system

Abstract

The system according to this embodiment aims to analyze idol activity data and fan behavior data to propose effective marketing strategies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects fan behavior data and idol activity data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides content based on the analysis results obtained by the analysis unit. The proposal unit proposes a marketing strategy based on the content provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to effectively utilize the idle activity data and fan behavior data to propose a marketing strategy, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the idle activity data and fan behavior data and propose an effective marketing strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects fan behavior data and idol activity data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides content based on the analysis results obtained by the analysis unit. The proposal unit proposes a marketing strategy based on the content provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze idol activity data and fan behavior data to propose effective marketing strategies. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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) The system according to an embodiment of the present invention is a system that uses AI to individually customize the provision of idol information, event notifications, and benefits to enhance fan engagement. This system collects fan behavior data and idol activity data and analyzes this data. Next, based on the analysis results, it provides content optimized for each individual fan. This improves fan engagement and increases event participation rates. Furthermore, the AI ​​proposes effective marketing strategies to maximize marketing effectiveness. First, the system collects fan behavior data and idol activity data. At this time, it collects detailed data such as what actions fans have taken, which events they have participated in, and which content they prefer. For example, by collecting data on events fans have attended and goods they have purchased, and having the AI ​​analyze it, it is possible to understand the interests and preferences of the fans. This makes it possible to provide content optimized for each individual fan. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns the fans' behavior patterns and preferences. For example, it can identify fans who have a high level of interest in a particular idol and provide appropriate content to those fans. This improves fan engagement and increases event participation rates. Furthermore, the AI ​​proposes effective marketing strategies. AI develops optimal marketing strategies based on fan behavior data and idol activity data. For example, by providing promotional information related to a specific event to fans who show a high level of interest in that event, event attendance rates can be increased. This maximizes marketing effectiveness. This mechanism improves fan engagement rates and increases event attendance. Fans feel a deeper engagement when they are provided with content tailored to their preferences and behaviors. In addition, idols and management teams can strengthen their relationships with fans and build a more active and participatory fan community. For example, if a fan attends an event by a particular idol, providing benefits and information related to that event can improve fan satisfaction.Furthermore, providing information related to merchandise purchased by fans can keep them interested. This improves fan engagement and increases event participation rates. In this way, by using AI to individually customize information provision, event notifications, and perks for idols, fan engagement can be enhanced. AI can also propose effective marketing strategies and maximize marketing effectiveness. This strengthens the relationship between idols and fans and builds a more active and participatory fan community. The system collects and analyzes fan behavior data and idol activity data to propose optimal content delivery and marketing strategies.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects fan behavior data and idol activity data. The collection unit collects data such as events attended by fans and merchandise purchased by fans. The collection unit can collect detailed data such as what actions fans took, which events they participated in, and which content they preferred. For example, the collection unit can understand fans' interests and preferences by collecting data on events attended by fans and having the AI ​​analyze it. The collection unit can understand fans' interests and preferences by collecting data on merchandise purchased by fans and having the AI ​​analyze it. The collection unit can understand fans' interests and preferences by collecting fan behavior data and having the AI ​​analyze it. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and learns fans' behavior patterns and preferences. For example, the analysis unit can identify fans who have a high level of interest in a particular idol and provide appropriate content to those fans. By learning fans' behavior patterns and preferences, the analysis unit can provide more appropriate content. The analysis department can analyze the collected data and learn fan behavior patterns and preferences. The content delivery department provides content based on the analysis results obtained by the analysis department. The content delivery department provides content optimized for each individual fan based on the analysis results. For example, the content delivery department can provide content related to a particular idol to fans who have a high level of interest in that idol. By providing content optimized for each individual fan based on the analysis results, the content delivery department can improve engagement. The content delivery department can provide content optimized for each individual fan based on the analysis results. The proposal department proposes marketing strategies based on the content provided by the content delivery department. The proposal department formulates optimal marketing strategies based on fan behavior data and idol activity data. For example, the proposal department can improve event participation rates by providing promotional information related to a particular event to fans who have a high level of interest in that event.The proposal department can maximize marketing effectiveness by formulating optimal marketing strategies based on fan behavior data and idol activity data. As a result, the system according to the embodiment can collect and analyze fan behavior data and idol activity data, enabling the provision of optimal content and the proposal of marketing strategies.

[0030] The data collection unit collects fan behavior data and idol activity data. Specifically, it collects data on events fans attend and merchandise they purchase. For example, it can collect data on ticket purchase history for events fans attend, check-in information on the day of the event, and activities during the event (e.g., purchases at specific booths or workshops attended). Data on merchandise purchased by fans includes purchase history from online stores and merchandise booths, the type and quantity of items purchased, and the date and time of purchase. This data is important for understanding fans' interests and preferences. The data collection unit centrally manages this data, and by analyzing it with AI, it is possible to understand fans' behavior patterns and preferences in detail. For example, it is possible to identify fans who frequently purchase merchandise from a particular idol or fans who repeatedly attend specific events. Furthermore, the data collection unit can also collect data from online platforms such as social media and blogs. By collecting comments, reviews, likes, and shares posted by fans and analyzing them with AI, it is possible to understand fans' emotions and opinions. In this way, the data collection unit can comprehensively collect fan behavior data and idol activity data and provide a foundation for detailed analysis.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, AI analyzes the collected data to learn fan behavior patterns and preferences. For example, it can identify fans with a high level of interest in a particular idol and provide them with appropriate content. The AI ​​uses machine learning algorithms to extract patterns from fan behavior data and predict fan interests and preferences. For example, based on past event attendance history and merchandise purchase history, it can predict what kind of event a fan is likely to attend next or what kind of merchandise they are likely to purchase. In addition, by analyzing data collected from social media and blogs and understanding fans' emotions and opinions, it can provide content that meets fans' needs and expectations. Furthermore, the analysis department can create fan segments based on the collected data. For example, by dividing fans into different segments such as those with a high level of interest in a particular idol, those with a high event attendance rate, and those with a high frequency of merchandise purchases, it is possible to formulate more targeted marketing strategies. In this way, the analysis department can support the provision of more appropriate content and the formulation of marketing strategies by analyzing the collected data in detail and learning fan behavior patterns and preferences.

[0032] The content delivery department provides content based on the analysis results obtained by the analysis department. Specifically, it provides content optimized for each individual fan based on the analysis results. For example, it can provide content related to a particular idol to fans who have a high level of interest in that idol. The content delivery department uses AI to generate personalized content based on fans' interests and preferences. For example, it can provide the latest news and event information, as well as information on the sale of limited edition goods for a particular idol. In addition, the content delivery department can recommend content that fans are likely to be interested in based on their behavioral data. For example, it can notify fans who have previously attended an event of a particular idol about information on the next event, or provide information on new related products to fans who have purchased a particular item, thus providing content that matches the fans' interests and preferences. Furthermore, the content delivery department can collect fan feedback and continuously improve the quality of the content it provides. For example, by collecting fan reactions and evaluations of the content provided and having AI analyze them, it can provide content that better meets the needs of fans. In this way, the content delivery department can provide content optimized for each individual fan and improve engagement.

