system

Abstract

The system according to this embodiment aims to efficiently collect information related to one's favorite idol / character and support the purchase of limited-edition merchandise and tickets. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, and a proxy purchase unit. The collection unit collects information related to the user's favorite idol. The analysis unit analyzes the information collected by the collection unit and creates a report to provide to the user. The provision unit provides the report created by the analysis unit to the user. The support unit provides sales information for limited-edition products in real time and supports purchases. The proxy purchase unit performs proxy purchases of tickets via mail order.

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, including 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 prior art, it has not been fully done to efficiently collect information related to the favorite and support the purchase of limited products and tickets, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect information related to the favorite and support the purchase of limited products and tickets.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, and a proxy purchase unit. The collection unit collects information related to the user's favorite idol / idol. The analysis unit analyzes the information collected by the collection unit and creates a report to provide to the user. The provision unit provides the report created by the analysis unit to the user. The support unit provides sales information for limited-edition products in real time and supports purchases. The proxy purchase unit performs proxy purchases of tickets via mail order. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect information related to one's favorite idol / character and support the purchase of limited-edition goods and tickets. [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 manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 receiving 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 receiving 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) An embodiment of the present invention provides a support system for supporting one's favorite idol / celebrity, which efficiently collects, analyzes, and provides information related to that idol / celebrity. This support system is characterized by its AI compiling news about the idol / celebrity and providing regular reports. It also supports proxy purchases of tickets through online retailers. This makes it possible to enjoy supporting multiple idols / celebrities without burden. For example, the support system automatically collects information related to the idol / celebrity from social media and news sites using AI. For example, it collects the latest news, event information, and sales information for limited-edition merchandise related to the idol / celebrity. This allows users to easily obtain the latest information about their idol / celebrity. Next, the support system uses AI to analyze the collected information and creates regular reports for the user to provide. For example, once a week, it provides the user with a report summarizing the latest news, event information, and sales information for limited-edition merchandise related to the idol / celebrity. This allows users to efficiently engage in idol / celebrity activities without missing important information about their idol / celebrity. Furthermore, the support system provides sales information for limited-edition merchandise in real time and supports purchases. For example, it notifies users of the start time of sales for limited-edition merchandise and supports them in making purchases. This ensures that users can purchase limited-edition items without missing the opportunity. The fan support system also assists with ticket purchases. For example, the AI ​​automatically purchases tickets for concerts and events and provides them to users. This simplifies the ticket purchase process, allowing users to enjoy their fan activities more. This service solves challenges faced by fans and idol / character enthusiasts. Specifically, it addresses issues such as the time-consuming nature of information gathering, missing opportunities to purchase limited-edition items, and the frustration of popular items selling out. For instance, if a user wants to know the latest news about their favorite idol, the AI ​​automatically collects information and provides it as a regular report. It also provides real-time notifications about sales of limited-edition items, supporting users in securing their purchases. Furthermore, the AI ​​purchases tickets for concerts and events on behalf of users and provides them to them.In this way, AI-powered fan support services allow users to efficiently engage in fan activities, easily obtain information related to their favorite idols, and reliably purchase limited-edition merchandise. This makes it possible to enjoy multiple fan activities without burden, improving user satisfaction. As a result, the fan support system can efficiently collect, analyze, and provide information related to the idols that users are interested in.

[0029] The idol fan support system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, and a proxy purchase unit. The collection unit collects information related to the idol. The collection unit collects information related to the idol from, for example, social media and news sites. The collection unit collects, for example, the latest news and event information about the idol, and sales information for limited edition products. The collection unit can automatically collect information related to the idol using, for example, AI. The analysis unit analyzes the information collected by the collection unit and creates reports to be provided to the user. The analysis unit analyzes the collected information and creates periodic reports. The analysis unit can analyze the collected information and create reports to be provided to the user using, for example, AI. The provision unit provides the reports created by the analysis unit to the user. The provision unit provides, for example, periodic reports to the user. The provision unit can provide the reports created by the analysis unit to the user using, for example, AI. The support unit provides sales information for limited edition products in real time and supports purchases. The support unit notifies the user of the start time of sales for limited edition products and supports the user in making a purchase. The support unit can, for example, use AI to provide real-time sales information on limited-edition products and support users in making purchases. The proxy purchase unit performs proxy purchases of tickets for mail-order sales. The proxy purchase unit can, for example, automatically purchase tickets for live performances and events and provide them to users. The proxy purchase unit can, for example, use AI to perform proxy purchases of tickets for mail-order sales and provide them to users. As a result, the fan activity support system according to the embodiment can efficiently collect, analyze, and provide information related to the user's favorite idol. Some or all of the above-described processes in the fan activity support system may be performed using AI, for example, or without AI. For example, the fan activity support system can input information collected by the collection unit into the AI ​​and have the AI ​​perform the analysis of the information.

[0030] The data collection unit collects information related to the idol / celebrity. For example, it collects information from social media and news sites. Specifically, it collects data from social media platforms such as the idol's latest posts, fan comments, retweets, and likes. It also collects articles, interviews, and event announcements from news sites. Furthermore, the unit obtains information from the idol's official website and fan club site to stay informed about their latest activities and releases. The data collection unit can automatically collect this information using AI. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract keywords and hashtags related to the idol. This allows the data collection unit to efficiently collect important information about the idol from a vast amount of data. Additionally, the data collection unit centrally manages the collected data and stores it in a database. This allows the analysis and provisioning units to quickly access the necessary information. The data collection unit can flexibly set the frequency and scope of information collection, strengthening information gathering at important times, such as during specific events or new product releases. This allows the data collection department to support users in staying up-to-date on the latest information about their favorite idols.

[0031] The analysis unit analyzes the information collected by the data collection unit and creates reports to provide to users. For example, the analysis unit analyzes the collected information and creates periodic reports. Specifically, the analysis unit classifies the collected data and analyzes the activity status of the idol, fan reactions, trends, etc. Using AI, it can analyze the collected information and create reports to provide to users. For example, it can use machine learning algorithms to predict fluctuations in the popularity of the idol and increases in fan interest. It can also use natural language processing technology to analyze social media posts and extract fan sentiments and opinions. This allows the analysis unit to report to users in detail the latest trends of the idol and fan reactions. Furthermore, the analysis unit can evaluate the effectiveness of the idol's activities and changes in trends by comparing them with past data. For example, it can analyze the success rate of past events and releases to make predictions for future activities. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to provide users with not only real-time situational awareness but also long-term trend analysis and anomaly detection, improving the reliability and usefulness of the entire idol support system.

