system

Abstract

The system according to this embodiment aims to optimize audience prediction, ticket pricing, and promotional strategies in the planning and operation of music events. [Solution] The system according to the embodiment comprises a data collection unit, a prediction unit, an optimization unit, a proposal unit, a risk management unit, and a recommendation unit. The data collection unit collects historical data and market trends. The prediction unit analyzes the data collected by the data collection unit and predicts the number of attendees. The optimization unit optimizes ticket prices based on the number of attendees predicted by the prediction unit. The proposal unit proposes effective advertising media and messaging based on the prices optimized by the optimization unit. The risk management unit analyzes risk factors based on the advertising media and messaging proposed by the proposal unit and proposes countermeasures. The recommendation unit recommends artists that match the event's theme and target audience based on the countermeasures proposed by the risk management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, in the planning and operation of music events, customer acquisition prediction, ticket price setting, and optimization of promotion strategies have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform customer acquisition prediction, ticket price setting, and optimization of promotion strategies in the planning and operation of music events.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a forecasting unit, an optimization unit, a proposal unit, a risk management unit, and a recommendation unit. The data collection unit collects historical data and market trends. The forecasting unit analyzes the data collected by the data collection unit and forecasts the number of attendees. The optimization unit optimizes ticket prices based on the number of attendees forecasted by the forecasting unit. The proposal unit proposes effective advertising media and messaging based on the prices optimized by the optimization unit. The risk management unit analyzes risk factors based on the advertising media and messaging proposed by the proposal unit and proposes countermeasures. The recommendation unit recommends artists that are suitable for the event's theme and target audience based on the countermeasures proposed by the risk management unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform tasks such as predicting attendance, setting ticket prices, and optimizing promotional strategies in the planning and operation of music events. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor and 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 music event planning and management support system according to an embodiment of the present invention is an AI agent that supports the planning and management of music events. This system performs audience prediction, ticket pricing, and promotional strategy optimization. The music event planning and management support system reduces the risks and uncertainties faced by event organizers and supports the holding of events with a high success rate. For example, the music event planning and management support system uses an audience prediction model to analyze past data and market trends to predict the number of attendees with high accuracy. Next, the music event planning and management support system optimizes ticket prices, maximizing revenue through demand-based pricing. Furthermore, the music event planning and management support system proposes targeted advertising, suggesting effective advertising media and messaging to improve promotional effectiveness. In addition, the music event planning and management support system performs risk management, analyzing risk factors such as weather and competing events and proposing countermeasures. Finally, the music event planning and management support system performs artist matching, recommending artists that match the event's theme and target audience. In this way, the music event planning and management support system reduces the risks and uncertainties faced by event organizers and supports the holding of events with a high success rate. This allows the music event planning and management support system to optimize the planning and operation of music events, reduce the risks and uncertainties faced by event organizers, and support the hosting of events with a high success rate.

[0029] The music event planning and management support system according to this embodiment comprises a data collection unit, a forecasting unit, an optimization unit, a proposal unit, a risk management unit, and a recommendation unit. The data collection unit collects historical data and market trends. For example, the data collection unit can collect historical event data and market trends. As historical event data, the data collection unit can collect the number of event participants, sales data, feedback, etc. As market trends, the data collection unit can collect industry reports and consumer trends. The forecasting unit analyzes the data collected by the data collection unit and predicts the number of attendees. For example, the forecasting unit can calculate the expected number of attendees for the next event based on the collected data. As data analysis, the forecasting unit can use statistical analysis and machine learning algorithms. As a method for calculating the number of attendees, the forecasting unit can use a prediction model and comparison with historical data. The optimization unit optimizes ticket prices based on the number of attendees predicted by the forecasting unit. For example, the optimization unit can set the optimal ticket price based on the popularity of the event and past ticket sales data. The Optimization Department can evaluate the popularity of an event by considering factors such as past attendance numbers and social media reactions. The Optimization Department can collect and analyze ticket sales data such as sales figures, sales period, and price range. The Proposal Department proposes effective advertising media and messaging based on the pricing optimized by the Optimization Department. For example, the Proposal Department can select the most suitable advertising media and propose effective messaging for a specific target audience. The Proposal Department can consider factors such as age group, interests, and location when determining the target audience. The Proposal Department can select online advertising, television advertising, radio advertising, etc., as advertising media. The Proposal Department can create advertising copy and promotional messages as messaging. The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. For example, the Risk Management Department can propose measures to minimize risk based on weather data and information on competing events. The Risk Management Department can collect and analyze weather data such as weather forecasts and historical weather data.The Risk Management Department can collect and analyze information on competing events, such as the date, time, location, and number of participants. The Recommendation Department recommends artists that are suitable for the event's theme and target audience based on the countermeasures proposed by the Risk Management Department. For example, the Recommendation Department can select and recommend artists that are best suited to the event's concept and target audience. The Recommendation Department can consider music genres and the event's theme as part of the event's concept. The Recommendation Department can consider age groups, interests, and geographical location as part of the target audience. As a result, the music event planning and management support system according to this embodiment can support the planning and management of music events, reduce the risks and uncertainties faced by event organizers, and support the holding of events with a high success rate by forecasting attendance, setting ticket prices, and optimizing promotion strategies.

[0030] The data collection department collects historical data and market trends. Specifically, it can collect historical event data such as the number of event attendees, sales data, and feedback. This data is an important source of information for analyzing the success and failure factors of an event. For example, the number of attendees is used to evaluate the scale and popularity of an event, and sales data is used to determine profitability and the appropriateness of ticket prices. Feedback data is important for understanding participant satisfaction and areas for improvement, and can be reflected in planning future events. The data collection department can also collect market trends such as industry reports and consumer trends. Industry reports are useful for understanding trends and new developments across the music industry, and consumer trends are important for understanding the preferences and behavioral patterns of the target audience. This allows the data collection department to comprehensively analyze historical data and market trends and provide foundational information for event planning. Furthermore, the data collection department can also collect data from social media and online platforms. For example, it can collect word-of-mouth, ratings, and participant posts about events to evaluate the reputation and impact of an event. This allows the data collection department to collect data from multiple perspectives and build an information foundation for successful event planning.

[0031] The prediction unit analyzes the data collected by the collection unit to predict the number of attendees. Specifically, it can calculate the expected number of attendees for the next event based on the collected data. The prediction unit can use statistical analysis and machine learning algorithms for data analysis. In statistical analysis, regression analysis and time series analysis are performed based on past data to understand trends and patterns in the number of attendees. In machine learning algorithms, a model is built to predict the number of attendees for the next event by learning from past event data and market trends. For example, by using event participant numbers, sales data, and feedback data as input data and training the prediction model, the expected number of attendees for the next event can be calculated with high accuracy. The prediction unit can use prediction models and comparisons with past data as methods for calculating the number of attendees. Prediction models can be built to make more accurate predictions by constructing complex models that take multiple variables into consideration. In comparisons with past data, the number of attendees is predicted based on data from similar events. In this way, the prediction unit can effectively utilize the collected data and predict the number of attendees for the next event with high accuracy. Furthermore, the prediction unit can update prediction results in real time and make predictions based on the latest data. This allows the prediction unit to support important decision-making in event planning and help ensure the successful hosting of events.

[0032] The optimization unit optimizes ticket prices based on the predicted attendance figures from the forecasting unit. Specifically, it can set optimal ticket prices based on the event's popularity and past ticket sales data. The optimization unit can evaluate past attendance figures and social media reactions as indicators of event popularity. For example, if past events have had a high attendance rate or positive social media reactions, it can determine that the event is highly popular and set appropriate ticket prices. The optimization unit can collect and analyze ticket sales data such as sales volume, sales period, and price range. This allows it to formulate optimal sales strategies based on past sales data. For example, by analyzing sales volume and sales period, it can set optimal start and duration for sales, maximizing ticket sales. By analyzing price ranges, it can set prices appropriate for the target audience, maximizing revenue. Furthermore, the optimization unit can implement dynamic pricing. Dynamic pricing allows for real-time adjustment of ticket prices in response to fluctuations in supply and demand. This maximizes revenue by raising prices when demand is high and lowering prices when demand is low. The optimization unit can use this data and algorithms to set optimal ticket prices and improve the profitability of the event.

[0033] The Proposal Department proposes effective advertising media and messaging based on pricing optimized by the Optimization Department. Specifically, it can select the most suitable advertising media and propose effective messages for a particular target audience. The Proposal Department can consider age group, interests, and location when determining the target audience. For example, social media and online advertising are effective when targeting younger generations, while television and radio advertising are suitable when targeting middle-aged and older generations. The Proposal Department can select online advertising, television advertising, and radio advertising as advertising media. Online advertising can reach the target audience using social media, search engine advertising, and display advertising. Television and radio advertising can effectively convey messages to a broad audience. The Proposal Department can create advertising copy and promotional messages as messaging. Advertising copy is a crucial element for attracting the target audience's interest and encouraging action, while promotional messages can increase participation by highlighting the event's appeal and benefits. This allows the Proposal Department to develop the optimal advertising strategy and maximize event attendance. Furthermore, the Proposal Department can monitor the effectiveness of the advertising campaign and modify the strategy as needed. This allows the proposal department to consistently provide the optimal advertising strategy and support the success of the event.