[0033] The Proposal Department proposes marketing strategies based on the content provided by the Provider Department. Specifically, it formulates optimal marketing strategies based on fan behavior data and idol activity data. For example, by providing promotional information related to a particular event to fans who have a high level of interest in that event, it is possible to increase event participation rates. The Proposal Department uses AI to analyze fan behavior data and idol activity data to formulate optimal marketing strategies. For example, based on past event participation history and merchandise purchase history, it can predict what kind of events and merchandise will be well-received by fans next and propose a promotional strategy based on that. In addition, by analyzing data collected from social media and blogs and understanding fans' emotions and opinions, it is possible to formulate marketing strategies that meet fans' needs and expectations. Furthermore, the Proposal Department can continuously monitor the effectiveness of the marketing strategies and modify them as needed. For example, it can analyze the effectiveness of promotional campaigns and revise the strategy if the effect is low, or further strengthen it if the effect is high, responding flexibly. In this way, the Proposal Department can formulate optimal marketing strategies based on fan behavior data and idol activity data and maximize marketing effectiveness.

[0034] The benefits section includes a section for providing benefits. The benefits section can provide benefits such as limited edition goods, event invitations, and point systems. The benefits section can provide benefits based on fan behavior data. By providing benefits based on fan behavior data, the benefits section can improve fan satisfaction. By providing benefits based on fan behavior data, the benefits section can improve fan engagement. This allows for improved fan satisfaction through the provision of benefits. Some or all of the above-described processes in the benefits section may be performed using AI, for example, or without AI. For example, the benefits section can input fan behavior data into a generating AI and have the generating AI execute the provision of benefits.

[0035] The notification unit includes a notification unit for event notifications. The notification unit can provide event notifications by methods such as email notifications, app notifications, and SNS notifications. The notification unit can provide event notifications based on fan behavior data. By providing event notifications based on fan behavior data, the notification unit can improve the fan participation rate in events. By providing event notifications based on fan behavior data, the notification unit can improve fan engagement. This allows the fan participation rate in events to be improved through event notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input fan behavior data into a generating AI and have the generating AI execute event notifications.

[0036] The data collection unit can collect data on events attended by fans and merchandise purchased. For example, the data collection unit can collect data on events attended by fans. The data collection unit can collect data on merchandise purchased by fans. By collecting data on events attended by fans and merchandise purchased by fans, the data collection unit can collect detailed data on fan behavior. This allows for more accurate analysis by collecting detailed data on fan behavior. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on events attended by fans into a generating AI and have the generating AI perform the data collection.

[0037] The analysis unit can analyze the collected data and learn the behavior patterns and preferences of fans. For example, the analysis unit can analyze the collected data and learn the behavior patterns of fans. The analysis unit can analyze the collected data and learn the preferences of fans. By analyzing the collected data and learning the behavior patterns and preferences of fans, the analysis unit can provide more appropriate content. This makes it possible to provide more appropriate content by learning the behavior patterns and preferences of fans. 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 collected data into a generating AI and have the generating AI perform the data analysis.

[0038] The service provider can provide content optimized for each individual fan based on the analysis results. For example, the service provider can provide content optimized for each individual fan based on the analysis results. By providing content optimized for each individual fan based on the analysis results, the service provider can improve engagement. By providing content optimized for each individual fan based on the analysis results, the service provider can provide content that matches the interests and concerns of the fans. This allows for improved engagement by providing content optimized for each individual fan. 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 analysis results into a generating AI and have the generating AI execute the provision of optimized content.

[0039] The proposal department can formulate an optimal marketing strategy based on fan behavior data and idol activity data. For example, the proposal department formulates an optimal marketing strategy based on fan behavior data and idol activity data. By formulating an optimal marketing strategy based on fan behavior data and idol activity data, the proposal department can maximize marketing effectiveness. By formulating an optimal marketing strategy based on fan behavior data and idol activity data, the proposal department can improve event participation rates. This allows for the maximization of marketing effectiveness by formulating an optimal marketing strategy. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input fan behavior data and idol activity data into a generating AI and have the generating AI formulate a marketing strategy.

[0040] The data collection unit can analyze fans' past behavioral data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from events that fans have frequently attended in the past. The data collection unit can collect relevant data based on data of merchandise that fans have purchased in the past. The data collection unit can analyze fans' past behavioral patterns and select the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on past behavioral data. 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 fans' past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the fan's current interests and activities during data collection. For example, the data collection unit can prioritize collecting data related to idols that the fan is currently interested in. The data collection unit can collect data related to events that the fan is currently participating in. The data collection unit can filter and collect highly relevant data based on the fan's current activities. This allows for the collection of highly relevant data by filtering data based on current interests and activities. 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 fan's current interests and activities into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of fans during data collection. For example, the data collection unit can prioritize the collection of data related to events in the area where the fan is currently located. The data collection unit can collect highly relevant data based on the geographical location information of fans. If the fan is on the move, the data collection unit can collect the most relevant data based on their current location. This allows for the efficient collection of highly relevant data by considering geographical location information during data collection. 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 geographical location information of fans into a generating AI and have the generating AI perform data collection.

[0043] The data collection unit can analyze fans' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by fans on social media. The data collection unit can analyze fans' social media activity and collect data related to content they are interested in. The data collection unit can collect data related to idols that fans follow on social media. This makes it possible to collect data based on fans' interests and preferences by analyzing social media activity and collecting data. 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 fans' social media activity into a generating AI and have the generating AI perform data collection.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of fan behavior data. For example, the analysis unit can perform a detailed analysis of data on events that fans frequently attend. The analysis unit can perform a detailed analysis of relevant data based on data on merchandise purchased by fans. The analysis unit can adjust the level of detail of its analysis based on the importance of fan behavior data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of behavior data. 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 fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the fan's behavior patterns during analysis. For example, the analysis unit can apply a specific algorithm to analyze data on events that fans frequently attend. The analysis unit can apply a different algorithm to analyze data on merchandise purchased by fans. The analysis unit can apply the optimal analysis algorithm according to the fan's behavior patterns. This enables highly accurate data analysis by applying different analysis algorithms according to behavior patterns. 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 fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0046] The analysis unit can prioritize analysis based on when fan behavior data was submitted. For example, the analysis unit can prioritize analyzing data from events that fans have recently attended. The analysis unit can prioritize analyzing data from merchandise that fans have recently purchased. The analysis unit can prioritize analysis based on when fan behavior data was submitted. This enables efficient data analysis by prioritizing analysis based on when behavior data was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of fan behavior data into a generating AI and have the generating AI determine the analysis priorities.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on fans during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on idols that fans are interested in. The analysis unit can improve the accuracy of its analysis by referring to literature on events that fans have attended. The analysis unit can improve the accuracy of its analysis by referring to literature on merchandise that fans have purchased. By improving the accuracy of the analysis by referring to relevant literature, more accurate data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature on fans into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0048] The content provider can adjust the level of detail of the content provided based on the fan's interests and preferences. For example, the provider can provide detailed content about an idol that the fan is interested in. The provider can also provide detailed content about an event that the fan attended. The provider can adjust the level of detail of the content provided based on the fan's interests and preferences. This allows for the provision of more appropriate content by adjusting the level of detail based on the fan's interests and preferences. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the fan's interests and preferences into a generating AI and have the generating AI perform the adjustment of the level of detail of the content.