[0032] The service provider delivers reports created by the analytics department to users. For example, the service provider provides users with regular reports. Specifically, the service provider customizes the reports created by the analytics department to the user's preferences and delivers them via email or a dedicated app. Using AI, the reports created by the analytics department can be delivered to users. For example, the content of the report can be personalized based on the user's past browsing history and interests. The service provider can also adjust the timing of report delivery to match the user's lifestyle. For example, by delivering reports during the morning commute or evening relaxation time, the information can be delivered at the time when the user is most likely to receive it. Furthermore, the service provider visualizes the data using graphs and charts to make the report content visually easy to understand. This allows users to grasp the activity status and trends of their favorite idols at a glance. The service provider can collect feedback from users and continuously improve the content and delivery methods of the reports. For example, it can reflect the information that users are particularly interested in and their opinions on the report format to provide more user-friendly reports. In this way, the service provider can always provide users with the latest and most useful information and support their idol activities.

[0033] The support department provides real-time sales information for limited-edition products and assists with purchases. Specifically, the support department notifies users of the start time of sales for limited-edition products and supports them in making purchases. By using AI, the support department can provide real-time sales information for limited-edition products and support users in making purchases. For example, the AI ​​monitors updates on the sales site and instantly detects the start time of sales for limited-edition products. It also sends individually customized notifications based on the user's purchase history and products of interest. This allows users to act quickly without missing important sales information. Furthermore, the support department also assists in simplifying the purchase process. For example, by registering the user's payment information and shipping address information in advance, the purchase process can be carried out smoothly. In addition, the support department can send notifications through multiple devices and platforms. For example, it can use a combination of smartphone push notifications, email, and SMS to ensure that information is delivered reliably. In this way, the support department helps users reliably purchase limited-edition products and makes their fan activities more fulfilling.

[0034] The ticket purchasing service acts as an agent for online ticket sales. Specifically, it automatically purchases tickets for live performances and events and provides them to users. It can utilize AI to purchase tickets online and provide them to users. For example, the AI ​​monitors updates on ticket sales websites and instantly detects the start time of sales. It also selects the most suitable tickets based on the user's desired seat type and price range, and automatically handles the purchase process. This allows users to secure their desired tickets without having to participate in the competition. Furthermore, the ticket purchasing service manages purchased tickets and handles delivery procedures. For example, it registers purchased tickets to the user's account and provides them as electronic tickets. For paper tickets, it arranges delivery to the specified address. The ticket purchasing service can also prioritize purchasing tickets for specific events or live performances upon user request. This allows the service to help users ensure they can attend their desired events and live performances, enriching their fan activities.

[0035] The collection unit can collect information related to one's favorite idol or celebrity from social media and news sites. The collection unit can, for example, collect information related to one's favorite idol or celebrity from social media. The collection unit can, for example, collect information related to one's favorite idol or celebrity from news sites. The collection unit can, for example, use AI to automatically collect information related to one's favorite idol or celebrity from social media and news sites. This allows for the collection of information related to one's favorite idol or celebrity from a wide range of sources. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information collected from social media and news sites into AI and have AI perform the information collection.

[0036] The analysis unit can analyze the collected information and generate periodic reports. For example, the analysis unit can analyze the collected information and generate periodic reports. The analysis unit can, for example, use AI to analyze the collected information and generate periodic reports. This allows for the analysis of collected information and the provision of periodic reports. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected information into the AI ​​and have the AI ​​perform the analysis of the information.

[0037] The service provider can provide periodic reports to users. The service provider can, for example, provide periodic reports to users. The service provider can, for example, use AI to provide periodic reports to users. This allows the service provider to provide reports to users on a regular basis. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input periodic reports into AI and have AI perform the task of providing the reports.

[0038] The support unit can notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. The support unit can, for example, notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. The support unit can, for example, use AI to notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. This ensures that users can purchase limited-edition products. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the start time of sales for limited-edition products into the AI ​​and have the AI ​​perform the notification support.

[0039] The ticket purchasing unit can automatically purchase tickets for live performances and events and provide them to users. For example, the ticket purchasing unit can automatically purchase tickets for live performances and events and provide them to users. The ticket purchasing unit can, for example, use AI to automatically purchase tickets for live performances and events and provide them to users. This makes it easy for users to obtain tickets for live performances and events. Some or all of the above-described processes in the ticket purchasing unit may be performed using AI, or not. For example, the ticket purchasing unit can input the live performance or event tickets into the AI ​​and have the AI ​​execute the ticket purchase.

[0040] The data collection unit can analyze the user's past information gathering history and select the optimal data collection method. For example, the data collection unit may prioritize collecting information from sources that the user has frequently accessed in the past. For example, the data collection unit may collect information based on topics that the user has shown interest in in the past. For example, the data collection unit may analyze the user's past information gathering patterns and collect information at the optimal time. This allows the data collection unit to select the optimal data collection method based on the user's past information gathering 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 may input the user's past information gathering history into AI and have AI select the optimal data collection method.

[0041] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can collect only information related to the user's current interests. For example, if the user is interested in a particular event, the data collection unit can prioritize collecting information related to that event. For example, if the user is looking for information on a particular product, the data collection unit can prioritize collecting information on that product. This allows the data collection unit to collect highly relevant information based on the user's current areas of interest. 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 current areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0042] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering information. For example, if the user is in a specific region, the data collection unit will prioritize collecting event information related to that region. For example, if the user is traveling, the data collection unit will prioritize collecting information related to their travel destination. For example, if the user is at home, the data collection unit will prioritize collecting information related to their home area. This allows the data collection unit to collect highly relevant information based on the user's geographical location. 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 and have the AI ​​perform the information collection.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit may prioritize collecting information on accounts that the user follows on social media. For example, the data collection unit may collect relevant information based on information that the user has "liked" or shared on social media. For example, the data collection unit may collect information related to groups and events that the user participates in on social media. In this way, relevant information can be collected based on the user's social media activity. 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 may input the user's social media activity into AI and have AI perform the data collection.

[0044] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, the analysis unit may analyze important news and event information in detail and reflect it in the report. For example, the analysis unit may analyze general information concisely and reflect only the key points in the report. For example, the analysis unit may analyze information of particular interest to the user in detail and reflect it in the report. This allows the level of detail in the analysis to be adjusted based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to news information to extract key points. For example, the analysis unit can apply a schedule analysis algorithm to event information to extract important dates. For example, the analysis unit can apply a price analysis algorithm to product information to extract the optimal purchase timing. This allows the appropriate analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information and reflect it in the report. For example, the analysis unit may analyze past information according to its importance and reflect it in the report. For example, the analysis unit may prioritize the analysis of information of particular interest to the user, regardless of when it was collected. This allows the analysis priority to be determined based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information collection into the AI ​​and have the AI ​​determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information of interest to the user and reflect it in the report. For example, the analysis unit may group highly relevant information and analyze it all at once. For example, the analysis unit may postpone the analysis of less relevant information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0048] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit can provide reports in the format that the user has frequently viewed in the past. For example, the delivery unit can prioritize providing information that the user has preferred to view in the past. For example, the delivery unit can analyze the user's past viewing history and provide reports at the optimal time. This allows the delivery unit to select the optimal delivery method based on the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past report viewing history into AI and have the AI ​​select the optimal delivery method.