[0034] The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. Specifically, it can propose measures to minimize risk based on weather data and information on competing events. The Risk Management Department can collect and analyze weather forecasts and historical weather data as weather data. For example, if bad weather is expected on the day of the event, it can consider securing an indoor venue or postponing the event. By analyzing historical weather data, it can evaluate weather risks in specific seasons and regions and take appropriate measures. The Risk Management Department can collect and analyze information on competing events, such as the date, time, location, and number of participants. If competing events are held at the same time, it may affect attendance, so it is important to evaluate the scale and popularity of competing events and take appropriate measures. For example, to differentiate from competing events, promotions emphasizing unique attractions and benefits can be developed. In this way, the Risk Management Department can comprehensively analyze the risk factors for the success of the event and propose appropriate countermeasures. Furthermore, the Risk Management Department can monitor the implementation status of risk countermeasures and modify them as needed. In this way, the Risk Management Department can minimize the risks of the event and increase the success rate.

[0035] The recommendation department recommends artists that are suitable for the event's theme and target audience, based on the measures proposed by the risk management department. Specifically, it can select and recommend artists that are best suited to the event's concept and target audience. The recommendation department can consider music genres and the event's theme as part of the event's concept. For example, for a rock festival, it can recommend popular rock bands or up-and-coming artists. For a jazz festival, it can recommend renowned jazz musicians or local jazz bands. The recommendation department can consider age groups, interests, and regions as part of the target audience. For example, if targeting a young audience, it can recommend artists popular on social media or artists who are sensitive to trends. If targeting a middle-aged or older audience, it can recommend artists with nostalgic hit songs or artists with a calm atmosphere. In this way, the recommendation department can select artists that are best suited to the event's theme and target audience, maximizing the event's appeal. Furthermore, the recommendation department can check artists' schedules and contract terms to make feasible recommendations. In this way, the recommendation department can effectively support artist selection, a crucial element for the event's success, and enhance the event's appeal.

[0036] The data collection unit can collect historical event data and market trends. For example, as historical event data, the data collection unit can collect event attendance figures, sales data, and feedback. Based on the number of event attendees, the data collection unit can evaluate the scale and popularity of events. Based on sales data, the data collection unit can evaluate the profitability of events. Based on feedback, the data collection unit can identify success factors and areas for improvement for events. As market trends, the data collection unit can collect industry reports and consumer trends. Based on industry reports, the data collection unit can understand overall industry trends and the activities of competitors. Based on consumer trends, the data collection unit can understand consumer preferences and behavioral patterns. As a result, by collecting historical event data and market trends, the data collection unit can improve the accuracy of attendance predictions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use generative AI to collect and analyze data in order to collect historical event data and market trends.

[0037] The prediction unit can analyze the data collected by the collection unit and calculate the expected attendance for the next event. For example, the prediction unit can calculate the expected attendance for the next event based on the collected data. The prediction unit can use statistical analysis and machine learning algorithms for data analysis. For statistical analysis, the prediction unit can use regression analysis and time series analysis. For machine learning algorithms, the prediction unit can use random forests and neural networks. As a method for calculating attendance, the prediction unit can use prediction models and comparisons with past data. For prediction models, the prediction unit can use linear regression models and nonlinear regression models. For comparisons with past data, the prediction unit can use data from similar events. In this way, the prediction unit can improve the success rate of events by analyzing the collected data and calculating the expected attendance for the next event. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the collected data into a generating AI, and the generating AI can predict the attendance.

[0038] The optimization unit can set the optimal ticket price based on the event's popularity and past ticket sales data. For example, the optimization unit can evaluate the event's popularity by assessing past attendance figures and social media reactions. The optimization unit can evaluate the event's popularity based on past attendance figures. The optimization unit can evaluate the event's popularity based on social media reactions. The optimization unit can collect and analyze ticket sales data such as the number of tickets sold, sales period, and price range. The optimization unit can evaluate ticket demand based on the number of tickets sold. The optimization unit can evaluate ticket sales strategies based on the sales period. The optimization unit can evaluate ticket pricing based on the price range. As a result, the optimization unit can maximize revenue by setting the optimal ticket price based on the event's popularity and past ticket sales data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not. For example, the optimization unit can input the event's popularity and past ticket sales data into a generating AI, which can then set the optimal ticket price.

[0039] The proposal department can select the most suitable advertising media for a specific target audience and propose effective messages. For example, the proposal department can consider age groups, interests, and geographical location as part of the target audience. The proposal department can identify target audiences based on age groups, interests, and geographical location. The proposal department can select online advertising, television advertising, and radio advertising as advertising media. Based on online advertising, the proposal department can select the most suitable advertising media for the target audience. Based on television advertising, the proposal department can select the most suitable advertising media for the target audience. Based on radio advertising, the proposal department can create advertising copy and promotional messages. Based on advertising copy, the proposal department can create messages optimized for the target audience. Based on promotional messages, the proposal department can create messages optimized for the target audience. This allows the proposal department to select the most suitable advertising media for a specific target audience and propose effective messages, thereby improving the effectiveness of promotions. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the most suitable advertising media and messages for the target audience into a generation AI, and the generation AI can then propose the most suitable advertising media and messages.

[0040] The Risk Management Department can propose measures to minimize risk based on weather data and information on competing events. For example, the Risk Management Department can collect and analyze weather forecasts and historical weather data as weather data. Based on weather forecasts, the Risk Management Department can predict the weather on the day of the event and propose measures to minimize risk. Based on historical weather data, the Risk Management Department can predict the weather on the day of the event and propose measures to minimize risk. The Risk Management Department can collect and analyze information on competing events, such as the date and time, location, and number of participants. Based on the date and time, the Risk Management Department can propose measures to avoid overlap with competing events. Based on the location, the Risk Management Department can propose measures to avoid overlap with competing events. Based on the number of participants, the Risk Management Department can propose measures to avoid overlap with competing events. In this way, the Risk Management Department can improve the success rate of the event by proposing measures to minimize risk based on weather data and information on competing events. Some or all of the above processing in the Risk Management Department may be performed using AI, for example, or not using AI. For example, the risk management department can input weather data and competitive event information into a generating AI, which can then propose measures to minimize risk.

[0041] The recommendation department can select and recommend artists that are best suited to the event's concept and target audience. For example, the recommendation department can consider music genres and the event's theme as part of the event's concept. Based on music genres, the recommendation department can select artists that are best suited to the event's concept. Based on the event's theme, the recommendation department can select artists that are best suited to the event's concept. The recommendation department can consider age groups, interests, and regions as part of the target audience. Based on age groups, the recommendation department can select artists that are best suited to the target audience. Based on interests, the recommendation department can select artists that are best suited to the target audience. Based on regions, the recommendation department can select artists that are best suited to the target audience. In this way, the recommendation department can enhance the appeal of the event by selecting and recommending artists that are best suited to the event's concept and target audience. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the event's concept and target audience's best suited artists into a generating AI, and the generating AI can recommend the best artists.

[0042] The data collection unit can evaluate the reliability of past event data and prioritize the collection of reliable data. For example, the data collection unit can verify the source of past event data and prioritize reliable data. The data collection unit can check data consistency and exclude unreliable data. The data collection unit can verify the data update frequency and prioritize the collection of the most recent data. This allows the data collection unit to improve prediction accuracy by evaluating the reliability of past event data and prioritizing the collection of reliable data. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the reliability of past event data into a generating AI, which can then prioritize the collection of reliable data.

[0043] The data collection unit can detect changes in market trends in real time and dynamically change the types of data collected. For example, the data collection unit can detect rapid changes in market trends and immediately change the types of data collected. The data collection unit can analyze real-time market data and dynamically add necessary data. The data collection unit can predict market trends and plan future data collection. As a result, the data collection unit can reflect the latest market information by detecting changes in market trends in real time and dynamically changing the types of data collected. 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 changes in market trends into a generating AI, and the generating AI can dynamically change the types of data collected.

[0044] The data collection unit can collect data from social media and gather real-time reactions to events. For example, the data collection unit can collect social media posts in real time and analyze reactions to events. The data collection unit can use hashtags to collect posts related to specific events. The data collection unit can analyze social media trends and evaluate the popularity of events. In this way, the data collection unit can grasp real-time reactions to events by collecting data from social media. 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 social media post data into a generating AI, and the generating AI can collect real-time reactions to events.

[0045] The data collection unit can prioritize the collection of data from specific regions, taking geographical market trends into consideration. For example, the data collection unit can analyze market trends in a specific region and prioritize the collection of data from that region. The data collection unit can analyze the trends of event participants in each region and determine the priority of data collection. The data collection unit can collect information on competing events in each region and predict the success rate of events. In this way, the data collection unit can reflect regional market trends by prioritizing the collection of data from specific regions, taking geographical market trends into consideration. 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 market trend data from a specific region into a generating AI, and the generating AI can prioritize the collection of data from that specific region.

[0046] The prediction unit can improve prediction accuracy by analyzing the success and failure factors of past events. For example, the prediction unit can identify the success factors of past events and reflect them in the prediction model. The prediction unit can analyze the failure factors of past events and find areas for improvement in the prediction model. The prediction unit can improve prediction accuracy by comparing the success and failure factors. In this way, the prediction unit can improve prediction accuracy by analyzing the success and failure factors of past events. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the success and failure factors of past events into a generating AI, and the generating AI can improve prediction accuracy.