[0049] The content delivery unit can apply different delivery algorithms depending on the fan's behavior patterns when providing content. For example, the delivery unit can apply a specific algorithm to provide content related to events that fans frequently attend. The delivery unit can apply a different algorithm to provide content related to merchandise that fans have purchased. The delivery unit can apply the optimal delivery algorithm depending on the fan's behavior patterns. This makes it possible to provide more appropriate content by applying different delivery algorithms depending on the behavior patterns. 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 fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal delivery algorithm.

[0050] The content delivery unit can prioritize providing highly relevant content by considering the fan's geographical location when delivering content. For example, the delivery unit can prioritize providing content related to events in the region where the fan is currently located. The delivery unit can provide highly relevant content based on the fan's geographical location. If the fan is on the move, the delivery unit can provide the most suitable content based on their current location. This makes it possible to provide highly relevant content by considering geographical location. 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 fan's geographical location information into a generating AI and have the generating AI deliver the content.

[0051] The content provider can analyze fans' social media activity and provide relevant content when providing content. For example, the provider can provide relevant content based on content shared by fans on social media. The provider can analyze fans' social media activity and provide content related to content they are interested in. The provider can provide content related to idols that fans follow on social media. This makes it possible to provide content based on fans' interests and preferences by analyzing social media activity and providing content accordingly. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input fans' social media activity into a generating AI and have the generating AI perform the content provision.

[0052] The proposal department can adjust the level of detail of its proposals based on the importance of fan behavior data. For example, the proposal department can propose a detailed marketing strategy based on data of events that fans frequently attend. The proposal department can also propose relevant marketing strategies based on data of merchandise that fans have purchased. The proposal department can adjust the level of detail of its proposals based on the importance of fan behavior data. This allows for the proposal of efficient marketing strategies by adjusting the level of detail of proposals based on the importance of behavior data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0053] The proposal unit can apply different proposal algorithms depending on the fan's behavior patterns when making a proposal. For example, the proposal unit can propose a marketing strategy by applying a specific algorithm based on data of events that fans frequently attend. The proposal unit can propose a marketing strategy by applying a different algorithm based on data of goods that fans have purchased. The proposal unit can apply the optimal proposal algorithm according to the fan's behavior patterns. This makes it possible to propose more appropriate marketing strategies by applying different proposal algorithms according to behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0054] The proposal department can prioritize highly relevant proposals by considering the fan's geographical location information when making proposals. For example, the proposal department can prioritize proposing marketing strategies for events related to the fan's current location. Based on the fan's geographical location information, the proposal department can propose highly relevant marketing strategies. If the fan is on the move, the proposal department can propose the most suitable marketing strategy based on their current location. This makes it possible to propose highly relevant marketing strategies by considering geographical location information. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the fan's geographical location information into a generating AI and have the generating AI execute the proposals.

[0055] The proposal unit can analyze fans' social media activity and make relevant suggestions when making proposals. For example, the proposal unit can propose relevant marketing strategies based on content shared by fans on social media. The proposal unit can analyze fans' social media activity and propose marketing strategies related to content they are interested in. The proposal unit can propose marketing strategies related to idols that fans follow on social media. This makes it possible to propose marketing strategies based on fans' interests and concerns by analyzing social media activity and making suggestions. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input fans' social media activity into a generating AI and have the generating AI execute the suggestions.

[0056] The rewards department can adjust the level of detail of rewards based on the importance of fan behavior data when providing rewards. For example, the rewards department can provide detailed rewards based on data of events that fans frequently attend. The rewards department can provide relevant rewards based on data of goods that fans have purchased. The rewards department can adjust the level of detail of rewards based on the importance of fan behavior data. This allows for efficient reward provision by adjusting the level of detail of rewards based on the importance of behavior data. Some or all of the above processing in the rewards department may be performed using AI, for example, or without using AI. For example, the rewards department can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the rewards.

[0057] The rewards unit can prioritize providing highly relevant rewards by considering the fan's geographical location information when providing rewards. For example, the rewards unit can prioritize providing rewards for events related to the region where the fan is currently located. The rewards unit can provide highly relevant rewards based on the fan's geographical location information. If the fan is on the move, the rewards unit can provide the most suitable reward based on their current location. This makes it possible to provide highly relevant rewards by considering geographical location information. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without using AI. For example, the rewards unit can input the fan's geographical location information into a generating AI and have the generating AI execute the provision of rewards.

[0058] The notification unit can adjust the level of detail of notifications based on the importance of fan behavior data when sending notifications. For example, the notification unit can provide detailed notifications based on data of events that fans frequently attend. The notification unit can also provide relevant notifications based on data of merchandise purchased by fans. The notification unit can adjust the level of detail of notifications based on the importance of fan behavior data. This allows for efficient notifications by adjusting the level of detail of notifications based on the importance of behavior data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of notifications.

[0059] The notification unit can prioritize highly relevant notifications by considering the fan's geographical location information when sending notifications. For example, the notification unit can prioritize notifications for events related to the fan's current location. The notification unit can send highly relevant notifications based on the fan's geographical location information. If the fan is on the move, the notification unit can send the most appropriate notification based on their current location. This makes it possible to send highly relevant notifications by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the fan's geographical location information into a generation AI and have the generation AI execute the notification.

[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 system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to analyze fans' past behavior data and select the optimal data collection method. For example, it can prioritize the collection of data from events that fans have frequently attended in the past. It can also collect relevant data based on data of merchandise that fans have purchased in the past. Moreover, by analyzing fans' past behavior patterns and selecting the optimal data collection method, efficient data collection becomes possible. This means that by selecting the optimal data collection method based on past behavior data, efficient data collection becomes possible.

[0062] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to filter data based on the fan's current interests and activities during data collection. For example, it can prioritize the collection of data related to idols that the fan is currently interested in. It can also collect data related to events that the fan is currently participating in. In addition, it can filter and collect highly relevant data based on the fan's current activities. This allows for the collection of highly relevant data by filtering data based on current interests and activities.

[0063] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to adjust the level of detail of the analysis based on the importance of the fan behavior data during analysis. For example, it can analyze in detail data on events that fans frequently attend. It can also analyze in detail related data based on data on merchandise purchased by fans. Moreover, by adjusting the level of detail of the analysis based on the importance of the fan behavior data, efficient data analysis becomes possible.

[0064] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to apply different analysis algorithms depending on the fan behavior patterns during analysis. For example, a specific algorithm can be applied to analyze data on events that fans frequently attend. Also, a different algorithm can be applied to analyze data on merchandise purchased by fans. Moreover, by applying the optimal analysis algorithm according to the fan behavior patterns, highly accurate data analysis becomes possible.