[0049] The service provider can provide customized reports based on the user's current areas of interest when delivering reports. For example, the service provider can prioritize providing information related to the user's current interests. For example, if the user is interested in a particular event, the service provider can provide information related to that event. For example, if the user is looking for information on a particular product, the service provider can prioritize providing information on that product. This allows the service provider to provide customized reports based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current areas of interest into the AI ​​and have the AI ​​perform the report customization.

[0050] The service provider can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a report adapted to the screen size. For example, if the user is using a tablet, the service provider will provide a report optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a report containing detailed information. This allows the service provider to select the optimal delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0051] The service provider can provide multilingual reports according to the user's language settings when providing reports. For example, the service provider can automatically set the report language based on the user's device language settings. For example, the service provider can provide a language switching function if the user uses multiple languages. For example, the service provider can provide the report in a specific language if the user selects one. This allows the service provider to provide multilingual reports based on the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into AI and have AI perform the task of providing multilingual reports.

[0052] The support unit can select the optimal support method by referring to the user's past purchase history during support. For example, the support unit can analyze the trends of products the user has purchased in the past and prioritize providing information on related products. For example, the support unit can analyze the timing of products the user has purchased in the past and notify the user of the optimal purchase timing. For example, the support unit can provide information on products with similar ratings based on the user's ratings of products they have purchased in the past. This allows the support unit to select the optimal support method based on the user's past purchase history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past purchase history into AI and have AI select the optimal support method.

[0053] The support unit can customize the support provided based on the user's current interests. For example, the support unit can prioritize providing information on products the user is currently interested in. For example, if the user is interested in a particular event, the support unit can provide information on products related to that event. For example, if the user is considering purchasing a particular product, the support unit can provide support for purchasing that product. This allows the support to be customized based on the user's current interests. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current interests into the AI ​​and have the AI ​​perform the customization of the support.

[0054] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit will prioritize providing information on products related to that region. For example, if the user is traveling, the support unit will prioritize providing information on recommended products related to the travel destination. For example, if the user is at home, the support unit will prioritize providing information on recommended products related to the user's home area. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into AI and have the AI ​​select the optimal support method.

[0055] The support department can analyze a user's social media activity and propose support during support sessions. For example, the support department can prioritize providing information on accounts the user follows on social media. For example, the support department can provide information on related products based on information the user has "liked" or shared on social media. For example, the support department can provide information on products related to groups or events the user participates in on social media. This allows the support department to propose support based on the user's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's social media activity into an AI and have the AI ​​propose support.

[0056] The proxy purchasing unit can select the optimal purchasing method by referring to the user's past purchase history when making a purchase on their behalf. For example, the proxy purchasing unit can analyze the trends of products the user has purchased in the past and prioritize the purchase of related products. For example, the proxy purchasing unit can analyze the timing of products the user has purchased in the past and make purchases at the optimal time. For example, the proxy purchasing unit can prioritize the purchase of products with similar ratings based on the user's ratings of products they have purchased in the past. This allows the system to select the optimal purchasing method based on the user's past purchase history. Some or all of the above processes in the proxy purchasing unit may be performed using AI, for example, or not. For example, the proxy purchasing unit can input the user's past purchase history into AI and have the AI ​​select the optimal purchasing method.

[0057] The proxy purchasing unit can customize purchases based on the user's current interests during the purchasing process. For example, the proxy purchasing unit prioritizes purchasing products related to events the user is currently interested in. For example, if the user is participating in a specific event, the proxy purchasing unit prioritizes purchasing products related to that event. For example, if the user is considering purchasing a specific product, the proxy purchasing unit prioritizes purchasing that product. This allows the purchase to be customized based on the user's current interests. Some or all of the above processing in the proxy purchasing unit may be performed using AI, for example, or without AI. For example, the proxy purchasing unit can input the user's current interests into the AI ​​and have the AI ​​perform the customization of the purchase.

[0058] The proxy purchasing unit can select the optimal purchasing method when making a purchase on behalf of a user, taking into account the user's geographical location. For example, if the user is in a specific region, the proxy purchasing unit will prioritize purchasing products related to that region. For example, if the user is traveling, the proxy purchasing unit will prioritize purchasing products related to their travel destination. For example, if the user is at home, the proxy purchasing unit will prioritize purchasing products related to their home area. This allows the system to select the optimal purchasing method based on the user's geographical location. Some or all of the above processing in the proxy purchasing unit may be performed using AI, for example, or without AI. For example, the proxy purchasing unit can input the user's geographical location information into AI and have the AI ​​select the optimal purchasing method.

[0059] The proxy purchasing unit can analyze a user's social media activity and suggest purchases during the proxy purchasing process. For example, the proxy purchasing unit can suggest product purchases based on information about accounts the user follows on social media. For example, the proxy purchasing unit can suggest product purchases based on information the user has "liked" or shared on social media. For example, the proxy purchasing unit can suggest product purchases related to groups or events the user participates in on social media. In this way, purchases can be suggested based on the user's social media activity. Some or all of the above processes in the proxy purchasing unit may be performed using AI, for example, or not using AI. For example, the proxy purchasing unit can input the user's social media activity into AI and have the AI ​​make purchase suggestions.

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

[0061] The fan activity support system can analyze a user's past information gathering history and select the optimal information gathering method. For example, it can prioritize collecting information from sources the user has frequently accessed in the past. It can collect information based on topics the user has shown interest in in the past. It can analyze the user's past information gathering patterns and collect information at the optimal time. This allows the system to select the optimal information gathering method based on the user's past information gathering history. Some or all of the above processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's past information gathering history into an AI and have the AI ​​select the optimal information gathering method.

[0062] The fan activity support system can prioritize the collection of highly relevant information by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of event information related to that region. If the user is traveling, it will prioritize the collection of information related to their travel destination. If the user is at home, it will prioritize the collection of information related to their home area. In this way, highly relevant information can be collected based on the user's geographical location. Some or all of the above processing in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's geographical location information into an AI and have the AI ​​perform the information collection.

[0063] The fan activity support system can analyze a user's social media activity and collect relevant information. For example, it can prioritize collecting information on accounts that the user follows on social media. It can also collect relevant information based on information that the user "likes" or shares on social media. Furthermore, it can collect information related to groups and events that the user participates in on social media. In this way, relevant information can be collected based on the user's social media activity. Some or all of the above processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's social media activity into an AI and have the AI ​​perform the information collection.