[0047] The prediction unit can make predictions by taking into account fluctuations in visitor numbers according to seasons and specific event periods. For example, the prediction unit can analyze seasonal fluctuations in visitor numbers and reflect them in the prediction model. The prediction unit can consider fluctuations in visitor numbers according to specific event periods (e.g., Christmas, summer holidays). The prediction unit can predict visitor numbers based on past data according to seasons and event periods. In this way, the prediction unit can improve the accuracy of its predictions by taking into account fluctuations in visitor numbers according to seasons and specific event periods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data according to seasons and specific event periods into a generating AI, and the generating AI can make predictions about visitor numbers.

[0048] The prediction unit can predict attendance figures by taking into account the impact of competing events. For example, the prediction unit can predict attendance figures by considering the dates of competing events. The prediction unit can analyze the popularity of competing events and reflect this in the attendance figure prediction. The prediction unit can predict attendance figures by considering the promotional activities of competing events. In this way, the prediction unit can improve the accuracy of its attendance figure predictions by taking into account the impact of competing events. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data on competing events into a generating AI, and the generating AI can perform the attendance figure prediction.

[0049] The prediction unit can apply different prediction algorithms based on the theme and content of the event. For example, the prediction unit can select the optimal prediction algorithm according to the theme of the event. The prediction unit can adjust the prediction algorithm based on the content of the event (e.g., concert, festival). The prediction unit can apply different prediction algorithms according to the scale and target audience of the event. This allows the prediction unit to improve prediction accuracy by applying the optimal prediction algorithm according to the theme and content of the event. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input different prediction algorithms based on the theme and content of the event into a generating AI, and the generating AI can apply the optimal prediction algorithm.

[0050] The optimization unit can analyze ticket sales history and determine the optimal sales timing. For example, the optimization unit can analyze past ticket sales data to identify the optimal sales timing. The optimization unit can predict peak demand periods from ticket sales history. Based on sales history, the optimization unit can optimize the start and end times of sales. In this way, the optimization unit can determine the optimal sales timing by analyzing ticket sales history and maximize revenue. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past ticket sales data into a generating AI, and the generating AI can determine the optimal sales timing.

[0051] The optimization unit can dynamically change prices in real time in response to fluctuations in demand. For example, the optimization unit can detect sudden fluctuations in demand and immediately change prices. The optimization unit can analyze real-time demand data and dynamically adjust prices. The optimization unit can raise prices when demand is at its peak and lower prices when demand is low. In this way, the optimization unit can maximize profits by dynamically changing prices in real time in response to fluctuations in demand. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time demand data into a generating AI, and the generating AI can dynamically change prices.

[0052] The optimization unit can apply different pricing strategies depending on the type of ticket. For example, the optimization unit can set a higher price for VIP tickets and offer more benefits. For general tickets, it can set a mid-range price to appeal to a broad target audience. For student tickets, it can set a lower price to attract students. In this way, the optimization unit can maximize revenue by applying different pricing strategies depending on the type of ticket. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input data corresponding to the type of ticket into a generating AI, and the generating AI can apply different pricing strategies.

[0053] The optimization unit can optimize prices on a regional basis, taking into account regional demand. For example, the optimization unit can analyze regional demand data and optimize prices. The optimization unit can adjust pricing considering regional economic conditions. The optimization unit can optimize prices considering the impact of regional competitive events. In this way, the optimization unit can maximize revenue by optimizing prices on a regional basis, taking into account regional demand. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional demand data into a generating AI, and the generating AI can optimize prices on a regional basis.

[0054] The proposal department can analyze the effectiveness of past advertising campaigns and select the most suitable advertising media. For example, the proposal department can analyze data from past advertising campaigns to identify highly effective advertising media. The proposal department can analyze the success factors of advertising campaigns and select the most suitable advertising media. The proposal department can analyze the failure factors of advertising campaigns and find areas for improvement. As a result, the proposal department can select the most suitable advertising media and improve promotional effectiveness by analyzing the effectiveness of past advertising campaigns. 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 data from past advertising campaigns into a generating AI, and the generating AI can select the most suitable advertising media.

[0055] The proposal department can analyze the target audience's attribute information in detail and propose personalized advertisements. For example, the proposal department can analyze the target audience's age, gender, and interests and propose personalized advertisements. The proposal department can analyze the target audience's past behavioral data and propose the most suitable advertising message. The proposal department can consider the target audience's geographical location and propose region-specific advertisements. In this way, the proposal department can propose personalized advertisements by analyzing the target audience's attribute information in detail and improve promotional effectiveness. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the target audience's attribute information into a generating AI and have the generating AI propose personalized advertisements.

[0056] The proposal department can propose advertising strategies that utilize social media. For example, the proposal department can analyze social media posts and propose the optimal advertising strategy. The proposal department can create advertising messages that leverage social media trends. The proposal department can maximize advertising effectiveness by utilizing social media influencers. In this way, the proposal department can improve promotional effectiveness by proposing advertising strategies that utilize social media. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input social media data into a generating AI and have the generating AI propose the optimal advertising strategy.

[0057] The proposal department can propose different advertising messages depending on the event's theme. For example, the proposal department can create the most suitable advertising message to match the event's theme. The proposal department can propose advertising messages that resonate with the target audience depending on the event's content. The proposal department can propose different advertising messages depending on the event's scale and target audience. This allows the proposal department to improve promotional effectiveness by proposing advertising messages that are appropriate to the event's theme. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data based on the event's theme into a generating AI, and the generating AI can propose the most suitable advertising message.

[0058] The risk management department can analyze past risk cases and detect early signs of risk occurrence. For example, the risk management department can analyze past risk cases and identify early signs of risk occurrence. The risk management department can find commonalities in risk cases and detect early signs. Based on these early signs of risk occurrence, the risk management department can take countermeasures early. In this way, the risk management department can detect early signs of risk occurrence and take countermeasures early by analyzing past risk cases. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input past risk case data into a generating AI, and the generating AI can detect early signs of risk occurrence.

[0059] The Risk Management Department can quantitatively evaluate the impact of risk factors and simulate the effectiveness of countermeasures. For example, the Risk Management Department can quantify the impact of risk factors and determine the priority of countermeasures. The Risk Management Department can simulate the effectiveness of countermeasures and select the optimal countermeasure. The Risk Management Department can compare the impact of risk factors with the effectiveness of countermeasures and formulate the optimal risk management strategy. In this way, the Risk Management Department can formulate the optimal risk management strategy by quantitatively evaluating the impact of risk factors and simulating the effectiveness of countermeasures. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or without AI. For example, the Risk Management Department can input data on risk factors into a generating AI, have the generating AI evaluate the impact, and simulate the effectiveness of countermeasures.

[0060] The Risk Management Department can propose risk countermeasures by considering the impact of natural disasters and social events. For example, the Risk Management Department can analyze the risk of natural disasters and propose countermeasures. The Risk Management Department can propose risk countermeasures by considering the impact of social events (e.g., demonstrations, strikes). The Risk Management Department can simulate the impact of natural disasters and social events and select the optimal countermeasures. In this way, the Risk Management Department can improve the accuracy of risk countermeasures by considering the impact of natural disasters and social events. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or not using AI. For example, the Risk Management Department can input data on natural disasters and social events into a generating AI, and the generating AI can propose risk countermeasures.

[0061] The risk management department can apply different countermeasures depending on the type of risk factor. For example, the risk management department can select the optimal countermeasure depending on the type of risk factor (e.g., weather, competitive events). The risk management department can evaluate the impact of risk factors and adjust the countermeasures. The risk management department can simulate countermeasures according to the type of risk factor and select the optimal method. In this way, the risk management department can improve the accuracy of risk management by applying the optimal countermeasure according to the type of risk factor. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input risk factor data into a generating AI and have the generating AI apply the optimal countermeasure.

[0062] The recommendation department can analyze performance data of artists from past events and select the most suitable artist. For example, the recommendation department can analyze performance data of artists from past events and select the most suitable artist. Based on the artist's performance data, the recommendation department can select an artist that matches the theme of the event. The recommendation department can compare the performance data of artists and select the most suitable artist. In this way, the recommendation department can select an artist that matches the theme of the event by analyzing performance data of artists from past events. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input performance data of artists from past events into a generating AI, and the generating AI can select the most suitable artist.

[0063] The recommendation department can adjust its artist recommendation criteria based on the event's theme and target audience. For example, it can adjust the artist recommendation criteria based on the event's theme. It can adjust the artist recommendation criteria based on the target audience's attribute information. It can adjust the artist recommendation criteria according to the event's scale and target audience. This allows the recommendation department to select more appropriate artists by adjusting the artist recommendation criteria based on the event's theme and target audience. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input data based on the event's theme and target audience into a generating AI, and the generating AI can adjust the artist recommendation criteria.