[0065] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can have a function to adjust the level of detail of the content provided based on the fan's interests. For example, it can provide detailed content about idols that the fan is interested in. It can also provide detailed content about events that the fan has attended. By adjusting the level of detail of the content provided based on the fan's interests, it becomes possible to provide more appropriate content.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects fan behavior data and idol activity data. For example, it collects data on events fans attended and merchandise they purchased, gathering detailed information such as what actions fans took, which events they attended, and what content they preferred. The data collection unit uses AI to analyze this data and understand the fans' interests and preferences. Step 2: The analysis unit analyzes the data collected by the collection unit. By analyzing the collected data, it learns the behavior patterns and preferences of fans. For example, it can identify fans who are highly interested in a particular idol and provide them with appropriate content. Step 3: The content delivery department provides content based on the analysis results obtained by the analysis department. Based on the analysis results, content optimized for each individual fan is provided. For example, fans who have a high level of interest in a particular idol can be provided with content related to that idol. Step 4: The proposal team proposes a marketing strategy based on the content provided by the supply team. They formulate the optimal marketing strategy based on fan behavior data and idol activity data. For example, by providing promotional information related to a particular event to fans who have a high level of interest in that event, it is possible to increase the event participation rate.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to individually customize the provision of idol information, event notifications, and benefits to enhance fan engagement. This system collects fan behavior data and idol activity data and analyzes this data. Next, based on the analysis results, it provides content optimized for each individual fan. This improves fan engagement and increases event participation rates. Furthermore, the AI ​​proposes effective marketing strategies to maximize marketing effectiveness. First, the system collects fan behavior data and idol activity data. At this time, it collects detailed data such as what actions fans have taken, which events they have participated in, and which content they prefer. For example, by collecting data on events fans have attended and goods they have purchased, and having the AI ​​analyze it, it is possible to understand the interests and preferences of the fans. This makes it possible to provide content optimized for each individual fan. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns the fans' behavior patterns and preferences. For example, it can identify fans who have a high level of interest in a particular idol and provide appropriate content to those fans. This improves fan engagement and increases event participation rates. Furthermore, the AI ​​proposes effective marketing strategies. AI develops optimal marketing strategies based on fan behavior data and idol activity data. For example, by providing promotional information related to a specific event to fans who show a high level of interest in that event, event attendance rates can be increased. This maximizes marketing effectiveness. This mechanism improves fan engagement rates and increases event attendance. Fans feel a deeper engagement when they are provided with content tailored to their preferences and behaviors. In addition, idols and management teams can strengthen their relationships with fans and build a more active and participatory fan community. For example, if a fan attends an event by a particular idol, providing benefits and information related to that event can improve fan satisfaction.Furthermore, providing information related to merchandise purchased by fans can keep them interested. This improves fan engagement and increases event participation rates. In this way, by using AI to individually customize information provision, event notifications, and perks for idols, fan engagement can be enhanced. AI can also propose effective marketing strategies and maximize marketing effectiveness. This strengthens the relationship between idols and fans and builds a more active and participatory fan community. The system collects and analyzes fan behavior data and idol activity data to propose optimal content delivery and marketing strategies.

[0069] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects fan behavior data and idol activity data. The collection unit collects data such as events attended by fans and merchandise purchased by fans. The collection unit can collect detailed data such as what actions fans took, which events they participated in, and which content they preferred. For example, the collection unit can understand fans' interests and preferences by collecting data on events attended by fans and having the AI ​​analyze it. The collection unit can understand fans' interests and preferences by collecting data on merchandise purchased by fans and having the AI ​​analyze it. The collection unit can understand fans' interests and preferences by collecting fan behavior data and having the AI ​​analyze it. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and learns fans' behavior patterns and preferences. For example, the analysis unit can identify fans who have a high level of interest in a particular idol and provide appropriate content to those fans. By learning fans' behavior patterns and preferences, the analysis unit can provide more appropriate content. The analysis department can analyze the collected data and learn fan behavior patterns and preferences. The content delivery department provides content based on the analysis results obtained by the analysis department. The content delivery department provides content optimized for each individual fan based on the analysis results. For example, the content delivery department can provide content related to a particular idol to fans who have a high level of interest in that idol. By providing content optimized for each individual fan based on the analysis results, the content delivery department can improve engagement. The content delivery department can provide content optimized for each individual fan based on the analysis results. The proposal department proposes marketing strategies based on the content provided by the content delivery department. The proposal department formulates optimal marketing strategies based on fan behavior data and idol activity data. For example, the proposal department can improve event participation rates by providing promotional information related to a particular event to fans who have a high level of interest in that event.The proposal department can maximize marketing effectiveness by formulating optimal marketing strategies based on fan behavior data and idol activity data. As a result, the system according to the embodiment can collect and analyze fan behavior data and idol activity data, enabling the provision of optimal content and the proposal of marketing strategies.

[0070] The data collection unit collects fan behavior data and idol activity data. Specifically, it collects data on events fans attend and merchandise they purchase. For example, it can collect data on ticket purchase history for events fans attend, check-in information on the day of the event, and activities during the event (e.g., purchases at specific booths or workshops attended). Data on merchandise purchased by fans includes purchase history from online stores and merchandise booths, the type and quantity of items purchased, and the date and time of purchase. This data is important for understanding fans' interests and preferences. The data collection unit centrally manages this data, and by analyzing it with AI, it is possible to understand fans' behavior patterns and preferences in detail. For example, it is possible to identify fans who frequently purchase merchandise from a particular idol or fans who repeatedly attend specific events. Furthermore, the data collection unit can also collect data from online platforms such as social media and blogs. By collecting comments, reviews, likes, and shares posted by fans and analyzing them with AI, it is possible to understand fans' emotions and opinions. In this way, the data collection unit can comprehensively collect fan behavior data and idol activity data and provide a foundation for detailed analysis.

[0071] The analysis department analyzes the data collected by the data collection department. Specifically, AI analyzes the collected data to learn fan behavior patterns and preferences. For example, it can identify fans with a high level of interest in a particular idol and provide them with appropriate content. The AI ​​uses machine learning algorithms to extract patterns from fan behavior data and predict fan interests and preferences. For example, based on past event attendance history and merchandise purchase history, it can predict what kind of event a fan is likely to attend next or what kind of merchandise they are likely to purchase. In addition, by analyzing data collected from social media and blogs and understanding fans' emotions and opinions, it can provide content that meets fans' needs and expectations. Furthermore, the analysis department can create fan segments based on the collected data. For example, by dividing fans into different segments such as those with a high level of interest in a particular idol, those with a high event attendance rate, and those with a high frequency of merchandise purchases, it is possible to formulate more targeted marketing strategies. In this way, the analysis department can support the provision of more appropriate content and the formulation of marketing strategies by analyzing the collected data in detail and learning fan behavior patterns and preferences.

[0072] The content delivery department provides content based on the analysis results obtained by the analysis department. Specifically, it provides content optimized for each individual fan based on the analysis results. For example, it can provide content related to a particular idol to fans who have a high level of interest in that idol. The content delivery department uses AI to generate personalized content based on fans' interests and preferences. For example, it can provide the latest news and event information, as well as information on the sale of limited edition goods for a particular idol. In addition, the content delivery department can recommend content that fans are likely to be interested in based on their behavioral data. For example, it can notify fans who have previously attended an event of a particular idol about information on the next event, or provide information on new related products to fans who have purchased a particular item, thus providing content that matches the fans' interests and preferences. Furthermore, the content delivery department can collect fan feedback and continuously improve the quality of the content it provides. For example, by collecting fan reactions and evaluations of the content provided and having AI analyze them, it can provide content that better meets the needs of fans. In this way, the content delivery department can provide content optimized for each individual fan and improve engagement.