[0064] The fan support system can adjust the level of detail in its analysis based on the importance of the information. For example, important news and event information is analyzed in detail and reflected in the report. General information is analyzed concisely, and only the key points are reflected in the report. Information that the user is particularly interested in is analyzed in detail and reflected in the report. This allows the level of detail in the analysis to be adjusted based on the importance of the information. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the analysis.

[0065] The fan support system can apply different analysis algorithms depending on the category of information during analysis. For example, a natural language processing algorithm can be applied to news information to extract key points. A schedule analysis algorithm can be applied to event information to extract important dates. A price analysis algorithm can be applied to product information to extract the optimal purchase timing. This allows the system to apply the appropriate analysis algorithm according to the category of information. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the category of information into an AI and have the AI ​​perform the application of the analysis algorithm.

[0066] The fan support system can prioritize analysis based on the timing of information collection. For example, it can prioritize the analysis of the latest information and reflect it in the report. Past information can be analyzed according to its importance and reflected in the report. Information of particular interest to the user can be prioritized regardless of when it was collected. This allows the system to determine the priority of analysis based on the timing of information collection. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the timing of information collection into the AI ​​and have the AI ​​determine the priority of analysis.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit gathers information related to the favorite idol / celebrity. The collection unit gathers information related to the favorite idol / celebrity from sources such as social media and news sites. The collection unit gathers information such as the latest news and event information about the favorite idol / celebrity, and information on the sale of limited-edition merchandise. The collection unit can also automatically collect information related to the favorite idol / celebrity using AI, for example. Step 2: The analysis unit analyzes the information collected by the collection unit and creates a report to provide to the user. For example, the analysis unit analyzes the collected information and creates periodic reports. For example, the analysis unit can use AI to analyze the collected information and create a report to provide to the user. Step 3: The service provider provides the user with the reports created by the analysis unit. For example, the service provider provides the user with periodic reports. For example, the service provider can use AI to provide the user with the reports created by the analysis unit. Step 4: The support department provides real-time sales information for limited-edition products and assists with purchases. For example, the support department notifies users of the start time of sales for limited-edition products and helps them to make a purchase. For example, the support department can use AI to provide real-time sales information for limited-edition products and help users to make a purchase. Step 5: The proxy purchasing unit performs ticket purchases on behalf of customers. For example, the proxy purchasing unit automatically purchases tickets for live performances and events and provides them to users. For example, the proxy purchasing unit can use AI to perform ticket purchases on behalf of customers and provide them to users.

[0069] (Example of form 2) An embodiment of the present invention provides a support system for supporting one's favorite idol / celebrity, which efficiently collects, analyzes, and provides information related to that idol / celebrity. This support system is characterized by its AI compiling news about the idol / celebrity and providing regular reports. It also supports proxy purchases of tickets through online retailers. This makes it possible to enjoy supporting multiple idols / celebrities without burden. For example, the support system automatically collects information related to the idol / celebrity from social media and news sites using AI. For example, it collects the latest news, event information, and sales information for limited-edition merchandise related to the idol / celebrity. This allows users to easily obtain the latest information about their idol / celebrity. Next, the support system uses AI to analyze the collected information and creates regular reports for the user to provide. For example, once a week, it provides the user with a report summarizing the latest news, event information, and sales information for limited-edition merchandise related to the idol / celebrity. This allows users to efficiently engage in idol / celebrity activities without missing important information about their idol / celebrity. Furthermore, the support system provides sales information for limited-edition merchandise in real time and supports purchases. For example, it notifies users of the start time of sales for limited-edition merchandise and supports them in making purchases. This ensures that users can purchase limited-edition items without missing the opportunity. The fan support system also assists with ticket purchases. For example, the AI ​​automatically purchases tickets for concerts and events and provides them to users. This simplifies the ticket purchase process, allowing users to enjoy their fan activities more. This service solves challenges faced by fans and idol / character enthusiasts. Specifically, it addresses issues such as the time-consuming nature of information gathering, missing opportunities to purchase limited-edition items, and the frustration of popular items selling out. For instance, if a user wants to know the latest news about their favorite idol, the AI ​​automatically collects information and provides it as a regular report. It also provides real-time notifications about sales of limited-edition items, supporting users in securing their purchases. Furthermore, the AI ​​purchases tickets for concerts and events on behalf of users and provides them to them.In this way, AI-powered fan support services allow users to efficiently engage in fan activities, easily obtain information related to their favorite idols, and reliably purchase limited-edition merchandise. This makes it possible to enjoy multiple fan activities without burden, improving user satisfaction. As a result, the fan support system can efficiently collect, analyze, and provide information related to the idols that users are interested in.

[0070] The idol fan support system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a support unit, and a proxy purchase unit. The collection unit collects information related to the idol. The collection unit collects information related to the idol from, for example, social media and news sites. The collection unit collects, for example, the latest news and event information about the idol, and sales information for limited edition products. The collection unit can automatically collect information related to the idol using, for example, AI. The analysis unit analyzes the information collected by the collection unit and creates reports to be provided to the user. The analysis unit analyzes the collected information and creates periodic reports. The analysis unit can analyze the collected information and create reports to be provided to the user using, for example, AI. The provision unit provides the reports created by the analysis unit to the user. The provision unit provides, for example, periodic reports to the user. The provision unit can provide the reports created by the analysis unit to the user using, for example, AI. The support unit provides sales information for limited edition products in real time and supports purchases. The support unit notifies the user of the start time of sales for limited edition products and supports the user in making a purchase. The support unit can, for example, use AI to provide real-time sales information on limited-edition products and support users in making purchases. The proxy purchase unit performs proxy purchases of tickets for mail-order sales. The proxy purchase unit can, for example, automatically purchase tickets for live performances and events and provide them to users. The proxy purchase unit can, for example, use AI to perform proxy purchases of tickets for mail-order sales and provide them to users. As a result, the fan activity support system according to the embodiment can efficiently collect, analyze, and provide information related to the user's favorite idol. Some or all of the above-described processes in the fan activity support system may be performed using AI, for example, or without AI. For example, the fan activity support system can input information collected by the collection unit into the AI ​​and have the AI ​​perform the analysis of the information.