[0064] The recommendation department can make recommendations considering the artist's schedule and contract terms. For example, the recommendation department can check the artist's schedule and make recommendations that match the event date. The recommendation department can recommend the most suitable artist considering the artist's contract terms. The recommendation department can select the most suitable artist from multiple candidates based on the artist's schedule and contract terms. In this way, the recommendation department can recommend the most suitable artist for the event date by considering the artist's schedule and contract terms. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input data on the artist's schedule and contract terms into a generating AI, and the generating AI can recommend the most suitable artist.

[0065] The recommendation department can recommend different artists depending on the scale and budget of the event. For example, the recommendation department can recommend the most suitable artist based on the scale of the event. The recommendation department can consider the event budget and recommend the most suitable artist within that budget. The recommendation department can select the most suitable artist from multiple candidates based on the scale and budget of the event. In this way, the recommendation department can select the most suitable artist within the budget by recommending different artists depending on the scale and budget of the event. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input data on the scale and budget of the event into a generating AI, and the generating AI can recommend the most suitable artist.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can evaluate the reliability of past event data and prioritize the collection of reliable data. For example, it can verify the source of past event data and prioritize reliable data. It can check the consistency of the data and exclude unreliable data. It can check the frequency of data updates and prioritize the collection of the latest data. As a result, the data collection unit can improve the accuracy of predictions by evaluating the reliability of past event data and prioritizing the collection of reliable data.

[0068] The prediction unit can predict attendance figures while taking into account the impact of competing events. For example, it can predict attendance figures by considering the dates of competing events. It can analyze the popularity of competing events and reflect that in the attendance forecast. It can also predict attendance figures by considering the promotional activities of competing events. In this way, the prediction unit can improve the accuracy of its attendance forecasts by taking into account the impact of competing events.

[0069] The optimization unit can dynamically change prices in real time in response to fluctuations in demand. For example, it can detect sudden changes in demand and immediately change prices. It can analyze real-time demand data and dynamically adjust prices. It can raise prices when demand is at its peak and lower prices when demand is low. In this way, the optimization unit can maximize profits by dynamically changing prices in real time in response to fluctuations in demand.

[0070] The proposal department can analyze the target audience's attributes in detail and propose personalized advertisements. For example, it can analyze the target audience's age, gender, and interests to propose personalized advertisements. It can analyze the target audience's past behavioral data to propose the most suitable advertising message. It can consider the target audience's geographical location to propose region-specific advertisements. In this way, the proposal department can improve promotional effectiveness by proposing personalized advertisements through a detailed analysis of the target audience's attributes.

[0071] The recommendation team can analyze performance data from past events to select the most suitable artists. For example, they can analyze performance data from past events to select the most suitable artists. Based on the artists' performance data, they can select artists that match the event's theme. They can compare the performance data of different artists to select the most suitable artist. In this way, the recommendation team can select artists that match the event's theme by analyzing performance data from past events.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection team gathers historical data and market trends. Specifically, they collect historical event data (number of participants, sales data, feedback, etc.) and market trends (industry reports, consumer trends, etc.). Step 2: The prediction unit analyzes the data collected by the collection unit and predicts the number of attendees. Specifically, it uses statistical analysis and machine learning algorithms to calculate the expected number of attendees for the next event. Step 3: The optimization unit optimizes ticket prices based on the predicted attendance figures from the prediction unit. Specifically, it sets the optimal ticket price based on the event's popularity and past ticket sales data. Step 4: The proposal team proposes effective advertising media and messaging based on the pricing optimized by the optimization team. Specifically, they select the most suitable advertising media for a particular target audience and propose effective messaging. Step 5: The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. Specifically, they propose measures to minimize risk based on weather data and information on competing events. Step 6: The recommendation team recommends artists who are suitable for the event's theme and target audience, based on the measures proposed by the risk management team. Specifically, they select and recommend artists who are best suited to the event's concept and target audience.

[0074] (Example of form 2) The music event planning and management support system according to an embodiment of the present invention is an AI agent that supports the planning and management of music events. This system performs audience prediction, ticket pricing, and promotional strategy optimization. The music event planning and management support system reduces the risks and uncertainties faced by event organizers and supports the holding of events with a high success rate. For example, the music event planning and management support system uses an audience prediction model to analyze past data and market trends to predict the number of attendees with high accuracy. Next, the music event planning and management support system optimizes ticket prices, maximizing revenue through demand-based pricing. Furthermore, the music event planning and management support system proposes targeted advertising, suggesting effective advertising media and messaging to improve promotional effectiveness. In addition, the music event planning and management support system performs risk management, analyzing risk factors such as weather and competing events and proposing countermeasures. Finally, the music event planning and management support system performs artist matching, recommending artists that match the event's theme and target audience. In this way, the music event planning and management support system reduces the risks and uncertainties faced by event organizers and supports the holding of events with a high success rate. This allows the music event planning and management support system to optimize the planning and operation of music events, reduce the risks and uncertainties faced by event organizers, and support the hosting of events with a high success rate.

[0075] The music event planning and management support system according to this embodiment comprises a data collection unit, a forecasting unit, an optimization unit, a proposal unit, a risk management unit, and a recommendation unit. The data collection unit collects historical data and market trends. For example, the data collection unit can collect historical event data and market trends. As historical event data, the data collection unit can collect the number of event participants, sales data, feedback, etc. As market trends, the data collection unit can collect industry reports and consumer trends. The forecasting unit analyzes the data collected by the data collection unit and predicts the number of attendees. For example, the forecasting unit can calculate the expected number of attendees for the next event based on the collected data. As data analysis, the forecasting unit can use statistical analysis and machine learning algorithms. As a method for calculating the number of attendees, the forecasting unit can use a prediction model and comparison with historical data. The optimization unit optimizes ticket prices based on the number of attendees predicted by the forecasting unit. For example, the optimization unit can set the optimal ticket price based on the popularity of the event and past ticket sales data. The Optimization Department can evaluate the popularity of an event by considering factors such as past attendance numbers and social media reactions. The Optimization Department can collect and analyze ticket sales data such as sales figures, sales period, and price range. The Proposal Department proposes effective advertising media and messaging based on the pricing optimized by the Optimization Department. For example, the Proposal Department can select the most suitable advertising media and propose effective messaging for a specific target audience. The Proposal Department can consider factors such as age group, interests, and location when determining the target audience. The Proposal Department can select online advertising, television advertising, radio advertising, etc., as advertising media. The Proposal Department can create advertising copy and promotional messages as messaging. The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. For example, the Risk Management Department can propose measures to minimize risk based on weather data and information on competing events. The Risk Management Department can collect and analyze weather data such as weather forecasts and historical weather data.The Risk Management Department can collect and analyze information on competing events, such as the date, time, location, and number of participants. The Recommendation Department recommends artists that are suitable for the event's theme and target audience based on the countermeasures proposed by the Risk Management Department. For example, the Recommendation Department can select and recommend artists that are best suited to the event's concept and target audience. The Recommendation Department can consider music genres and the event's theme as part of the event's concept. The Recommendation Department can consider age groups, interests, and geographical location as part of the target audience. As a result, the music event planning and management support system according to this embodiment can support the planning and management of music events, reduce the risks and uncertainties faced by event organizers, and support the holding of events with a high success rate by forecasting attendance, setting ticket prices, and optimizing promotion strategies.

[0076] The data collection department collects historical data and market trends. Specifically, it can collect historical event data such as the number of event attendees, sales data, and feedback. This data is an important source of information for analyzing the success and failure factors of an event. For example, the number of attendees is used to evaluate the scale and popularity of an event, and sales data is used to determine profitability and the appropriateness of ticket prices. Feedback data is important for understanding participant satisfaction and areas for improvement, and can be reflected in planning future events. The data collection department can also collect market trends such as industry reports and consumer trends. Industry reports are useful for understanding trends and new developments across the music industry, and consumer trends are important for understanding the preferences and behavioral patterns of the target audience. This allows the data collection department to comprehensively analyze historical data and market trends and provide foundational information for event planning. Furthermore, the data collection department can also collect data from social media and online platforms. For example, it can collect word-of-mouth, ratings, and participant posts about events to evaluate the reputation and impact of an event. This allows the data collection department to collect data from multiple perspectives and build an information foundation for successful event planning.

[0077] The prediction unit analyzes the data collected by the collection unit to predict the number of attendees. Specifically, it can calculate the expected number of attendees for the next event based on the collected data. The prediction unit can use statistical analysis and machine learning algorithms for data analysis. In statistical analysis, regression analysis and time series analysis are performed based on past data to understand trends and patterns in the number of attendees. In machine learning algorithms, a model is built to predict the number of attendees for the next event by learning from past event data and market trends. For example, by using event participant numbers, sales data, and feedback data as input data and training the prediction model, the expected number of attendees for the next event can be calculated with high accuracy. The prediction unit can use prediction models and comparisons with past data as methods for calculating the number of attendees. Prediction models can be built to make more accurate predictions by constructing complex models that take multiple variables into consideration. In comparisons with past data, the number of attendees is predicted based on data from similar events. In this way, the prediction unit can effectively utilize the collected data and predict the number of attendees for the next event with high accuracy. Furthermore, the prediction unit can update prediction results in real time and make predictions based on the latest data. This allows the prediction unit to support important decision-making in event planning and help ensure the successful hosting of events.