[0073] The Proposal Department proposes marketing strategies based on the content provided by the Provider Department. Specifically, it formulates optimal marketing strategies based on fan behavior data and idol activity data. For example, by providing promotional information related to a particular event to fans who have a high level of interest in that event, it is possible to increase event participation rates. The Proposal Department uses AI to analyze fan behavior data and idol activity data to formulate optimal marketing strategies. For example, based on past event participation history and merchandise purchase history, it can predict what kind of events and merchandise will be well-received by fans next and propose a promotional strategy based on that. In addition, by analyzing data collected from social media and blogs and understanding fans' emotions and opinions, it is possible to formulate marketing strategies that meet fans' needs and expectations. Furthermore, the Proposal Department can continuously monitor the effectiveness of the marketing strategies and modify them as needed. For example, it can analyze the effectiveness of promotional campaigns and revise the strategy if the effect is low, or further strengthen it if the effect is high, responding flexibly. In this way, the Proposal Department can formulate optimal marketing strategies based on fan behavior data and idol activity data and maximize marketing effectiveness.

[0074] The benefits section includes a section for providing benefits. The benefits section can provide benefits such as limited edition goods, event invitations, and point systems. The benefits section can provide benefits based on fan behavior data. By providing benefits based on fan behavior data, the benefits section can improve fan satisfaction. By providing benefits based on fan behavior data, the benefits section can improve fan engagement. This allows for improved fan satisfaction through the provision of benefits. Some or all of the above-described processes in the benefits section may be performed using AI, for example, or without AI. For example, the benefits section can input fan behavior data into a generating AI and have the generating AI execute the provision of benefits.

[0075] The notification unit includes a notification unit for event notifications. The notification unit can provide event notifications by methods such as email notifications, app notifications, and SNS notifications. The notification unit can provide event notifications based on fan behavior data. By providing event notifications based on fan behavior data, the notification unit can improve the fan participation rate in events. By providing event notifications based on fan behavior data, the notification unit can improve fan engagement. This allows the fan participation rate in events to be improved through event notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input fan behavior data into a generating AI and have the generating AI execute event notifications.

[0076] The data collection unit can collect data on events attended by fans and merchandise purchased. For example, the data collection unit can collect data on events attended by fans. The data collection unit can collect data on merchandise purchased by fans. By collecting data on events attended by fans and merchandise purchased by fans, the data collection unit can collect detailed data on fan behavior. This allows for more accurate analysis by collecting detailed data on fan behavior. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on events attended by fans into a generating AI and have the generating AI perform the data collection.

[0077] The analysis unit can analyze the collected data and learn the behavior patterns and preferences of fans. For example, the analysis unit can analyze the collected data and learn the behavior patterns of fans. The analysis unit can analyze the collected data and learn the preferences of fans. By analyzing the collected data and learning the behavior patterns and preferences of fans, the analysis unit can provide more appropriate content. This makes it possible to provide more appropriate content by learning the behavior patterns and preferences of fans. 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 collected data into a generating AI and have the generating AI perform the data analysis.

[0078] The service provider can provide content optimized for each individual fan based on the analysis results. For example, the service provider can provide content optimized for each individual fan based on the analysis results. By providing content optimized for each individual fan based on the analysis results, the service provider can improve engagement. By providing content optimized for each individual fan based on the analysis results, the service provider can provide content that matches the interests and concerns of the fans. This allows for improved engagement by providing content optimized for each individual fan. 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 analysis results into a generating AI and have the generating AI execute the provision of optimized content.

[0079] The proposal department can formulate an optimal marketing strategy based on fan behavior data and idol activity data. For example, the proposal department formulates an optimal marketing strategy based on fan behavior data and idol activity data. By formulating an optimal marketing strategy based on fan behavior data and idol activity data, the proposal department can maximize marketing effectiveness. By formulating an optimal marketing strategy based on fan behavior data and idol activity data, the proposal department can improve event participation rates. This allows for the maximization of marketing effectiveness by formulating an optimal marketing strategy. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input fan behavior data and idol activity data into a generating AI and have the generating AI formulate a marketing strategy.

[0080] The data collection unit can estimate the emotions of fans and adjust the timing of data collection based on the estimated emotions. For example, if a fan is excited, the data collection unit can collect data in real time and reflect it immediately. If a fan is relaxed, the data collection unit can collect data at regular time intervals to reduce the burden on the fan. If a fan is stressed, the data collection unit can reduce the frequency of data collection to minimize the burden on the fan. In this way, the burden on fans can be reduced by adjusting the timing of data collection according to their 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 fan emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0081] The data collection unit can analyze fans' past behavioral data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from events that fans have frequently attended in the past. The data collection unit can collect relevant data based on data of merchandise that fans have purchased in the past. The data collection unit can analyze fans' past behavioral patterns and select the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on past behavioral data. 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 fans' past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter data based on the fan's current interests and activities during data collection. For example, the data collection unit can prioritize collecting data related to idols that the fan is currently interested in. The data collection unit can collect data related to events that the fan is currently participating in. The data collection unit can filter and collect highly relevant data based on the fan's current activities. This allows for the collection of highly relevant data by filtering data based on current interests and activities. 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 fan's current interests and activities into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the emotions of fans and determine the priority of data to collect based on the estimated emotions. For example, if a fan is excited, the data collection unit can prioritize the data to be collected in real time. If a fan is relaxed, the data collection unit can adjust the priority of data to be collected at regular time intervals. If a fan is stressed, the data collection unit can lower the priority of data to be collected, reducing the burden on the fan. This enables efficient data collection by determining the priority of data to be collected according to the emotions of fans. 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 fan emotion data into a generative AI and have the generative AI determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of fans during data collection. For example, the data collection unit can prioritize the collection of data related to events in the area where the fan is currently located. The data collection unit can collect highly relevant data based on the geographical location information of fans. If the fan is on the move, the data collection unit can collect the most relevant data based on their current location. This allows for the efficient collection of highly relevant data by considering geographical location information during data collection. 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 geographical location information of fans into a generating AI and have the generating AI perform data collection.

[0085] The data collection unit can analyze fans' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by fans on social media. The data collection unit can analyze fans' social media activity and collect data related to content they are interested in. The data collection unit can collect data related to idols that fans follow on social media. This makes it possible to collect data based on fans' interests and preferences by analyzing social media activity and collecting data. 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 fans' social media activity into a generating AI and have the generating AI perform data collection.