[0071] The data collection unit collects information related to the idol / celebrity. For example, it collects information from social media and news sites. Specifically, it collects data from social media platforms such as the idol's latest posts, fan comments, retweets, and likes. It also collects articles, interviews, and event announcements from news sites. Furthermore, the unit obtains information from the idol's official website and fan club site to stay informed about their latest activities and releases. The data collection unit can automatically collect this information using AI. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract keywords and hashtags related to the idol. This allows the data collection unit to efficiently collect important information about the idol from a vast amount of data. Additionally, the data collection unit centrally manages the collected data and stores it in a database. This allows the analysis and provisioning units to quickly access the necessary information. The data collection unit can flexibly set the frequency and scope of information collection, strengthening information gathering at important times, such as during specific events or new product releases. This allows the data collection department to support users in staying up-to-date on the latest information about their favorite idols.

[0072] The analysis unit analyzes the information collected by the data collection unit and creates reports to provide to users. For example, the analysis unit analyzes the collected information and creates periodic reports. Specifically, the analysis unit classifies the collected data and analyzes the activity status of the idol, fan reactions, trends, etc. Using AI, it can analyze the collected information and create reports to provide to users. For example, it can use machine learning algorithms to predict fluctuations in the popularity of the idol and increases in fan interest. It can also use natural language processing technology to analyze social media posts and extract fan sentiments and opinions. This allows the analysis unit to report to users in detail the latest trends of the idol and fan reactions. Furthermore, the analysis unit can evaluate the effectiveness of the idol's activities and changes in trends by comparing them with past data. For example, it can analyze the success rate of past events and releases to make predictions for future activities. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to provide users with not only real-time situational awareness but also long-term trend analysis and anomaly detection, improving the reliability and usefulness of the entire idol support system.

[0073] The service provider delivers reports created by the analytics department to users. For example, the service provider provides users with regular reports. Specifically, the service provider customizes the reports created by the analytics department to the user's preferences and delivers them via email or a dedicated app. Using AI, the reports created by the analytics department can be delivered to users. For example, the content of the report can be personalized based on the user's past browsing history and interests. The service provider can also adjust the timing of report delivery to match the user's lifestyle. For example, by delivering reports during the morning commute or evening relaxation time, the information can be delivered at the time when the user is most likely to receive it. Furthermore, the service provider visualizes the data using graphs and charts to make the report content visually easy to understand. This allows users to grasp the activity status and trends of their favorite idols at a glance. The service provider can collect feedback from users and continuously improve the content and delivery methods of the reports. For example, it can reflect the information that users are particularly interested in and their opinions on the report format to provide more user-friendly reports. In this way, the service provider can always provide users with the latest and most useful information and support their idol activities.

[0074] The support department provides real-time sales information for limited-edition products and assists with purchases. Specifically, the support department notifies users of the start time of sales for limited-edition products and supports them in making purchases. By using AI, the support department can provide real-time sales information for limited-edition products and support users in making purchases. For example, the AI ​​monitors updates on the sales site and instantly detects the start time of sales for limited-edition products. It also sends individually customized notifications based on the user's purchase history and products of interest. This allows users to act quickly without missing important sales information. Furthermore, the support department also assists in simplifying the purchase process. For example, by registering the user's payment information and shipping address information in advance, the purchase process can be carried out smoothly. In addition, the support department can send notifications through multiple devices and platforms. For example, it can use a combination of smartphone push notifications, email, and SMS to ensure that information is delivered reliably. In this way, the support department helps users reliably purchase limited-edition products and makes their fan activities more fulfilling.

[0075] The ticket purchasing service acts as an agent for online ticket sales. Specifically, it automatically purchases tickets for live performances and events and provides them to users. It can utilize AI to purchase tickets online and provide them to users. For example, the AI ​​monitors updates on ticket sales websites and instantly detects the start time of sales. It also selects the most suitable tickets based on the user's desired seat type and price range, and automatically handles the purchase process. This allows users to secure their desired tickets without having to participate in the competition. Furthermore, the ticket purchasing service manages purchased tickets and handles delivery procedures. For example, it registers purchased tickets to the user's account and provides them as electronic tickets. For paper tickets, it arranges delivery to the specified address. The ticket purchasing service can also prioritize purchasing tickets for specific events or live performances upon user request. This allows the service to help users ensure they can attend their desired events and live performances, enriching their fan activities.

[0076] The collection unit can collect information related to one's favorite idol or celebrity from social media and news sites. The collection unit can, for example, collect information related to one's favorite idol or celebrity from social media. The collection unit can, for example, collect information related to one's favorite idol or celebrity from news sites. The collection unit can, for example, use AI to automatically collect information related to one's favorite idol or celebrity from social media and news sites. This allows for the collection of information related to one's favorite idol or celebrity from a wide range of sources. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information collected from social media and news sites into AI and have AI perform the information collection.

[0077] The analysis unit can analyze the collected information and generate periodic reports. For example, the analysis unit can analyze the collected information and generate periodic reports. The analysis unit can, for example, use AI to analyze the collected information and generate periodic reports. This allows for the analysis of collected information and the provision of periodic reports. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected information into the AI ​​and have the AI ​​perform the analysis of the information.

[0078] The service provider can provide periodic reports to users. The service provider can, for example, provide periodic reports to users. The service provider can, for example, use AI to provide periodic reports to users. This allows the service provider to provide reports to users on a regular basis. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input periodic reports into AI and have AI perform the task of providing the reports.

[0079] The support unit can notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. The support unit can, for example, notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. The support unit can, for example, use AI to notify users of the start time of sales for limited-edition products and support them in ensuring they can purchase them. This ensures that users can purchase limited-edition products. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the start time of sales for limited-edition products into the AI ​​and have the AI ​​perform the notification support.

[0080] The ticket purchasing unit can automatically purchase tickets for live performances and events and provide them to users. For example, the ticket purchasing unit can automatically purchase tickets for live performances and events and provide them to users. The ticket purchasing unit can, for example, use AI to automatically purchase tickets for live performances and events and provide them to users. This makes it easy for users to obtain tickets for live performances and events. Some or all of the above-described processes in the ticket purchasing unit may be performed using AI, or not. For example, the ticket purchasing unit can input the live performance or event tickets into the AI ​​and have the AI ​​execute the ticket purchase.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is excited, the data collection unit can collect the latest information related to their favorite things in real time and provide it immediately. For example, if the user is relaxed, the data collection unit can collect information periodically and provide it all at once. For example, if the user is busy, the data collection unit can prioritize collecting only important information and provide it all at once later. This allows for more appropriate information to be provided by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of information collection.

[0082] The data collection unit can analyze the user's past information gathering history and select the optimal data collection method. For example, the data collection unit may prioritize collecting information from sources that the user has frequently accessed in the past. For example, the data collection unit may collect information based on topics that the user has shown interest in in the past. For example, the data collection unit may analyze the user's past information gathering patterns and collect information at the optimal time. This allows the data collection unit to select the optimal data collection method based on the user's past information gathering 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 may input the user's past information gathering history into AI and have AI select the optimal data collection method.