[0078] The optimization unit optimizes ticket prices based on the predicted attendance figures from the forecasting unit. Specifically, it can set optimal ticket prices based on the event's popularity and past ticket sales data. The optimization unit can evaluate past attendance figures and social media reactions as indicators of event popularity. For example, if past events have had a high attendance rate or positive social media reactions, it can determine that the event is highly popular and set appropriate ticket prices. The optimization unit can collect and analyze ticket sales data such as sales volume, sales period, and price range. This allows it to formulate optimal sales strategies based on past sales data. For example, by analyzing sales volume and sales period, it can set optimal start and duration for sales, maximizing ticket sales. By analyzing price ranges, it can set prices appropriate for the target audience, maximizing revenue. Furthermore, the optimization unit can implement dynamic pricing. Dynamic pricing allows for real-time adjustment of ticket prices in response to fluctuations in supply and demand. This maximizes revenue by raising prices when demand is high and lowering prices when demand is low. The optimization unit can use this data and algorithms to set optimal ticket prices and improve the profitability of the event.

[0079] The Proposal Department proposes effective advertising media and messaging based on pricing optimized by the Optimization Department. Specifically, it can select the most suitable advertising media and propose effective messages for a particular target audience. The Proposal Department can consider age group, interests, and location when determining the target audience. For example, social media and online advertising are effective when targeting younger generations, while television and radio advertising are suitable when targeting middle-aged and older generations. The Proposal Department can select online advertising, television advertising, and radio advertising as advertising media. Online advertising can reach the target audience using social media, search engine advertising, and display advertising. Television and radio advertising can effectively convey messages to a broad audience. The Proposal Department can create advertising copy and promotional messages as messaging. Advertising copy is a crucial element for attracting the target audience's interest and encouraging action, while promotional messages can increase participation by highlighting the event's appeal and benefits. This allows the Proposal Department to develop the optimal advertising strategy and maximize event attendance. Furthermore, the Proposal Department can monitor the effectiveness of the advertising campaign and modify the strategy as needed. This allows the proposal department to consistently provide the optimal advertising strategy and support the success of the event.

[0080] The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. Specifically, it can propose measures to minimize risk based on weather data and information on competing events. The Risk Management Department can collect and analyze weather forecasts and historical weather data as weather data. For example, if bad weather is expected on the day of the event, it can consider securing an indoor venue or postponing the event. By analyzing historical weather data, it can evaluate weather risks in specific seasons and regions and take appropriate measures. The Risk Management Department can collect and analyze information on competing events, such as the date, time, location, and number of participants. If competing events are held at the same time, it may affect attendance, so it is important to evaluate the scale and popularity of competing events and take appropriate measures. For example, to differentiate from competing events, promotions emphasizing unique attractions and benefits can be developed. In this way, the Risk Management Department can comprehensively analyze the risk factors for the success of the event and propose appropriate countermeasures. Furthermore, the Risk Management Department can monitor the implementation status of risk countermeasures and modify them as needed. In this way, the Risk Management Department can minimize the risks of the event and increase the success rate.

[0081] The recommendation department recommends artists that are suitable for the event's theme and target audience, based on the measures proposed by the risk management department. Specifically, it can select and recommend artists that are best suited to the event's concept and target audience. The recommendation department can consider music genres and the event's theme as part of the event's concept. For example, for a rock festival, it can recommend popular rock bands or up-and-coming artists. For a jazz festival, it can recommend renowned jazz musicians or local jazz bands. The recommendation department can consider age groups, interests, and regions as part of the target audience. For example, if targeting a young audience, it can recommend artists popular on social media or artists who are sensitive to trends. If targeting a middle-aged or older audience, it can recommend artists with nostalgic hit songs or artists with a calm atmosphere. In this way, the recommendation department can select artists that are best suited to the event's theme and target audience, maximizing the event's appeal. Furthermore, the recommendation department can check artists' schedules and contract terms to make feasible recommendations. In this way, the recommendation department can effectively support artist selection, a crucial element for the event's success, and enhance the event's appeal.

[0082] The data collection unit can collect historical event data and market trends. For example, as historical event data, the data collection unit can collect event attendance figures, sales data, and feedback. Based on the number of event attendees, the data collection unit can evaluate the scale and popularity of events. Based on sales data, the data collection unit can evaluate the profitability of events. Based on feedback, the data collection unit can identify success factors and areas for improvement for events. As market trends, the data collection unit can collect industry reports and consumer trends. Based on industry reports, the data collection unit can understand overall industry trends and the activities of competitors. Based on consumer trends, the data collection unit can understand consumer preferences and behavioral patterns. As a result, by collecting historical event data and market trends, the data collection unit can improve the accuracy of attendance predictions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use generative AI to collect and analyze data in order to collect historical event data and market trends.

[0083] The prediction unit can analyze the data collected by the collection unit and calculate the expected attendance for the next event. For example, the prediction unit can calculate the expected attendance for the next event based on the collected data. The prediction unit can use statistical analysis and machine learning algorithms for data analysis. For statistical analysis, the prediction unit can use regression analysis and time series analysis. For machine learning algorithms, the prediction unit can use random forests and neural networks. As a method for calculating attendance, the prediction unit can use prediction models and comparisons with past data. For prediction models, the prediction unit can use linear regression models and nonlinear regression models. For comparisons with past data, the prediction unit can use data from similar events. In this way, the prediction unit can improve the success rate of events by analyzing the collected data and calculating the expected attendance for the next event. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the collected data into a generating AI, and the generating AI can predict the attendance.

[0084] The optimization unit can set the optimal ticket price based on the event's popularity and past ticket sales data. For example, the optimization unit can evaluate the event's popularity by assessing past attendance figures and social media reactions. The optimization unit can evaluate the event's popularity based on past attendance figures. The optimization unit can evaluate the event's popularity based on social media reactions. The optimization unit can collect and analyze ticket sales data such as the number of tickets sold, sales period, and price range. The optimization unit can evaluate ticket demand based on the number of tickets sold. The optimization unit can evaluate ticket sales strategies based on the sales period. The optimization unit can evaluate ticket pricing based on the price range. As a result, the optimization unit can maximize revenue by setting the optimal ticket price based on the event's popularity and past ticket sales data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not. For example, the optimization unit can input the event's popularity and past ticket sales data into a generating AI, which can then set the optimal ticket price.

[0085] The proposal department can select the most suitable advertising media for a specific target audience and propose effective messages. For example, the proposal department can consider age groups, interests, and geographical location as part of the target audience. The proposal department can identify target audiences based on age groups, interests, and geographical location. The proposal department can select online advertising, television advertising, and radio advertising as advertising media. Based on online advertising, the proposal department can select the most suitable advertising media for the target audience. Based on television advertising, the proposal department can select the most suitable advertising media for the target audience. Based on radio advertising, the proposal department can create advertising copy and promotional messages. Based on advertising copy, the proposal department can create messages optimized for the target audience. Based on promotional messages, the proposal department can create messages optimized for the target audience. This allows the proposal department to select the most suitable advertising media for a specific target audience and propose effective messages, thereby improving the effectiveness of promotions. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the most suitable advertising media and messages for the target audience into a generation AI, and the generation AI can then propose the most suitable advertising media and messages.

[0086] The Risk Management Department can propose measures to minimize risk based on weather data and information on competing events. For example, the Risk Management Department can collect and analyze weather forecasts and historical weather data as weather data. Based on weather forecasts, the Risk Management Department can predict the weather on the day of the event and propose measures to minimize risk. Based on historical weather data, the Risk Management Department can predict the weather on the day of the event and propose measures to minimize risk. The Risk Management Department can collect and analyze information on competing events, such as the date and time, location, and number of participants. Based on the date and time, the Risk Management Department can propose measures to avoid overlap with competing events. Based on the location, the Risk Management Department can propose measures to avoid overlap with competing events. Based on the number of participants, the Risk Management Department can propose measures to avoid overlap with competing events. In this way, the Risk Management Department can improve the success rate of the event by proposing measures to minimize risk based on weather data and information on competing events. Some or all of the above processing in the Risk Management Department may be performed using AI, for example, or not using AI. For example, the risk management department can input weather data and competitive event information into a generating AI, which can then propose measures to minimize risk.

[0087] The recommendation department can select and recommend artists that are best suited to the event's concept and target audience. For example, the recommendation department can consider music genres and the event's theme as part of the event's concept. Based on music genres, the recommendation department can select artists that are best suited to the event's concept. Based on the event's theme, the recommendation department can select artists that are best suited to the event's concept. The recommendation department can consider age groups, interests, and regions as part of the target audience. Based on age groups, the recommendation department can select artists that are best suited to the target audience. Based on interests, the recommendation department can select artists that are best suited to the target audience. Based on regions, the recommendation department can select artists that are best suited to the target audience. In this way, the recommendation department can enhance the appeal of the event by selecting and recommending artists that are best suited to the event's concept and target audience. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the event's concept and target audience's best suited artists into a generating AI, and the generating AI can recommend the best artists.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. If the user is in a hurry, the data collection unit can prioritize collecting only important data. In this way, the data collection unit can reduce the burden on the user by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can adjust the timing of data collection.