[0086] The analysis unit can estimate the emotions of fans and adjust the data analysis method based on the estimated emotions. For example, if fans are excited, the analysis unit can analyze the data in real time and reflect the changes immediately. If fans are relaxed, the analysis unit can analyze the data at regular intervals to reduce the burden on fans. If fans are stressed, the analysis unit can reduce the frequency of data analysis to minimize the burden on fans. In this way, the burden on fans can be reduced by adjusting the data analysis method according to their 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 fan emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0087] The analysis unit can adjust the level of detail of its analysis based on the importance of fan behavior data. For example, the analysis unit can perform a detailed analysis of data on events that fans frequently attend. The analysis unit can perform a detailed analysis of relevant data based on data on merchandise purchased by fans. The analysis unit can adjust the level of detail of its analysis based on the importance of fan behavior data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of behavior data. 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 fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the fan's behavior patterns during analysis. For example, the analysis unit can apply a specific algorithm to analyze data on events that fans frequently attend. The analysis unit can apply a different algorithm to analyze data on merchandise purchased by fans. The analysis unit can apply the optimal analysis algorithm according to the fan's behavior patterns. This enables highly accurate data analysis by applying different analysis algorithms according to behavior patterns. 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 fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0089] The analysis unit can estimate the emotions of fans and adjust how the analysis results are displayed based on the estimated emotions. For example, if a fan is excited, the analysis unit can display the analysis results in real time. If a fan is relaxed, the analysis unit can display the analysis results at regular intervals. If a fan is stressed, the analysis unit can reduce the frequency of displaying the analysis results to alleviate the burden on the fan. In this way, the burden on fans can be reduced by adjusting how the analysis results are displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fan emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0090] The analysis unit can prioritize analysis based on when fan behavior data was submitted. For example, the analysis unit can prioritize analyzing data from events that fans have recently attended. The analysis unit can prioritize analyzing data from merchandise that fans have recently purchased. The analysis unit can prioritize analysis based on when fan behavior data was submitted. This enables efficient data analysis by prioritizing analysis based on when behavior data was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of fan behavior data into a generating AI and have the generating AI determine the analysis priorities.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on fans during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on idols that fans are interested in. The analysis unit can improve the accuracy of its analysis by referring to literature on events that fans have attended. The analysis unit can improve the accuracy of its analysis by referring to literature on merchandise that fans have purchased. By improving the accuracy of the analysis by referring to relevant literature, more accurate data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature on fans into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0092] The content delivery unit can estimate the emotions of fans and adjust the content delivery method based on the estimated emotions. For example, if a fan is excited, the content delivery unit can deliver content in real time. If a fan is relaxed, the content delivery unit can deliver content at regular intervals. If a fan is stressed, the content delivery unit can reduce the frequency of content delivery to alleviate the burden on the fan. In this way, the burden on fans can be reduced by adjusting the content delivery method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or not using AI. For example, the content delivery unit can input fan emotion data into a generative AI and have the generative AI adjust the content delivery method.

[0093] The content provider can adjust the level of detail of the content provided based on the fan's interests and preferences. For example, the provider can provide detailed content about an idol that the fan is interested in. The provider can also provide detailed content about an event that the fan attended. The provider can adjust the level of detail of the content provided based on the fan's interests and preferences. This allows for the provision of more appropriate content by adjusting the level of detail based on the fan's interests and preferences. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the fan's interests and preferences into a generating AI and have the generating AI perform the adjustment of the level of detail of the content.

[0094] The content delivery unit can apply different delivery algorithms depending on the fan's behavior patterns when providing content. For example, the delivery unit can apply a specific algorithm to provide content related to events that fans frequently attend. The delivery unit can apply a different algorithm to provide content related to merchandise that fans have purchased. The delivery unit can apply the optimal delivery algorithm depending on the fan's behavior patterns. This makes it possible to provide more appropriate content by applying different delivery algorithms depending on the behavior patterns. 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 fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal delivery algorithm.

[0095] The content delivery unit can estimate the emotions of fans and determine the priority of the content to be delivered based on the estimated emotions. For example, if a fan is excited, the content delivery unit can prioritize the content delivered in real time. If a fan is relaxed, the content delivery unit can adjust the priority of the content delivered at regular time intervals. If a fan is stressed, the content delivery unit can lower the priority of the content delivered to reduce the burden on the fan. This enables efficient content delivery by determining the priority of the content delivered according to the emotions of fans. 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 content delivery unit may be performed using AI, for example, or not using AI. For example, the content delivery unit can input fan emotion data into a generative AI and have the generative AI perform the determination of content priority.

[0096] The content delivery unit can prioritize providing highly relevant content by considering the fan's geographical location when delivering content. For example, the delivery unit can prioritize providing content related to events in the region where the fan is currently located. The delivery unit can provide highly relevant content based on the fan's geographical location. If the fan is on the move, the delivery unit can provide the most suitable content based on their current location. This makes it possible to provide highly relevant content by considering geographical location. 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 fan's geographical location information into a generating AI and have the generating AI deliver the content.

[0097] The content provider can analyze fans' social media activity and provide relevant content when providing content. For example, the provider can provide relevant content based on content shared by fans on social media. The provider can analyze fans' social media activity and provide content related to content they are interested in. The provider can provide content related to idols that fans follow on social media. This makes it possible to provide content based on fans' interests and preferences by analyzing social media activity and providing content accordingly. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input fans' social media activity into a generating AI and have the generating AI perform the content provision.

[0098] The suggestion unit can estimate the emotions of fans and adjust the method of suggesting marketing strategies based on the estimated emotions. For example, if a fan is excited, the suggestion unit can suggest marketing strategies in real time. If a fan is relaxed, the suggestion unit can suggest marketing strategies at regular intervals. If a fan is stressed, the suggestion unit can reduce the frequency of suggesting marketing strategies to alleviate the burden on the fan. In this way, the burden on fans can be reduced by adjusting the method of suggesting marketing strategies according to their 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input fan emotion data into a generative AI and have the generative AI adjust the method of suggesting marketing strategies.

[0099] The proposal department can adjust the level of detail of its proposals based on the importance of fan behavior data. For example, the proposal department can propose a detailed marketing strategy based on data of events that fans frequently attend. The proposal department can also propose relevant marketing strategies based on data of merchandise that fans have purchased. The proposal department can adjust the level of detail of its proposals based on the importance of fan behavior data. This allows for the proposal of efficient marketing strategies by adjusting the level of detail of proposals based on the importance of behavior data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0100] The proposal unit can apply different proposal algorithms depending on the fan's behavior patterns when making a proposal. For example, the proposal unit can propose a marketing strategy by applying a specific algorithm based on data of events that fans frequently attend. The proposal unit can propose a marketing strategy by applying a different algorithm based on data of goods that fans have purchased. The proposal unit can apply the optimal proposal algorithm according to the fan's behavior patterns. This makes it possible to propose more appropriate marketing strategies by applying different proposal algorithms according to behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the fan's behavior patterns into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0101] The proposal unit can estimate the emotions of fans and determine the priority of proposals based on those estimated emotions. For example, if a fan is excited, the proposal unit will prioritize the suggested marketing strategies in real time. If a fan is relaxed, the proposal unit can adjust the priority of the suggested marketing strategies at regular intervals. If a fan is stressed, the proposal unit can lower the priority of the suggested marketing strategies to reduce the burden on the fan. This allows for the proposal of efficient marketing strategies by determining the priority of proposals according to the emotions of fans. 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 proposal unit may be performed using AI or not using AI. For example, the proposal unit can input fan emotion data into a generative AI and have the generative AI determine the priority of proposals.

[0102] The proposal department can prioritize highly relevant proposals by considering the fan's geographical location information when making proposals. For example, the proposal department can prioritize proposing marketing strategies for events related to the fan's current location. Based on the fan's geographical location information, the proposal department can propose highly relevant marketing strategies. If the fan is on the move, the proposal department can propose the most suitable marketing strategy based on their current location. This makes it possible to propose highly relevant marketing strategies by considering geographical location information. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the fan's geographical location information into a generating AI and have the generating AI execute the proposals.