[0083] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can collect only information related to the user's current interests. For example, if the user is interested in a particular event, the data collection unit can prioritize collecting information related to that event. For example, if the user is looking for information on a particular product, the data collection unit can prioritize collecting information on that product. This allows the data collection unit to collect highly relevant information based on the user's current areas of interest. 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 current areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0084] The data collection 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 excited, the data collection unit will prioritize collecting the latest news and event information. If the user is relaxed, the data collection unit will prioritize collecting detailed and background information. If the user is busy, the data collection unit will prioritize collecting only important information. This allows the data collection unit to determine the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the determination of information prioritization.

[0085] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering information. For example, if the user is in a specific region, the data collection unit will prioritize collecting event information related to that region. For example, if the user is traveling, the data collection unit will prioritize collecting information related to their travel destination. For example, if the user is at home, the data collection unit will prioritize collecting information related to their home area. This allows the data collection unit to collect highly relevant information based on the user's geographical location. 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 and have the AI ​​perform the information collection.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit may prioritize collecting information on accounts that the user follows on social media. For example, the data collection unit may collect relevant information based on information that the user has "liked" or shared on social media. For example, the data collection unit may collect information related to groups and events that the user participates in on social media. In this way, relevant information can be collected based on the user's social media activity. 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 may input the user's social media activity into AI and have AI perform the data collection.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is excited, the analysis unit provides a report with a visually stimulating design. For example, if the user is relaxed, the analysis unit provides a report with a calm design. For example, if the user is busy, the analysis unit provides a concise report that gets straight to the point. This allows the presentation of the report to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the report.

[0088] The analysis unit can adjust the level of detail in its analysis based on the importance of the information. For example, the analysis unit may analyze important news and event information in detail and reflect it in the report. For example, the analysis unit may analyze general information concisely and reflect only the key points in the report. For example, the analysis unit may analyze information of particular interest to the user in detail and reflect it in the report. This allows the level of detail in the analysis to be adjusted based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to news information to extract key points. For example, the analysis unit can apply a schedule analysis algorithm to event information to extract important dates. For example, the analysis unit can apply a price analysis algorithm to product information to extract the optimal purchase timing. This allows the appropriate analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is busy, the analysis unit provides a short, concise report. If the user is relaxed, the analysis unit provides a longer report with more detailed information. If the user is excited, the analysis unit provides a report with visually stimulating effects. This allows the length of the report to be adjusted according to the user's emotions. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the length of the report.

[0091] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information and reflect it in the report. For example, the analysis unit may analyze past information according to its importance and reflect it in the report. For example, the analysis unit may prioritize the analysis of information of particular interest to the user, regardless of when it was collected. This allows the analysis priority to be determined based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information collection into the AI ​​and have the AI ​​determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information of interest to the user and reflect it in the report. For example, the analysis unit may group highly relevant information and analyze it all at once. For example, the analysis unit may postpone the analysis of less relevant information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0093] The service provider can estimate the user's emotions and adjust the report delivery method based on the estimated emotions. For example, if the user is excited, the service provider can provide reports in real time. For example, if the user is relaxed, the service provider can provide reports periodically. For example, if the user is busy, the service provider can prioritize providing only important information. This allows the service provider to adjust the report delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​adjust the report delivery method.

[0094] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit can provide reports in the format that the user has frequently viewed in the past. For example, the delivery unit can prioritize providing information that the user has preferred to view in the past. For example, the delivery unit can analyze the user's past viewing history and provide reports at the optimal time. This allows the delivery unit to select the optimal delivery method based on the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past report viewing history into AI and have the AI ​​select the optimal delivery method.

[0095] The service provider can provide customized reports based on the user's current areas of interest when delivering reports. For example, the service provider can prioritize providing information related to the user's current interests. For example, if the user is interested in a particular event, the service provider can provide information related to that event. For example, if the user is looking for information on a particular product, the service provider can prioritize providing information on that product. This allows the service provider to provide customized reports based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current areas of interest into the AI ​​and have the AI ​​perform the report customization.

[0096] The service provider can estimate the user's emotions and adjust the frequency of report delivery based on the estimated emotions. For example, if the user is excited, the service provider will provide reports frequently. For example, if the user is relaxed, the service provider will provide reports regularly. For example, if the user is busy, the service provider will prioritize providing only important information. This allows the service provider to adjust the frequency of report delivery according to the user's emotions. 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 above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​adjust the frequency of report delivery.

[0097] The service provider can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a report adapted to the screen size. For example, if the user is using a tablet, the service provider will provide a report optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a report containing detailed information. This allows the service provider to select the optimal delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0098] The service provider can provide multilingual reports according to the user's language settings when providing reports. For example, the service provider can automatically set the report language based on the user's device language settings. For example, the service provider can provide a language switching function if the user uses multiple languages. For example, the service provider can provide the report in a specific language if the user selects one. This allows the service provider to provide multilingual reports based on the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into AI and have AI perform the task of providing multilingual reports.

[0099] The support unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is excited, the support unit will send notifications in real time. For example, if the user is relaxed, the support unit will send notifications periodically. For example, if the user is busy, the support unit will prioritize sending only important information. This allows the timing of notifications to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into AI and have the AI ​​adjust the timing of notifications.

[0100] The support unit can select the optimal support method by referring to the user's past purchase history during support. For example, the support unit can analyze the trends of products the user has purchased in the past and prioritize providing information on related products. For example, the support unit can analyze the timing of products the user has purchased in the past and notify the user of the optimal purchase timing. For example, the support unit can provide information on products with similar ratings based on the user's ratings of products they have purchased in the past. This allows the support unit to select the optimal support method based on the user's past purchase history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past purchase history into AI and have AI select the optimal support method.

[0101] The support unit can customize the support provided based on the user's current interests. For example, the support unit can prioritize providing information on products the user is currently interested in. For example, if the user is interested in a particular event, the support unit can provide information on products related to that event. For example, if the user is considering purchasing a particular product, the support unit can provide support for purchasing that product. This allows the support to be customized based on the user's current interests. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current interests into the AI ​​and have the AI ​​perform the customization of the support.

[0102] The support unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is excited, the support unit will prioritize important information. If the user is relaxed, the support unit will provide notifications containing detailed information. If the user is busy, the support unit will provide concise notifications that get straight to the point. This allows the support unit to determine notification priorities according to the user's emotions. 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 above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into an AI and have the AI ​​determine notification priorities.

[0103] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit will prioritize providing information on products related to that region. For example, if the user is traveling, the support unit will prioritize providing information on recommended products related to the travel destination. For example, if the user is at home, the support unit will prioritize providing information on recommended products related to the user's home area. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into AI and have the AI ​​select the optimal support method.