[0089] The data collection unit can evaluate the reliability of past event data and prioritize the collection of reliable data. For example, the data collection unit can verify the source of past event data and prioritize reliable data. The data collection unit can check data consistency and exclude unreliable data. The data collection unit can verify the data update frequency and prioritize the collection of the most recent data. This allows the data collection unit to improve prediction accuracy by evaluating the reliability of past event data and prioritizing the collection of reliable data. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the reliability of past event data into a generating AI, which can then prioritize the collection of reliable data.

[0090] The data collection unit can detect changes in market trends in real time and dynamically change the types of data collected. For example, the data collection unit can detect rapid changes in market trends and immediately change the types of data collected. The data collection unit can analyze real-time market data and dynamically add necessary data. The data collection unit can predict market trends and plan future data collection. As a result, the data collection unit can reflect the latest market information by detecting changes in market trends in real time and dynamically changing the types of data collected. 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 changes in market trends into a generating AI, and the generating AI can dynamically change the types of data collected.

[0091] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, the data collection unit can prioritize collecting important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of data to collect.

[0092] The data collection unit can collect data from social media and gather real-time reactions to events. For example, the data collection unit can collect social media posts in real time and analyze reactions to events. The data collection unit can use hashtags to collect posts related to specific events. The data collection unit can analyze social media trends and evaluate the popularity of events. In this way, the data collection unit can grasp real-time reactions to events by collecting data from social media. 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 social media post data into a generating AI, and the generating AI can collect real-time reactions to events.

[0093] The data collection unit can prioritize the collection of data from specific regions, taking geographical market trends into consideration. For example, the data collection unit can analyze market trends in a specific region and prioritize the collection of data from that region. The data collection unit can analyze the trends of event participants in each region and determine the priority of data collection. The data collection unit can collect information on competing events in each region and predict the success rate of events. In this way, the data collection unit can reflect regional market trends by prioritizing the collection of data from specific regions, taking geographical market trends into consideration. 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 market trend data from a specific region into a generating AI, and the generating AI can prioritize the collection of data from that specific region.

[0094] The prediction unit can estimate the user's emotions and adjust the parameters of the prediction model based on the estimated user emotions. For example, if the user is stressed, the prediction unit can set the parameters of the prediction model conservatively. If the user is relaxed, the prediction unit can set the parameters of the prediction model aggressively. If the user is in a hurry, the prediction unit can adjust the parameters to obtain prediction results quickly. In this way, the prediction unit can improve the accuracy of predictions by adjusting the parameters of the prediction model based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI, and the generative AI can adjust the parameters of the prediction model.

[0095] The prediction unit can improve prediction accuracy by analyzing the success and failure factors of past events. For example, the prediction unit can identify the success factors of past events and reflect them in the prediction model. The prediction unit can analyze the failure factors of past events and find areas for improvement in the prediction model. The prediction unit can improve prediction accuracy by comparing the success and failure factors. In this way, the prediction unit can improve prediction accuracy by analyzing the success and failure factors of past events. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the success and failure factors of past events into a generating AI, and the generating AI can improve prediction accuracy.

[0096] The prediction unit can make predictions by taking into account fluctuations in visitor numbers according to seasons and specific event periods. For example, the prediction unit can analyze seasonal fluctuations in visitor numbers and reflect them in the prediction model. The prediction unit can consider fluctuations in visitor numbers according to specific event periods (e.g., Christmas, summer holidays). The prediction unit can predict visitor numbers based on past data according to seasons and event periods. In this way, the prediction unit can improve the accuracy of its predictions by taking into account fluctuations in visitor numbers according to seasons and specific event periods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data according to seasons and specific event periods into a generating AI, and the generating AI can make predictions about visitor numbers.

[0097] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is stressed, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can provide a display method that includes detailed information. If the user is in a hurry, the prediction unit can provide a display method that gets straight to the point. In this way, the prediction unit can provide a display that is easy for the user to understand by adjusting the display method of the prediction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the prediction results.

[0098] The prediction unit can predict attendance figures by taking into account the impact of competing events. For example, the prediction unit can predict attendance figures by considering the dates of competing events. The prediction unit can analyze the popularity of competing events and reflect this in the attendance figure prediction. The prediction unit can predict attendance figures by considering the promotional activities of competing events. In this way, the prediction unit can improve the accuracy of its attendance figure predictions by taking into account the impact of competing events. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input data on competing events into a generating AI, and the generating AI can perform the attendance figure prediction.

[0099] The prediction unit can apply different prediction algorithms based on the theme and content of the event. For example, the prediction unit can select the optimal prediction algorithm according to the theme of the event. The prediction unit can adjust the prediction algorithm based on the content of the event (e.g., concert, festival). The prediction unit can apply different prediction algorithms according to the scale and target audience of the event. This allows the prediction unit to improve prediction accuracy by applying the optimal prediction algorithm according to the theme and content of the event. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input different prediction algorithms based on the theme and content of the event into a generating AI, and the generating AI can apply the optimal prediction algorithm.

[0100] The optimization unit can estimate the user's emotions and adjust the pricing strategy based on the estimated emotions. For example, if the user is stressed, the optimization unit can set prices conservatively. If the user is relaxed, the optimization unit can set prices aggressively. If the user is in a hurry, the optimization unit can set prices quickly. This allows the optimization unit to set more appropriate prices by adjusting the pricing strategy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the pricing strategy.

[0101] The optimization unit can analyze ticket sales history and determine the optimal sales timing. For example, the optimization unit can analyze past ticket sales data to identify the optimal sales timing. The optimization unit can predict peak demand periods from ticket sales history. Based on sales history, the optimization unit can optimize the start and end times of sales. In this way, the optimization unit can determine the optimal sales timing by analyzing ticket sales history and maximize revenue. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past ticket sales data into a generating AI, and the generating AI can determine the optimal sales timing.

[0102] The optimization unit can dynamically change prices in real time in response to fluctuations in demand. For example, the optimization unit can detect sudden fluctuations in demand and immediately change prices. The optimization unit can analyze real-time demand data and dynamically adjust prices. The optimization unit can raise prices when demand is at its peak and lower prices when demand is low. In this way, the optimization unit can maximize profits by dynamically changing prices in real time in response to fluctuations in demand. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time demand data into a generating AI, and the generating AI can dynamically change prices.

[0103] The optimization unit can estimate the user's emotions and adjust the price display method based on the estimated emotions. For example, if the user is stressed, the optimization unit can display a simple and highly visible price. If the user is relaxed, the optimization unit can provide detailed price information. If the user is in a hurry, the optimization unit can display a concise price. In this way, the optimization unit can provide a display that is easy for the user to understand by adjusting the price display method based on the user'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 optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into a generative AI, and the generative AI can adjust the price display method.

[0104] The optimization unit can apply different pricing strategies depending on the type of ticket. For example, the optimization unit can set a higher price for VIP tickets and offer more benefits. For general tickets, it can set a mid-range price to appeal to a broad target audience. For student tickets, it can set a lower price to attract students. In this way, the optimization unit can maximize revenue by applying different pricing strategies depending on the type of ticket. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input data corresponding to the type of ticket into a generating AI, and the generating AI can apply different pricing strategies.

[0105] The optimization unit can optimize prices on a regional basis, taking into account regional demand. For example, the optimization unit can analyze regional demand data and optimize prices. The optimization unit can adjust pricing considering regional economic conditions. The optimization unit can optimize prices considering the impact of regional competitive events. In this way, the optimization unit can maximize revenue by optimizing prices on a regional basis, taking into account regional demand. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional demand data into a generating AI, and the generating AI can optimize prices on a regional basis.

[0106] The suggestion unit can estimate the user's emotions and adjust the content of the ad message based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and highly visible ad message. If the user is relaxed, the suggestion unit can provide an ad message that includes detailed information. If the user is in a hurry, the suggestion unit can provide a concise ad message. In this way, the suggestion unit can provide more effective ads by adjusting the content of the ad message based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of the ad message.

[0107] The proposal department can analyze the effectiveness of past advertising campaigns and select the most suitable advertising media. For example, the proposal department can analyze data from past advertising campaigns to identify highly effective advertising media. The proposal department can analyze the success factors of advertising campaigns and select the most suitable advertising media. The proposal department can analyze the failure factors of advertising campaigns and find areas for improvement. As a result, the proposal department can select the most suitable advertising media and improve promotional effectiveness by analyzing the effectiveness of past advertising campaigns. 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 data from past advertising campaigns into a generating AI, and the generating AI can select the most suitable advertising media.

[0108] The proposal department can analyze the target audience's attribute information in detail and propose personalized advertisements. For example, the proposal department can analyze the target audience's age, gender, and interests and propose personalized advertisements. The proposal department can analyze the target audience's past behavioral data and propose the most suitable advertising message. The proposal department can consider the target audience's geographical location and propose region-specific advertisements. In this way, the proposal department can propose personalized advertisements by analyzing the target audience's attribute information in detail and improve promotional effectiveness. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the target audience's attribute information into a generating AI and have the generating AI propose personalized advertisements.

