can suggest event information held in that region. In this way, the suggestion department can make optimal suggestions to users and support efficient fan activities. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide users with the best possible suggestions and support their fan activities.

[0068] The data collection unit can collect user lifestyle information. For example, it can collect information such as work schedules and family circumstances provided by the user through the app. The data collection unit can also collect information such as the user's behavioral patterns, hobbies, and interests. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information provided by the user through the app into the AI, which can then analyze and collect lifestyle information. By collecting user lifestyle information, it becomes possible to provide individually optimized activity plans.

[0069] The information gathering unit can collect information about a user's favorite idol from social media and news sites, and summarize and organize it based on the user's interests. For example, the information gathering unit can collect information about a user's favorite idol from social media and news sites, and summarize and organize it based on the user's interests. The information gathering unit can also collect information about events, merchandise, and social media posts related to the idol. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the information gathering unit can input information collected from social media and news sites into a generative AI, which can then summarize and organize it. This allows for efficient information provision by summarizing and organizing information about the idol based on the user's interests.

[0070] The reminder function can send notifications when an event featuring the user's favorite artist is approaching. For example, the reminder function can send notifications when an event featuring the user's favorite artist is approaching. The reminder function can send notifications in various formats, such as email, push notifications, and SMS. Some or all of the above-described processes in the reminder function may be performed using, for example, a generative AI, or not. For example, the reminder function can use a generative AI to send notifications when an event featuring the user's favorite artist is approaching. This prevents the user from missing the event by sending notifications when the event is approaching.

[0071] The proposal department can suggest online participation and purchasing options. For example, the proposal department can suggest specific details and methods for suggesting online participation and purchasing options. For example, the proposal department can suggest how to participate in an event and the types of merchandise available for purchase. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input suggestions for online participation and purchasing options into an AI, which can then make the most appropriate suggestions. This supports users in efficiently engaging in fan activities by suggesting online participation and purchasing options.

[0072] The suggestion department can recommend fan activities that fit within a budget. For example, the suggestion department can recommend specific content and methods of suggesting fan activities that fit within a budget. For example, the suggestion department can recommend budget ranges and types of fan activities. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input recommendations for fan activities that fit within a budget into an AI, and the AI ​​can make the optimal recommendation. This allows fans to enjoy their fan activities while reducing their financial burden by recommending fan activities that fit within a budget.

[0073] The information gathering unit can analyze user behavior data and generate optimal event participation and merchandise purchase plans. For example, the information gathering unit can analyze user behavior data and generate optimal event participation and merchandise purchase plans. For example, the information gathering unit can recommend events and merchandise that users are likely to be interested in based on past behavior data. Some or all of the above processing in the information gathering unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the information gathering unit can input user behavior data into a generating AI, and the generating AI can generate optimal event participation and merchandise purchase plans. In this way, by analyzing user behavior data, optimal event participation and merchandise purchase plans can be provided.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of lifestyle information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect lifestyle information during times when the user is relaxed. If the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. If the user is relaxed, the data collection unit can collect detailed lifestyle information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the collection timing. This allows lifestyle information to be collected at a more appropriate time by adjusting the collection timing based on the user's emotions.

[0075] The data collection unit can analyze the user's past lifestyle information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on the user's past lifestyle information. For example, the data collection unit can analyze the user's past behavioral patterns and determine the optimal data collection timing. For example, the data collection unit can determine the priority of information to collect based on the user's past lifestyle information. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past lifestyle information into AI, which can then select the optimal data collection method. This allows for the selection of the optimal data collection method by analyzing the user's past lifestyle information.

[0076] The data collection unit can filter the collected lifestyle information based on the user's current living situation and areas of interest. For example, the data collection unit can filter the information to be collected based on the user's current living situation. For example, the data collection unit can filter the information to be collected based on the user's areas of interest. For example, the data collection unit can filter the information to be collected considering both the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's current living situation and areas of interest into the AI, which can then perform the filtering. This allows for the collection of more relevant information by filtering the information based on the user's living situation and areas of interest.

[0077] The data collection unit can estimate the user's emotions and determine the priority of lifestyle information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information related to relaxation. For example, if the user is excited, the data collection unit can prioritize collecting information related to entertainment. For example, if the user is tired, the data collection unit can prioritize collecting information related to rest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then determine the priority of the information. This allows for the priority collection of more important information based on the user's emotions.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting lifestyle information. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. For example, the data collection unit can prioritize the collection of region-specific benefit information based on the user's geographical location. For example, the data collection unit can prioritize the collection of highly relevant information by referring to the user's travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information. This allows for the priority collection of highly relevant information by considering the user's geographical location.