[0104] The support department can analyze a user's social media activity and propose support during support sessions. For example, the support department can prioritize providing information on accounts the user follows on social media. For example, the support department can provide information on related products based on information the user has "liked" or shared on social media. For example, the support department can provide information on products related to groups or events the user participates in on social media. This allows the support department to propose support based on the user's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's social media activity into an AI and have the AI ​​propose support.

[0105] The proxy purchasing unit can estimate the user's emotions and adjust the timing of purchases based on those emotions. For example, if the user is excited, the proxy purchasing unit will make a purchase in real time. If the user is relaxed, the proxy purchasing unit will make a purchase at the optimal time. If the user is busy, the proxy purchasing unit will prioritize purchasing only important items. This allows the timing of purchases to be adjusted according to the user's emotions. 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 above processing in the proxy purchasing unit may be performed using AI or not. For example, the proxy purchasing unit can input user emotion data into an AI and have the AI ​​adjust the timing of purchases.

[0106] The proxy purchasing unit can select the optimal purchasing method by referring to the user's past purchase history when making a purchase on their behalf. For example, the proxy purchasing unit can analyze the trends of products the user has purchased in the past and prioritize the purchase of related products. For example, the proxy purchasing unit can analyze the timing of products the user has purchased in the past and make purchases at the optimal time. For example, the proxy purchasing unit can prioritize the purchase of products with similar ratings based on the user's ratings of products they have purchased in the past. This allows the system to select the optimal purchasing method based on the user's past purchase history. Some or all of the above processes in the proxy purchasing unit may be performed using AI, for example, or not. For example, the proxy purchasing unit can input the user's past purchase history into AI and have the AI ​​select the optimal purchasing method.

[0107] The proxy purchasing unit can customize purchases based on the user's current interests during the purchasing process. For example, the proxy purchasing unit prioritizes purchasing products related to events the user is currently interested in. For example, if the user is participating in a specific event, the proxy purchasing unit prioritizes purchasing products related to that event. For example, if the user is considering purchasing a specific product, the proxy purchasing unit prioritizes purchasing that product. This allows the purchase to be customized based on the user's current interests. Some or all of the above processing in the proxy purchasing unit may be performed using AI, for example, or without AI. For example, the proxy purchasing unit can input the user's current interests into the AI ​​and have the AI ​​perform the customization of the purchase.

[0108] The proxy purchasing unit can estimate the user's emotions and determine purchase priorities based on those emotions. For example, if the user is excited, the proxy purchasing unit will prioritize purchasing important items. If the user is relaxed, the proxy purchasing unit will prioritize purchasing items containing detailed information. If the user is busy, the proxy purchasing unit will prioritize purchasing concise items that get straight to the point. This allows the purchase priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 above processing in the proxy purchasing unit may be performed using AI or not. For example, the proxy purchasing unit can input user emotion data into an AI and have the AI ​​determine the purchase priorities.

[0109] The proxy purchasing unit can select the optimal purchasing method when making a purchase on behalf of a user, taking into account the user's geographical location. For example, if the user is in a specific region, the proxy purchasing unit will prioritize purchasing products related to that region. For example, if the user is traveling, the proxy purchasing unit will prioritize purchasing products related to their travel destination. For example, if the user is at home, the proxy purchasing unit will prioritize purchasing products related to their home area. This allows the system to select the optimal purchasing method based on the user's geographical location. Some or all of the above processing in the proxy purchasing unit may be performed using AI, for example, or without AI. For example, the proxy purchasing unit can input the user's geographical location information into AI and have the AI ​​select the optimal purchasing method.

[0110] The proxy purchasing unit can analyze a user's social media activity and suggest purchases during the proxy purchasing process. For example, the proxy purchasing unit can suggest product purchases based on information about accounts the user follows on social media. For example, the proxy purchasing unit can suggest product purchases based on information the user has "liked" or shared on social media. For example, the proxy purchasing unit can suggest product purchases related to groups or events the user participates in on social media. In this way, purchases can be suggested based on the user's social media activity. Some or all of the above processes in the proxy purchasing unit may be performed using AI, for example, or not using AI. For example, the proxy purchasing unit can input the user's social media activity into AI and have the AI ​​make purchase suggestions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The fan activity support system can estimate the user's emotions and provide customized information related to their favorite idol / idol based on those emotions. For example, if the user is excited, it can prioritize providing the latest news and event information. If the user is relaxed, it can provide detailed and background information. If the user is busy, it can provide only the essential information concisely. This allows for information provision tailored to the user's emotions, thereby improving user satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above-described processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input user emotion data into AI and have the AI ​​perform the information customization.

[0113] The fan activity support system can analyze a user's past information gathering history and select the optimal information gathering method. For example, it can prioritize collecting information from sources the user has frequently accessed in the past. It can collect information based on topics the user has shown interest in in the past. It can analyze the user's past information gathering patterns and collect information at the optimal time. This allows the system to select the optimal information gathering method based on the user's past information gathering history. Some or all of the above processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's past information gathering history into an AI and have the AI ​​select the optimal information gathering method.

[0114] The fan activity support system can prioritize the collection of highly relevant information by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of event information related to that region. If the user is traveling, it will prioritize the collection of information related to their travel destination. If the user is at home, it will prioritize the collection of information related to their home area. In this way, highly relevant information can be collected based on the user's geographical location. Some or all of the above processing in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's geographical location information into an AI and have the AI ​​perform the information collection.

[0115] The fan activity support system can analyze a user's social media activity and collect relevant information. For example, it can prioritize collecting information on accounts that the user follows on social media. It can also collect relevant information based on information that the user "likes" or shares on social media. Furthermore, it can collect information related to groups and events that the user participates in on social media. In this way, relevant information can be collected based on the user's social media activity. Some or all of the above processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input the user's social media activity into an AI and have the AI ​​perform the information collection.

[0116] The fan activity support system can estimate the user's emotions and adjust the presentation of reports based on those emotions. For example, if the user is excited, it can provide a report with a visually stimulating design. If the user is relaxed, it can provide a report with a calm design. If the user is busy, it can provide a concise report that gets straight to the point. This allows the presentation of reports to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input user emotion data into AI and have the AI ​​adjust the presentation of reports.

[0117] The fan support system can adjust the level of detail in its analysis based on the importance of the information. For example, important news and event information is analyzed in detail and reflected in the report. General information is analyzed concisely, and only the key points are reflected in the report. Information that the user is particularly interested in is analyzed in detail and reflected in the report. This allows the level of detail in the analysis to be adjusted based on the importance of the information. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the analysis.