[0109] The suggestion unit can estimate the user's emotions and adjust the timing of ad display based on the estimated emotions. For example, if the user is stressed, the suggestion unit can reduce the frequency of ad display. If the user is relaxed, the suggestion unit can increase the frequency of ad display. If the user is in a hurry, the suggestion unit can prioritize displaying only important ads. In this way, the suggestion unit can provide more effective ads by adjusting the timing of ad display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can adjust the timing of ad display.

[0110] The proposal department can propose advertising strategies that utilize social media. For example, the proposal department can analyze social media posts and propose the optimal advertising strategy. The proposal department can create advertising messages that leverage social media trends. The proposal department can maximize advertising effectiveness by utilizing social media influencers. In this way, the proposal department can improve promotional effectiveness by proposing advertising strategies that utilize social media. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input social media data into a generating AI and have the generating AI propose the optimal advertising strategy.

[0111] The proposal department can propose different advertising messages depending on the event's theme. For example, the proposal department can create the most suitable advertising message to match the event's theme. The proposal department can propose advertising messages that resonate with the target audience depending on the event's content. The proposal department can propose different advertising messages depending on the event's scale and target audience. This allows the proposal department to improve promotional effectiveness by proposing advertising messages that are appropriate to the event's theme. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data based on the event's theme into a generating AI, and the generating AI can propose the most suitable advertising message.

[0112] The risk management unit can estimate the user's emotions and determine the priority of risk countermeasures based on the estimated emotions. For example, if the user is stressed, the risk management unit can prioritize implementing important risk countermeasures. If the user is relaxed, the risk management unit can implement detailed risk countermeasures. If the user is in a hurry, the risk management unit can prioritize risk countermeasures that can be implemented quickly. In this way, the risk management unit can prioritize important risk countermeasures by determining the priority of risk countermeasures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk management unit may be performed using AI, for example, or not using AI. For example, the risk management unit can input user emotion data into a generative AI, and the generative AI can determine the priority of risk countermeasures.

[0113] The risk management department can analyze past risk cases and detect early signs of risk occurrence. For example, the risk management department can analyze past risk cases and identify early signs of risk occurrence. The risk management department can find commonalities in risk cases and detect early signs. Based on these early signs of risk occurrence, the risk management department can take countermeasures early. In this way, the risk management department can detect early signs of risk occurrence and take countermeasures early by analyzing past risk cases. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input past risk case data into a generating AI, and the generating AI can detect early signs of risk occurrence.

[0114] The Risk Management Department can quantitatively evaluate the impact of risk factors and simulate the effectiveness of countermeasures. For example, the Risk Management Department can quantify the impact of risk factors and determine the priority of countermeasures. The Risk Management Department can simulate the effectiveness of countermeasures and select the optimal countermeasure. The Risk Management Department can compare the impact of risk factors with the effectiveness of countermeasures and formulate the optimal risk management strategy. In this way, the Risk Management Department can formulate the optimal risk management strategy by quantitatively evaluating the impact of risk factors and simulating the effectiveness of countermeasures. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or without AI. For example, the Risk Management Department can input data on risk factors into a generating AI, have the generating AI evaluate the impact, and simulate the effectiveness of countermeasures.

[0115] The risk management unit can estimate the user's emotions and adjust the way risk countermeasures are displayed based on the estimated emotions. For example, if the user is stressed, the risk management unit can provide a simple and highly visible display. If the user is relaxed, the risk management unit can provide a display that includes detailed information. If the user is in a hurry, the risk management unit can provide a display that gets straight to the point. In this way, the risk management unit can provide a display that is easy for the user to understand by adjusting the way risk countermeasures are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk management unit may be performed using AI or not using AI. For example, the risk management unit can input user emotion data into a generative AI and have the generative AI adjust the way risk countermeasures are displayed.

[0116] The Risk Management Department can propose risk countermeasures by considering the impact of natural disasters and social events. For example, the Risk Management Department can analyze the risk of natural disasters and propose countermeasures. The Risk Management Department can propose risk countermeasures by considering the impact of social events (e.g., demonstrations, strikes). The Risk Management Department can simulate the impact of natural disasters and social events and select the optimal countermeasures. In this way, the Risk Management Department can improve the accuracy of risk countermeasures by considering the impact of natural disasters and social events. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or not using AI. For example, the Risk Management Department can input data on natural disasters and social events into a generating AI, and the generating AI can propose risk countermeasures.

[0117] The risk management department can apply different countermeasures depending on the type of risk factor. For example, the risk management department can select the optimal countermeasure depending on the type of risk factor (e.g., weather, competitive events). The risk management department can evaluate the impact of risk factors and adjust the countermeasures. The risk management department can simulate countermeasures according to the type of risk factor and select the optimal method. In this way, the risk management department can improve the accuracy of risk management by applying the optimal countermeasure according to the type of risk factor. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input risk factor data into a generating AI and have the generating AI apply the optimal countermeasure.

[0118] The recommendation system can estimate the user's emotions and recommend artists based on those emotions. For example, if the user is relaxed, the recommendation system can recommend artists with a calm atmosphere. If the user is excited, the recommendation system can recommend energetic artists. If the user is stressed, the recommendation system can recommend relaxing artists. This allows the recommendation system to select artists that are appropriate for the event's theme and target audience by recommending artists based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI, which can then recommend artists.

[0119] The recommendation department can analyze performance data of artists from past events and select the most suitable artist. For example, the recommendation department can analyze performance data of artists from past events and select the most suitable artist. Based on the artist's performance data, the recommendation department can select an artist that matches the theme of the event. The recommendation department can compare the performance data of artists and select the most suitable artist. In this way, the recommendation department can select an artist that matches the theme of the event by analyzing performance data of artists from past events. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input performance data of artists from past events into a generating AI, and the generating AI can select the most suitable artist.

[0120] The recommendation department can adjust its artist recommendation criteria based on the event's theme and target audience. For example, it can adjust the artist recommendation criteria based on the event's theme. It can adjust the artist recommendation criteria based on the target audience's attribute information. It can adjust the artist recommendation criteria according to the event's scale and target audience. This allows the recommendation department to select more appropriate artists by adjusting the artist recommendation criteria based on the event's theme and target audience. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input data based on the event's theme and target audience into a generating AI, and the generating AI can adjust the artist recommendation criteria.

[0121] The recommendation system can estimate the user's emotions and display reasons for recommending artists based on those emotions. For example, if the user is relaxed, the recommendation system can recommend artists with a calm atmosphere and display the reason. If the user is excited, the recommendation system can recommend energetic artists and display the reason. If the user is stressed, the recommendation system can recommend relaxing artists and display the reason. In this way, the recommendation system can make recommendations that are convincing to the user by displaying reasons for recommending artists based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input the user's emotion data into a generative AI, and the generative AI can display reasons for recommending artists.

[0122] The recommendation department can make recommendations considering the artist's schedule and contract terms. For example, the recommendation department can check the artist's schedule and make recommendations that match the event date. The recommendation department can recommend the most suitable artist considering the artist's contract terms. The recommendation department can select the most suitable artist from multiple candidates based on the artist's schedule and contract terms. In this way, the recommendation department can recommend the most suitable artist for the event date by considering the artist's schedule and contract terms. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input data on the artist's schedule and contract terms into a generating AI, and the generating AI can recommend the most suitable artist.

[0123] The recommendation department can recommend different artists depending on the scale and budget of the event. For example, the recommendation department can recommend the most suitable artist based on the scale of the event. The recommendation department can consider the event budget and recommend the most suitable artist within that budget. The recommendation department can select the most suitable artist from multiple candidates based on the scale and budget of the event. In this way, the recommendation department can select the most suitable artist within the budget by recommending different artists depending on the scale and budget of the event. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input data on the scale and budget of the event into a generating AI, and the generating AI can recommend the most suitable artist.

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

[0125] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the burden. If the user is relaxed, the frequency of data collection can be increased to collect more detailed information. If the user is in a hurry, only important data can be prioritized for collection. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection based on the user's emotions.

[0126] The prediction unit can estimate the user's emotions and adjust the parameters of the prediction model based on the estimated emotions. For example, if the user is stressed, the parameters of the prediction model can be set conservatively. If the user is relaxed, the parameters of the prediction model can be set aggressively. If the user is in a hurry, the parameters can be adjusted to obtain prediction results quickly. In this way, the prediction unit can improve the accuracy of predictions by adjusting the parameters of the prediction model based on the user's emotions.

[0127] The optimization unit can estimate the user's emotions and adjust the pricing strategy based on those emotions. For example, if the user is stressed, pricing can be set conservatively. If the user is relaxed, pricing can be set aggressively. If the user is in a hurry, pricing can be set quickly. In this way, the optimization unit can set more appropriate prices by adjusting the pricing strategy based on the user's emotions.

[0128] The proposal team can estimate the user's emotions and adjust the content of the ad message based on those emotions. For example, if the user is stressed, a simple and highly visible ad message can be provided. If the user is relaxed, an ad message containing detailed information can be provided. If the user is in a hurry, a concise ad message can be provided. In this way, the proposal team can provide more effective ads by adjusting the content of the ad message based on the user's emotions.

[0129] The risk management department can estimate user emotions and prioritize risk mitigation measures based on those emotions. For example, if a user is stressed, critical risk mitigation measures can be prioritized. If a user is relaxed, detailed risk mitigation measures can be implemented. If a user is in a hurry, risk mitigation measures that can be implemented quickly can be prioritized. In this way, the risk management department can prioritize critical risk mitigation measures by determining priority based on user emotions.