[0079] The data collection unit can analyze a user's social media activity and collect relevant information when collecting lifestyle information. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant information. For example, the data collection unit can analyze the activity of a user's followers and the accounts they follow and collect relevant information. For example, the data collection unit can analyze a user's social media trends and collect relevant information. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant information. This allows the data collection unit to collect relevant information by analyzing the user's social media activity.

[0080] The information gathering unit can estimate the user's emotions and adjust the method of collecting information about the user's favorite things based on the estimated emotions. For example, if the user is stressed, the information gathering unit will collect concise and to-the-point information. For example, if the user is relaxed, the information gathering unit will collect detailed information. For example, if the user is excited, the information gathering unit will collect visually appealing information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's emotion data into the generative AI, which can then adjust the method of collecting information. This allows for the collection of more appropriate information by adjusting the method of collecting information based on the user's emotions.

[0081] The information gathering unit can adjust the level of detail of information collected based on its importance when gathering information about a favorite subject. For example, the information gathering unit can collect important event information in detail. For example, the information gathering unit can collect general news information concisely. For example, the information gathering unit can adjust the level of detail of information based on the user's level of interest. Some or all of the above processing in the information gathering unit may be performed using AI, or not. For example, the information gathering unit can input the importance of the information into the AI, and the AI ​​can adjust the level of detail of the collection. This allows for efficient information collection by adjusting the level of detail of the collection based on the importance of the information.

[0082] The information gathering unit can apply different collection algorithms depending on the information category when gathering information about a favorite subject. For example, the information gathering unit can apply a real-time collection algorithm to event information. For example, the information gathering unit can apply a periodic collection algorithm to news information. For example, the information gathering unit can apply a trend collection algorithm to social media information. Some or all of the above processing in the information gathering unit may be performed using AI, or not using AI. For example, the information gathering unit can input the information category into the AI, and the AI ​​can apply an appropriate collection algorithm. This allows for efficient information collection by applying a collection algorithm according to the information category.

[0083] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the information gathering unit will prioritize collecting information related to relaxation. For example, if the user is excited, the information gathering unit can prioritize collecting information related to entertainment. For example, if the user is tired, the information gathering unit can prioritize collecting information related to rest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generative AI, which can then determine the priority of information. This allows for the priority collection of more important information based on the user's emotions.

[0084] The information gathering unit can determine the priority of information collection based on when the information was submitted when gathering information about a favorite celebrity. For example, the information gathering unit can prioritize collecting information about recent events. For example, the information gathering unit can prioritize collecting the latest news information. For example, the information gathering unit can determine the priority of information based on the user's level of interest. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the information gathering unit can input the information submission dates into a generative AI, and the generative AI can determine the priority of collection. This allows for efficient information collection by determining the priority of collection based on when the information was submitted.

[0085] The information gathering unit can adjust the order of information collection based on the relevance of the information when gathering information about a favorite subject. For example, the information gathering unit can adjust the order of information collection based on the user's level of interest. For example, the information gathering unit can adjust the order of information collection based on the importance of the information. For example, the information gathering unit can adjust the order of information collection based on the category of the information. Some or all of the above processing in the information gathering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information gathering unit can input the relevance of the information into a generative AI, and the generative AI can adjust the order of collection. This allows for efficient information collection by adjusting the order of collection based on the relevance of the information.

[0086] The reminder unit can estimate the user's emotions and adjust the way the reminder is presented based on the estimated emotions. For example, if the user is stressed, the reminder unit will provide a simple and highly visible reminder. If the user is relaxed, the reminder unit can provide a more detailed reminder. If the user is excited, the reminder unit can provide a visually appealing reminder. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI, which can then adjust the way the reminder is presented. This allows for more appropriate reminders by adjusting the way the reminder is presented based on the user's emotions.

[0087] The reminder unit can adjust the level of detail in reminders based on the importance of the event. For example, the reminder unit can provide detailed reminders for important events, and concise reminders for general events. The reminder unit can also adjust the level of detail in reminders based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the importance of the event into the generative AI, which can then adjust the level of detail in the reminder. This allows for efficient reminder delivery by adjusting the level of detail based on the importance of the event.