[0118] The fan support system can apply different analysis algorithms depending on the category of information during analysis. For example, a natural language processing algorithm can be applied to news information to extract key points. A schedule analysis algorithm can be applied to event information to extract important dates. A price analysis algorithm can be applied to product information to extract the optimal purchase timing. This allows the system to apply the appropriate analysis algorithm according to the category of information. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the category of information into an AI and have the AI ​​perform the application of the analysis algorithm.

[0119] The fan activity support system can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is busy, it provides a short, concise report. If the user is relaxed, it provides a longer report with detailed information. If the user is excited, it provides a report with visually stimulating effects. This allows the length of the report to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input user emotion data into AI and have the AI ​​adjust the length of the report.

[0120] The fan support system can prioritize analysis based on the timing of information collection. For example, it can prioritize the analysis of the latest information and reflect it in the report. Past information can be analyzed according to its importance and reflected in the report. Information of particular interest to the user can be prioritized regardless of when it was collected. This allows the system to determine the priority of analysis based on the timing of information collection. Some or all of the above processes in the fan support system may be performed using AI or not. For example, the fan support system can input the timing of information collection into the AI ​​and have the AI ​​determine the priority of analysis.

[0121] The fan activity support system can estimate the user's emotions and adjust the way reports are delivered based on those estimated emotions. For example, if the user is excited, reports are provided in real time. If the user is relaxed, reports are provided periodically. If the user is busy, only important information is prioritized. This allows the system to adjust the way reports are delivered according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processes in the fan activity support system may be performed using AI or not. For example, the fan activity support system can input user emotion data into AI and have the AI ​​adjust the way reports are delivered.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit gathers information related to the favorite idol / celebrity. The collection unit gathers information related to the favorite idol / celebrity from sources such as social media and news sites. The collection unit gathers information such as the latest news and event information about the favorite idol / celebrity, and information on the sale of limited-edition merchandise. The collection unit can also automatically collect information related to the favorite idol / celebrity using AI, for example. Step 2: The analysis unit analyzes the information collected by the collection unit and creates a report to provide to the user. For example, the analysis unit analyzes the collected information and creates periodic reports. For example, the analysis unit can use AI to analyze the collected information and create a report to provide to the user. Step 3: The service provider provides the user with the reports created by the analysis unit. For example, the service provider provides the user with periodic reports. For example, the service provider can use AI to provide the user with the reports created by the analysis unit. Step 4: The support department provides real-time sales information for limited-edition products and assists with purchases. For example, the support department notifies users of the start time of sales for limited-edition products and helps them to make a purchase. For example, the support department can use AI to provide real-time sales information for limited-edition products and help users to make a purchase. Step 5: The proxy purchasing unit performs ticket purchases on behalf of customers. For example, the proxy purchasing unit automatically purchases tickets for live performances and events and provides them to users. For example, the proxy purchasing unit can use AI to perform ticket purchases on behalf of customers and provide them to users.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, and proxy purchase unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information related to the fandom using the camera 42 and communication I / F 44 of the smart device 14 and processes the information with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and creates a report. The provision unit provides the report to the user using, for example, the output device 40 of the smart device 14. The support unit provides sales information for limited products in real time using the communication I / F 44 of the smart device 14 and supports the purchase. The proxy purchase unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which performs proxy purchases of tickets via mail order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, and proxy purchase unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information related to the fandom using the camera 42 and communication I / F 44 of the smart glasses 214 and processes the information with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which analyzes the collected information and creates a report. The provision unit provides the report to the user using, for example, the speaker 240 of the smart glasses 214. The support unit provides sales information for limited products in real time using the communication I / F 44 of the smart glasses 214 and supports the purchase. The proxy purchase unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which performs proxy purchases of tickets via mail order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, and proxy purchase unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information related to the fandom using the camera 42 and communication I / F 44 of the headset terminal 314 and processes the information with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and creates a report. The provision unit provides the report to the user using the display 343 of the headset terminal 314. The support unit provides sales information for limited products in real time using the communication I / F 44 of the headset terminal 314 and supports purchases. The proxy purchase unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which performs proxy purchases of tickets via mail order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, support unit, and proxy purchase unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects push-related information using the camera 42 and communication I / F 44 of the robot 414 and processes the information with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and creates a report. The provision unit provides the report to the user, for example, using the speaker 240 of the robot 414. The support unit provides sales information for limited products in real time using the communication I / F 44 of the robot 414 and supports purchases. The proxy purchase unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which performs proxy purchases of tickets via mail order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection department that gathers information related to their favorite idol, An analysis unit analyzes the information collected by the aforementioned collection unit and creates a report to be provided to the user, A provisioning unit that provides the user with the report created by the analysis unit, A support department provides real-time sales information on limited-edition products and assists with purchases. It includes a purchasing agency department that handles the purchase of tickets through mail order. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information about your favorite idol from social media and news sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected information and create periodic reports. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide regular reports to users The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We notify users of the start time for limited-edition products and support them in ensuring they can purchase them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned purchasing agency department, Automatically purchases and provides tickets for live performances and events to users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is 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 11) The aforementioned collection unit is When collecting 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 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, 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 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate user sentiment and adjust how reports are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing reports, the system selects the most suitable delivery method by referring to the user's past report viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing reports, we offer customized reports based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We estimate user sentiment and adjust the frequency of report delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing reports, the optimal delivery method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing reports, we will provide multilingual reports according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, the system will refer to the user's past purchase history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, customize the support content based on the user's current product interests. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity and propose support tailored to their needs. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned purchasing agency department, It estimates the user's emotions and adjusts the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned purchasing agency department, When making a purchase on behalf of someone else, the system selects the most suitable purchase method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned purchasing agency department, When purchasing on behalf of someone else, customize the purchase based on the user's current interests and events. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned purchasing agency department, It estimates user emotions and determines purchase priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned purchasing agency department, When making a purchase on behalf of someone else, the system selects the optimal purchase method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned purchasing agency department, When making a purchase on behalf of someone else, we analyze the user's social media activity and suggest suitable purchase options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 gathers information related to their favorite idol, An analysis unit analyzes the information collected by the aforementioned collection unit and creates a report to be provided to the user, A provisioning unit that provides the user with the report created by the analysis unit, A support department provides real-time sales information on limited-edition products and assists with purchases. It includes a purchasing agency department that handles the purchase of tickets through mail order. A system characterized by the following features.

2. The aforementioned collection unit is Gather information about your favorite idol from social media and news sites. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected information and create periodic reports. The system according to feature 1.

4. The aforementioned supply unit is, Provide regular reports to users The system according to feature 1.

5. The aforementioned support unit is We notify users of the start time for limited-edition products and support them in ensuring they can purchase them. The system according to feature 1.

6. The aforementioned purchasing agency department, Automatically purchases and provides tickets for live performances and events to users. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system according to feature 1.

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

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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