[0103] The proposal unit can analyze fans' social media activity and make relevant suggestions when making proposals. For example, the proposal unit can propose relevant marketing strategies based on content shared by fans on social media. The proposal unit can analyze fans' social media activity and propose marketing strategies related to content they are interested in. The proposal unit can propose marketing strategies related to idols that fans follow on social media. This makes it possible to propose marketing strategies based on fans' interests and concerns by analyzing social media activity and making suggestions. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input fans' social media activity into a generating AI and have the generating AI execute the suggestions.

[0104] The rewards unit can estimate the emotions of fans and adjust the method of providing rewards based on the estimated emotions. For example, if a fan is excited, the rewards unit can provide rewards in real time. If a fan is relaxed, the rewards unit can provide rewards at regular intervals. If a fan is stressed, the rewards unit can reduce the frequency of reward provision to alleviate the burden on the fan. In this way, the burden on fans can be reduced by adjusting the method of providing rewards according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without using AI. For example, the rewards unit can input fan emotion data into a generative AI and have the generative AI adjust the method of providing rewards.

[0105] The rewards department can adjust the level of detail of rewards based on the importance of fan behavior data when providing rewards. For example, the rewards department can provide detailed rewards based on data of events that fans frequently attend. The rewards department can provide relevant rewards based on data of goods that fans have purchased. The rewards department can adjust the level of detail of rewards based on the importance of fan behavior data. This allows for efficient reward provision by adjusting the level of detail of rewards based on the importance of behavior data. Some or all of the above processing in the rewards department may be performed using AI, for example, or without using AI. For example, the rewards department can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of the rewards.

[0106] The rewards unit can estimate the emotions of fans and determine the priority of rewards based on those estimated emotions. For example, if a fan is excited, the rewards unit can prioritize rewards offered in real time. If a fan is relaxed, the rewards unit can adjust the priority of rewards offered at regular time intervals. If a fan is stressed, the rewards unit can lower the priority of rewards offered to reduce the burden on the fan. This enables efficient reward provision by determining the priority of rewards according to the emotions of the fans. 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 rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input fan emotion data into a generative AI and have the generative AI determine the priority of rewards.

[0107] The rewards unit can prioritize providing highly relevant rewards by considering the fan's geographical location information when providing rewards. For example, the rewards unit can prioritize providing rewards for events related to the region where the fan is currently located. The rewards unit can provide highly relevant rewards based on the fan's geographical location information. If the fan is on the move, the rewards unit can provide the most suitable reward based on their current location. This makes it possible to provide highly relevant rewards by considering geographical location information. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without using AI. For example, the rewards unit can input the fan's geographical location information into a generating AI and have the generating AI execute the provision of rewards.

[0108] The notification unit can estimate the fan's emotions and adjust the notification method based on the estimated emotions. For example, if the fan is excited, the notification unit can send a notification in real time. If the fan is relaxed, the notification unit can send notifications at regular intervals. If the fan is stressed, the notification unit can reduce the frequency of notifications to alleviate the fan's burden. In this way, the burden on the fan can be reduced by adjusting the notification method according to the fan's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input fan emotion data into a generative AI and have the generative AI adjust the notification method.

[0109] The notification unit can adjust the level of detail of notifications based on the importance of fan behavior data when sending notifications. For example, the notification unit can provide detailed notifications based on data of events that fans frequently attend. The notification unit can also provide relevant notifications based on data of merchandise purchased by fans. The notification unit can adjust the level of detail of notifications based on the importance of fan behavior data. This allows for efficient notifications by adjusting the level of detail of notifications based on the importance of behavior data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input fan behavior data into a generating AI and have the generating AI perform the adjustment of the level of detail of notifications.

[0110] The notification unit can estimate the fan's emotions and determine the priority of notifications based on the estimated emotions. For example, if the fan is excited, the notification unit will prioritize the content of the notification in real time. If the fan is relaxed, the notification unit can adjust the priority of the content of the notification at regular time intervals. If the fan is stressed, the notification unit can lower the priority of the content of the notification to reduce the burden on the fan. This enables efficient notifications by determining the priority of notifications according to the fan's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input fan emotion data into the generative AI and have the generative AI determine the priority of notifications.

[0111] The notification unit can prioritize highly relevant notifications by considering the fan's geographical location information when sending notifications. For example, the notification unit can prioritize notifications for events related to the fan's current location. The notification unit can send highly relevant notifications based on the fan's geographical location information. If the fan is on the move, the notification unit can send the most appropriate notification based on their current location. This makes it possible to send highly relevant notifications by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the fan's geographical location information into a generation AI and have the generation AI execute the notification.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can be equipped with an emotion estimation function that estimates the emotions of fans and adjusts the timing of data collection based on the estimated emotions. For example, if fans are excited, data can be collected in real time and reflected immediately. If fans are relaxed, data can be collected at regular time intervals to reduce the burden on fans. Furthermore, if fans are stressed, the frequency of data collection can be reduced to minimize the burden on fans. In this way, the burden on fans can be reduced by adjusting the timing of data collection according to their emotions.

[0114] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to analyze fans' past behavior data and select the optimal data collection method. For example, it can prioritize the collection of data from events that fans have frequently attended in the past. It can also collect relevant data based on data of merchandise that fans have purchased in the past. Moreover, by analyzing fans' past behavior patterns and selecting the optimal data collection method, efficient data collection becomes possible. This means that by selecting the optimal data collection method based on past behavior data, efficient data collection becomes possible.

[0115] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to estimate the emotions of fans and determine the priority of data to be collected based on the estimated emotions. For example, if fans are excited, the priority of data to be collected in real time can be increased. Also, if fans are relaxed, the priority of data to be collected can be adjusted at regular time intervals. Moreover, if fans are stressed, the priority of data to be collected can be lowered to reduce the burden on fans. In this way, by determining the priority of data to be collected according to the emotions of fans, efficient data collection becomes possible.

[0116] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to filter data based on the fan's current interests and activities during data collection. For example, it can prioritize the collection of data related to idols that the fan is currently interested in. It can also collect data related to events that the fan is currently participating in. In addition, it can filter and collect highly relevant data based on the fan's current activities. This allows for the collection of highly relevant data by filtering data based on current interests and activities.

[0117] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can have a function to estimate the emotions of fans and adjust the data analysis method based on the estimated emotions. For example, if fans are excited, the data can be analyzed in real time and reflected immediately. If fans are relaxed, the data can be analyzed at regular intervals to reduce the burden on fans. Furthermore, if fans are stressed, the frequency of data analysis can be reduced to minimize the burden on fans. In this way, the burden on fans can be reduced by adjusting the data analysis method according to their emotions.

[0118] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to adjust the level of detail of the analysis based on the importance of the fan behavior data during analysis. For example, it can analyze in detail data on events that fans frequently attend. It can also analyze in detail related data based on data on merchandise purchased by fans. Moreover, by adjusting the level of detail of the analysis based on the importance of the fan behavior data, efficient data analysis becomes possible.

[0119] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can have a function to estimate the fans' emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the fans are excited, the analysis results can be displayed in real time. If the fans are relaxed, the analysis results can be displayed at regular intervals. Furthermore, if the fans are stressed, the frequency of displaying the analysis results can be reduced to lessen the burden on the fans. In this way, the burden on fans can be reduced by adjusting how the analysis results are displayed according to their emotions.