[0088] The reminder unit can apply different reminder algorithms depending on the event category when a reminder is sent. For example, the reminder unit can apply a detailed reminder algorithm to important events. For example, the reminder unit can apply a concise reminder algorithm to general events. For example, the reminder unit can adjust the reminder algorithm based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the event category into a generative AI, which can then apply an appropriate reminder algorithm. This allows for efficient reminders by applying a reminder algorithm according to the event category.

[0089] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will send reminders during times when the user is relaxed. If the user is busy, the reminder unit can adjust the timing of reminders to fit their schedule. If the user is relaxed, the reminder unit can provide more detailed reminders. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI, which can then adjust the timing of reminders. This allows for more appropriate timing of reminders by adjusting the timing based on the user's emotions.

[0090] The reminder unit can determine the priority of reminders based on the event submission date. For example, the reminder unit may prioritize reminders for the most recent events. For example, the reminder unit may prioritize reminders for important events. For example, the reminder unit may determine the priority of reminders based on the user's level of interest. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reminder unit can input the event submission date into a generative AI, which can then determine the priority of reminders. This allows for efficient reminder execution by determining the priority of reminders based on the event submission date.

[0091] The reminder unit can adjust the order of reminders based on the relevance of events. For example, the reminder unit can adjust the order of reminders based on the user's level of interest. For example, the reminder unit can adjust the order of reminders based on the importance of events. For example, the reminder unit can adjust the order of reminders based on the category of events. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the relevance of events into a generative AI, which can then adjust the order of reminders. This allows for efficient reminder delivery by adjusting the order of reminders based on the relevance of events.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is excited, the suggestion unit can present visually appealing suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI, which can then adjust the way it presents its suggestions. This allows for more appropriate suggestions to be made by adjusting the presentation based on the user's emotions.

[0093] The proposal unit can adjust the level of detail of its proposals based on the importance of the options. For example, it can provide detailed proposals for important options, and concise proposals for general options. The proposal unit can also adjust the level of detail of its proposals based on the user's level of interest. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the options into the AI, which can then adjust the level of detail of the proposals. This allows for efficient proposal generation by adjusting the level of detail of proposals based on the importance of the options.

[0094] The proposal unit can apply different proposal algorithms depending on the option category when making a proposal. For example, the proposal unit can apply a detailed proposal algorithm to important options. For example, the proposal unit can apply a concise proposal algorithm to general options. For example, the proposal unit can adjust the proposal algorithm based on the user's level of interest. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input the option categories into the AI, and the AI ​​can apply an appropriate proposal algorithm. This allows for efficient proposals by applying proposal algorithms according to the option categories.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is excited, the suggestion unit can make visually appealing suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI, which can then adjust the length of the suggestions. This allows for more appropriate suggestions to be made by adjusting the length of the suggestions based on the user's emotions.

[0096] The proposal department can determine the priority of proposals based on the timing of option submissions. For example, the proposal department may prioritize the most recent options. For example, the proposal department may prioritize important options. For example, the proposal department may determine the priority of proposals based on user interest. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the option submission timings into the AI, and the AI ​​can determine the priority of proposals. This allows for efficient proposal creation by determining the priority of proposals based on the option submission timings.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the options when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the user's level of interest. For example, the suggestion unit can adjust the order of suggestions based on the importance of the options. For example, the suggestion unit can adjust the order of suggestions based on the category of the options. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the relevance of the options into the AI, and the AI ​​can adjust the order of the suggestions. This allows for efficient suggestion by adjusting the order of suggestions based on the relevance of the options.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite idol / idol, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchase options, and an emotion estimation unit that estimates the user's emotions and adjusts the idol / idol activity plan based on those estimates. For example, if the user is feeling stressed, the emotion estimation unit can suggest a relaxing idol / idol activity plan. If the user is excited, it can recommend highly entertaining events. Furthermore, if the user is tired, it can provide an idol / idol activity plan that prioritizes rest. This allows the platform to provide the optimal idol / idol activity plan tailored to the user's emotions.

[0100] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite idol / celebrity, a reminder unit that sends reminders, a suggestion unit that proposes online participation and purchasing options, and a geographic information unit that adjusts the idol / celebrity activity plan considering the user's geographic location. For example, the geographic information unit can prioritize collecting information about nearby events based on the user's current location. It can also provide highly relevant information based on the user's travel history. Furthermore, it can collect and provide information on region-specific benefits to the user. This allows for the provision of an optimal idol / celebrity activity plan based on the user's geographic location.