[0130] The data collection unit can evaluate the reliability of past event data and prioritize the collection of reliable data. For example, it can verify the source of past event data and prioritize reliable data. It can check the consistency of the data and exclude unreliable data. It can check the frequency of data updates and prioritize the collection of the latest data. As a result, the data collection unit can improve the accuracy of predictions by evaluating the reliability of past event data and prioritizing the collection of reliable data.

[0131] The prediction unit can predict attendance figures while taking into account the impact of competing events. For example, it can predict attendance figures by considering the dates of competing events. It can analyze the popularity of competing events and reflect that in the attendance forecast. It can also predict attendance figures by considering the promotional activities of competing events. In this way, the prediction unit can improve the accuracy of its attendance forecasts by taking into account the impact of competing events.

[0132] The optimization unit can dynamically change prices in real time in response to fluctuations in demand. For example, it can detect sudden changes in demand and immediately change prices. It can analyze real-time demand data and dynamically adjust prices. It can raise prices when demand is at its peak and lower prices when demand is low. In this way, the optimization unit can maximize profits by dynamically changing prices in real time in response to fluctuations in demand.

[0133] The proposal department can analyze the target audience's attributes in detail and propose personalized advertisements. For example, it can analyze the target audience's age, gender, and interests to propose personalized advertisements. It can analyze the target audience's past behavioral data to propose the most suitable advertising message. It can consider the target audience's geographical location to propose region-specific advertisements. In this way, the proposal department can improve promotional effectiveness by proposing personalized advertisements through a detailed analysis of the target audience's attributes.

[0134] The recommendation team can analyze performance data from past events to select the most suitable artists. For example, they can analyze performance data from past events to select the most suitable artists. Based on the artists' performance data, they can select artists that match the event's theme. They can compare the performance data of different artists to select the most suitable artist. In this way, the recommendation team can select artists that match the event's theme by analyzing performance data from past events.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection team gathers historical data and market trends. Specifically, they collect historical event data (number of participants, sales data, feedback, etc.) and market trends (industry reports, consumer trends, etc.). Step 2: The prediction unit analyzes the data collected by the collection unit and predicts the number of attendees. Specifically, it uses statistical analysis and machine learning algorithms to calculate the expected number of attendees for the next event. Step 3: The optimization unit optimizes ticket prices based on the predicted attendance figures from the prediction unit. Specifically, it sets the optimal ticket price based on the event's popularity and past ticket sales data. Step 4: The proposal team proposes effective advertising media and messaging based on the pricing optimized by the optimization team. Specifically, they select the most suitable advertising media for a particular target audience and propose effective messaging. Step 5: The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the Proposal Department and proposes countermeasures. Specifically, they propose measures to minimize risk based on weather data and information on competing events. Step 6: The recommendation team recommends artists who are suitable for the event's theme and target audience, based on the measures proposed by the risk management team. Specifically, they select and recommend artists who are best suited to the event's concept and target audience.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, prediction unit, optimization unit, proposal unit, risk management unit, and recommendation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict the number of attendees. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and optimizes ticket prices based on the predicted number of attendees. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14, and proposes advertising media and messaging based on the optimized price. The risk management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes risk factors and proposes countermeasures. The recommendation unit is implemented, for example, by the control unit 46A of the smart device 14, and recommends artists that match the theme and target of the event. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0154] 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.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0156] Each of the multiple elements described above, including the data collection unit, prediction unit, optimization unit, proposal unit, risk management unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to predict the number of attendees. The optimization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which optimizes ticket prices based on the predicted number of attendees. The proposal unit is implemented, for example, in the control unit 46A of the smart glasses 214, which proposes advertising media and messaging based on the optimized price. The risk management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes risk factors and proposes countermeasures. The recommendation unit is implemented, for example, in the control unit 46A of the smart glasses 214, which recommends artists that match the event's theme and target audience. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0172] Each of the multiple elements described above, including the data collection unit, prediction unit, optimization unit, proposal unit, risk management unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to predict the number of attendees. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes ticket prices based on the predicted number of attendees. The proposal unit is implemented in the specific processing unit 46A of the headset terminal 314 and proposes advertising media and messaging based on the optimized price. The risk management unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes risk factors and proposes countermeasures. The recommendation unit is implemented in the specific processing unit 46A of the headset terminal 314 and recommends artists that match the event's theme and target audience. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0187] 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.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0189] Each of the multiple elements described above, including the data collection unit, prediction unit, optimization unit, proposal unit, risk management unit, and recommendation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to predict the number of attendees. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which optimizes ticket prices based on the predicted number of attendees. The proposal unit is implemented, for example, by the control unit 46A of the robot 414, which proposes advertising media and messaging based on the optimized price. The risk management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes risk factors and proposes countermeasures. The recommendation unit is implemented, for example, by the control unit 46A of the robot 414, which recommends artists that match the theme and target of the event. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The data collection department collects historical data and market trends, A prediction unit analyzes the data collected by the aforementioned collection unit and predicts the number of customers, An optimization unit optimizes ticket prices based on the number of attendees predicted by the prediction unit, A proposal unit proposes effective advertising media and messaging based on the price optimized by the aforementioned optimization unit, The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the aforementioned Proposal Department and proposes countermeasures. Based on the measures proposed by the aforementioned Risk Management Department, the Recommendation Department recommends artists who are suitable for the event's theme and target audience, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect past event data and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, The data collected by the aforementioned data collection unit is analyzed to calculate the expected number of attendees for the next event. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, We set the optimal ticket price based on the event's popularity and past ticket sales data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We select the most suitable advertising media and propose effective messages for specific target audiences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned risk management department, Based on weather data and information on competing events, we propose measures to minimize risks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recommendation department, Select and recommend the most suitable artist for the event's concept and target audience. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Evaluate the reliability of past event data and prioritize collecting reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It detects changes in market trends in real time and dynamically changes the types of data collected. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We collect data from social media to gather real-time reactions to events. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Taking geographical market trends into consideration, we will focus on collecting data from specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, It estimates the user's emotions and adjusts the parameters of the predictive model based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, Analyze the success and failure factors of past events to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, The forecast takes into account fluctuations in visitor numbers depending on the season and specific event periods. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, We will forecast the number of attendees, taking into account the impact of competing events. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, Apply different prediction algorithms based on the event's theme and content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, We estimate user sentiment and adjust pricing strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, We analyze ticket sales history to determine the optimal timing for sales. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, The price is dynamically changed in real time in response to fluctuations in demand. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It estimates user sentiment and adjusts how prices are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, We apply different pricing strategies depending on the type of ticket. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, Optimize pricing by region, taking into account the demand in each area. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the ad message based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Analyze the effectiveness of past advertising campaigns and select the most suitable advertising media. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, We analyze the attribute information of the target audience in detail and propose personalized advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of ad display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, We propose advertising strategies that utilize social media. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, We propose different advertising messages depending on the event's theme. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned risk management department, The system estimates user sentiment and prioritizes risk mitigation measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned risk management department, Analyze past risk cases to detect early signs of risk occurrence. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned risk management department, Quantitatively evaluate the impact of risk factors and simulate the effectiveness of countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned risk management department, The system estimates the user's emotions and adjusts how risk countermeasures are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned risk management department, We propose risk management measures that take into account the impact of natural disasters and social events. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned risk management department, Depending on the type of risk factor, different mitigation methods should be applied. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned recommendation department, It estimates the user's emotions and recommends artists based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned recommendation department, We analyze past performance data of artists at past events to select the most suitable artists. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned recommendation department, We adjust the artist recommendation criteria based on the event's theme and target audience. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned recommendation department, It estimates the user's emotions and displays the artist's recommendation reason based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recommendation department, We make recommendations taking into consideration the artist's schedule and contract terms. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned recommendation department, We recommend different artists depending on the scale and budget of the event. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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. The data collection department collects historical data and market trends, A prediction unit analyzes the data collected by the aforementioned collection unit and predicts the number of customers, An optimization unit optimizes ticket prices based on the number of attendees predicted by the prediction unit, A proposal unit proposes effective advertising media and messaging based on the price optimized by the aforementioned optimization unit, The Risk Management Department analyzes risk factors based on the advertising media and messaging proposed by the aforementioned Proposal Department and proposes countermeasures. Based on the measures proposed by the aforementioned Risk Management Department, the Recommendation Department recommends artists who are suitable for the event's theme and target audience, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect past event data and market trends. The system according to feature 1.

3. The prediction unit, The data collected by the aforementioned data collection unit is analyzed to calculate the expected number of attendees for the next event. The system according to feature 1.

4. The optimization unit, We set the optimal ticket price based on the event's popularity and past ticket sales data. The system according to feature 1.

5. The aforementioned proposal section is, We select the most suitable advertising media and propose effective messages for specific target audiences. The system according to feature 1.

6. The aforementioned risk management department, Based on weather data and information on competing events, we propose measures to minimize risks. The system according to feature 1.

7. The aforementioned recommendation department, Select and recommend the most suitable artist for the event's concept and target audience. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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