[0120] The system comprises a collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that proposes marketing strategies based on the provided content. Furthermore, the system can have a function to apply different analysis algorithms depending on the fan behavior patterns during analysis. For example, a specific algorithm can be applied to analyze data on events that fans frequently attend. Also, a different algorithm can be applied to analyze data on merchandise purchased by fans. Moreover, by applying the optimal analysis algorithm according to the fan behavior patterns, highly accurate data analysis becomes possible.

[0121] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content delivery unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can have a function to estimate the emotions of fans and adjust the content delivery method based on the estimated emotions. For example, if fans are excited, content can be delivered in real time. If fans are relaxed, content can be delivered at regular intervals. Moreover, if fans are stressed, the frequency of content delivery can be reduced to alleviate the burden on fans. In this way, the burden on fans can be reduced by adjusting the content delivery method according to their emotions.

[0122] The system comprises a data collection unit that collects fan behavior data and idol activity data, an analysis unit that analyzes the collected data, a content provision unit that provides content based on the analysis results, and a proposal unit that suggests marketing strategies based on the provided content. Furthermore, the system can have a function to adjust the level of detail of the content provided based on the fan's interests. For example, it can provide detailed content about idols that the fan is interested in. It can also provide detailed content about events that the fan has attended. By adjusting the level of detail of the content provided based on the fan's interests, it becomes possible to provide more appropriate content.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects fan behavior data and idol activity data. For example, it collects data on events fans attended and merchandise they purchased, gathering detailed information such as what actions fans took, which events they attended, and what content they preferred. The data collection unit uses AI to analyze this data and understand the fans' interests and preferences. Step 2: The analysis unit analyzes the data collected by the collection unit. By analyzing the collected data, it learns the behavior patterns and preferences of fans. For example, it can identify fans who are highly interested in a particular idol and provide them with appropriate content. Step 3: The content delivery department provides content based on the analysis results obtained by the analysis department. Based on the analysis results, content optimized for each individual fan is provided. For example, fans who have a high level of interest in a particular idol can be provided with content related to that idol. Step 4: The proposal team proposes a marketing strategy based on the content provided by the supply team. They formulate the optimal marketing strategy based on fan behavior data and idol activity data. For example, by providing promotional information related to a particular event to fans who have a high level of interest in that event, it is possible to increase the event participation rate.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, reward unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects fan behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns fan behavior patterns and preferences by analyzing the collected data. The provision unit is implemented by the control unit 46A of the smart device 14 as a processing unit that provides content optimized for each individual fan based on the analysis results. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and formulates an optimal marketing strategy. The reward unit is implemented by the control unit 46A of the smart device 14 and provides rewards based on fan behavior data. The notification unit is implemented by the control unit 46A of the smart device 14 and provides event notifications. 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, reward unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects fan behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the fan's behavior patterns and preferences by analyzing the collected data. The provision unit is implemented by the control unit 46A of the smart glasses 214 as a processing unit that provides content optimized for each individual fan based on the analysis results. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and formulates the optimal marketing strategy. The reward unit is implemented by the control unit 46A of the smart glasses 214 and provides rewards based on the fan's behavior data. The notification unit is implemented by the control unit 46A of the smart glasses 214 and provides event notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, reward unit, and notification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects fan behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns fan behavior patterns and preferences by analyzing the collected data. The provision unit is implemented by the control unit 46A of the headset terminal 314 as a processing unit that provides content optimized for each individual fan based on the analysis results. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and formulates an optimal marketing strategy. The reward unit is implemented by the control unit 46A of the headset terminal 314 and provides rewards based on fan behavior data. The notification unit is implemented by the control unit 46A of the headset terminal 314 and provides event notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, reward unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects fan behavior data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the fan behavior patterns and preferences by analyzing the collected data. The provision unit is implemented by the control unit 46A of the robot 414 as a processing unit that provides content optimized for each individual fan based on the analysis results. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and formulates the optimal marketing strategy. The reward unit is implemented by the control unit 46A of the robot 414 and provides rewards based on the fan behavior data. The notification unit is implemented by the control unit 46A of the robot 414 and provides event notifications. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A data collection department that collects fan behavior data and idol activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, A content provision unit provides content based on the analysis results obtained by the aforementioned analysis unit, A proposal department that proposes a marketing strategy based on the content provided by the aforementioned provision department, Equipped with A system characterized by the following features. (Note 2) It includes a special benefits section for providing special benefits. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a notification unit for event notifications. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect data on events that fans attend and merchandise they purchase. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is The collected data is analyzed to learn the behavior patterns and preferences of fans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on the analysis results, we provide content optimized for each individual fan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Based on fan behavior data and idol activity data, we formulate the optimal marketing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate fan sentiment and adjust the timing of data collection based on the estimated fan sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We analyze past fan behavior data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the fans' current interests and activities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate fan sentiment and prioritize the data to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the geographical location information of fans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, we analyze fans' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate fan sentiment and adjust the data analysis method based on the estimated fan sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of fan behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the fan's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the fans' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we prioritize the analysis based on when the fan behavior data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, we refer to relevant literature related to fans to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate fan sentiment and adjust how we deliver content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, adjust the level of detail provided based on the interests and concerns of the fans. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing content, different distribution algorithms are applied depending on the fan's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate fan sentiment and prioritize the content we provide based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing content, we prioritize delivering highly relevant content by taking into account the geographical location of our fans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, we analyze fans' social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We estimate fan sentiment and adjust our marketing strategy proposals based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of fan behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the fan's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We estimate the fans' emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, we prioritize those that are highly relevant, taking into account the geographical location of the fans. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we analyze the fans' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned special features section is, We estimate the feelings of the fans and adjust the way we provide rewards based on those estimated feelings. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned special features section is, When providing rewards, we adjust the level of detail in the rewards based on the importance of fan behavior data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned special features section is, We estimate the fans' emotions and determine the priority of rewards based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned special features section is, When providing rewards, we will prioritize providing rewards that are highly relevant to the fan's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned notification unit, We estimate the fans' emotions and adjust the notification method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of fan behavior data. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned notification unit, It estimates fan sentiment and prioritizes notifications based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned notification unit, When sending notifications, we prioritize relevant notifications by considering the fan's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0197] 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 data collection department that collects fan behavior data and idol activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, A content provision unit provides content based on the analysis results obtained by the aforementioned analysis unit, A proposal department that proposes a marketing strategy based on the content provided by the aforementioned provision department, Equipped with A system characterized by the following features.

2. It includes a special benefits section for providing special benefits. The system according to feature 1.

3. Equipped with a notification unit for event notifications. The system according to feature 1.

4. The aforementioned collection unit is We collect data on events that fans attend and merchandise they purchase. The system according to feature 1.

5. The aforementioned analysis unit is The collected data is analyzed to learn the behavior patterns and preferences of fans. The system according to feature 1.

6. The aforementioned supply unit is, Based on the analysis results, we provide content optimized for each individual fan. The system according to feature 1.

7. The aforementioned proposal section is, Based on fan behavior data and idol activity data, we formulate the optimal marketing strategy. The system according to feature 1.

8. The aforementioned collection unit is We estimate fan sentiment and adjust the timing of data collection based on the estimated fan sentiment. The system according to feature 1.

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