[0101] The support platform can include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite idol / celebrity, a reminder unit that sends reminders, a suggestion unit that proposes online participation and purchasing options, and a social media analysis unit that analyzes the user's social media activity and collects relevant information. For example, the social media analysis unit can analyze the user's posts and collect relevant information. It can also analyze the activity of the user's followers and the accounts they follow and provide relevant information. Furthermore, it can analyze social media trends and provide this information to the user. This allows the platform to provide the optimal idol / celebrity activity plan based on the user's social media activity.

[0102] The support platform can include a data collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchasing options, and an emotional reminder unit that estimates the user's emotions and adjusts the way reminders are presented based on those emotions. For example, the emotional reminder unit can provide a simple and highly visible reminder when the user is stressed, a more detailed reminder when the user is relaxed, and a visually appealing reminder when the user is excited. This allows the platform to provide optimal reminders based on the user's emotions.

[0103] The support platform may include a data collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchase options, and an emotion suggestion unit that estimates the user's emotions and adjusts the way suggestions are presented based on those emotions. For example, if the user is feeling stressed, the emotion suggestion unit can make simple and highly visual suggestions. If the user is relaxed, it can make more detailed suggestions. Furthermore, if the user is excited, it can make visually appealing suggestions. This allows the platform to provide optimal suggestions based on the user's emotions.

[0104] The support platform can include a data collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that sends reminders, a proposal unit that suggests online participation and purchasing options, and a historical data analysis unit that analyzes the user's past lifestyle information and selects the optimal data collection method. For example, the historical data analysis unit can analyze the user's past behavior patterns and determine the optimal timing for data collection. It can also determine the priority of information to collect based on past lifestyle information. Furthermore, it can select the most efficient data collection method based on past lifestyle information. This allows the platform to provide the optimal data collection method based on the user's past lifestyle information.

[0105] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchasing options, as well as an emotion collection unit that estimates the user's emotions and determines the priority of lifestyle information to collect based on those estimated emotions. For example, if the user is feeling stressed, the emotion collection unit can prioritize collecting information related to relaxation. If the user is excited, it can prioritize collecting information related to entertainment. Furthermore, if the user is tired, it can prioritize collecting information related to rest. This allows for the collection of optimal lifestyle information based on the user's emotions.

[0106] The support platform can include a data collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchasing options, and an emotion timing unit that estimates the user's emotions and adjusts the timing of reminders based on those emotions. For example, if the emotion timing unit is stressed, it can send reminders during times when the user is relaxed. If the user is busy, it can adjust the timing of reminders to fit their schedule. Furthermore, if the user is relaxed, it can send more detailed reminders. This allows the platform to provide optimal reminder timing based on the user's emotions.

[0107] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchasing options, as well as an emotion collection unit that estimates the user's emotions and determines the priority of lifestyle information to collect based on those estimated emotions. For example, if the user is feeling stressed, the emotion collection unit can prioritize collecting information related to relaxation. If the user is excited, it can prioritize collecting information related to entertainment. Furthermore, if the user is tired, it can prioritize collecting information related to rest. This allows for the collection of optimal lifestyle information based on the user's emotions.

[0108] The support platform may include a collection unit that collects user lifestyle information, an information collection unit that collects information about the user's favorite things, a reminder unit that issues reminders, a suggestion unit that proposes online participation and purchasing options, as well as an emotion collection unit that estimates the user's emotions and determines the priority of lifestyle information to collect based on those estimated emotions. For example, if the user is feeling stressed, the emotion collection unit can prioritize collecting information related to relaxation. If the user is excited, it can prioritize collecting information related to entertainment. Furthermore, if the user is tired, it can prioritize collecting information related to rest. This allows for the collection of optimal lifestyle information based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects user lifestyle information. For example, it collects information such as the user's work schedule, family situation, behavioral patterns, hobbies, and interests, which the user provides through the app. Step 2: The Information Gathering Department collects information about the idol / celebrity based on the lifestyle information collected by the Information Gathering Department. For example, they gather information about the idol / celebrity from social media and news sites, and summarize and organize it based on the user's interests. They can also collect information about events, merchandise, and social media posts related to the idol / celebrity. Step 3: The reminder unit sends reminders based on the information collected by the information gathering unit. For example, it sends a notification when an event featuring a favorite artist is approaching. The reminder unit can send notifications in various formats such as email, push notifications, and SMS. Step 4: The proposal team proposes online participation and purchasing options based on the information reminded by the reminder team. For example, they propose specific details and methods for proposing online participation and purchasing options. This could include proposing how to participate in the event or the types of merchandise available for purchase.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the collection unit, information collection unit, reminder unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as work schedules and home situations provided by the user through the app. The information collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information about the user's favorite idol from social media and news sites, and summarizes and organizes it based on the user's interests. The reminder unit is implemented by the control unit 46A of the smart device 14 and sends a notification when an event related to the idol is approaching. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific content and methods for suggesting online participation and purchasing options. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, information collection unit, reminder unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as work schedules and home situations provided by the user through an app. The information collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information about the user's favorite idol from social media and news sites, and summarizes and organizes it based on the user's interests. The reminder unit is implemented by the control unit 46A of the smart glasses 214 and sends a notification when an event related to the idol is approaching. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific content and methods for suggesting online participation and purchasing options. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, information collection unit, reminder unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as work schedules and family situations provided by the user through the app. The information collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information about the user's favorite idol from social media and news sites, and summarizes and organizes it based on the user's interests. The reminder unit is implemented by the control unit 46A of the headset terminal 314 and sends a notification when an event related to the idol is approaching. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific content and methods for suggesting online participation and purchasing options. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, information collection unit, reminder unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information such as work schedules and home situations provided by the user through the app. The information collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information about the user's favorite idol from social media and news sites, and summarizes and organizes it based on the user's interests. The reminder unit is implemented by the control unit 46A of the robot 414 and sends a notification when an event related to the idol is approaching. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific content and methods for suggesting online participation and purchasing options. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection department that collects lifestyle information, An information collection unit collects information about a favorite based on lifestyle information collected by the aforementioned collection unit, A reminder unit that sends reminders based on the information collected by the aforementioned information collection unit, The system includes a suggestion unit that proposes online participation and purchasing options based on the information reminded by the reminder unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user lifestyle information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned information gathering unit, Gather information about your favorite idol from social media and news sites, and summarize and organize it based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reminder unit, Send notifications when your favorite artist's event is approaching. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We offer online participation and purchasing options. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We recommend activities that fit your budget for supporting your favorite idol / character. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, By analyzing user behavior data, we generate optimal event participation and merchandise purchase plans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of lifestyle information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past lifestyle information and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting lifestyle information, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of lifestyle information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting lifestyle information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting lifestyle information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information gathering unit, We estimate the user's emotions and adjust how we collect information about their favorite things based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information gathering unit, When gathering information about your favorite idol, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned information gathering unit, When gathering information about your favorite idol, apply different collection algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information gathering unit, When gathering information about your favorite idol, prioritize the information collection based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information gathering unit, When gathering information about your favorite idol, adjust the order of collection based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reminder unit, It estimates the user's emotions and adjusts the way reminders are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reminder unit, When sending a reminder, adjust the level of detail based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reminder unit, When sending a reminder, apply a different reminder algorithm depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reminder unit, When sending reminders, prioritize them based on when the event was due. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reminder unit, When sending reminders, adjust the order of reminders based on the relevance of the events. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the options. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the option category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the options were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the options. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects lifestyle information, An information collection unit collects information about a favorite based on lifestyle information collected by the aforementioned collection unit, A reminder unit that sends reminders based on the information collected by the aforementioned information collection unit, The system includes a suggestion unit that proposes online participation and purchasing options based on the information reminded by the reminder unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user lifestyle information. The system according to feature 1.

3. The aforementioned information gathering unit, Gather information about your favorite idols from social media and news sites, and summarize and organize it based on the user's interests. The system according to feature 1.

4. The aforementioned reminder unit, Send notifications when your favorite artist's event is approaching. The system according to feature 1.

5. The aforementioned proposal section is, We offer online participation and purchasing options. The system according to feature 1.

6. The aforementioned proposal section is, We recommend activities that fit your budget for supporting your favorite idol / character. The system according to feature 1.

7. The aforementioned information gathering unit, By analyzing user behavior data, we generate optimal event participation and merchandise purchase plans. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of lifestyle information collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past lifestyle information and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting lifestyle information, filtering is performed based on the user's current living situation and areas of interest. The system according to feature 1.

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