system

Abstract

The system according to this embodiment aims to enable prospective buyers to easily obtain tickets. [Solution] The system according to the embodiment comprises a sales unit, an acquisition unit, a notification unit, a question unit, and an application unit. The sales unit provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. The acquisition unit acquires ticket information that the user may be interested in, the type of seat they are likely to purchase, and its price information from past browsing and purchase history acquired by the sales unit. The notification unit notifies the user of the expected odds of purchasing the ticket and the start date of sales based on the information acquired by the acquisition unit. The question unit asks the user about their purchase in a Yes / No question format. If Yes is selected by the question unit, the application unit automatically makes an application using a generating AI, following the normal application flow.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the ticket purchase process is complicated and it is difficult for a person who wishes to purchase a ticket to surely obtain the ticket.

[0005] The system according to the embodiment aims to enable a person who wishes to purchase a ticket to easily obtain the ticket.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sales unit, an acquisition unit, a notification unit, a question unit, and an application unit. The sales unit provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. The acquisition unit acquires ticket information that the user may be interested in, the type of seat they are likely to purchase, and its price information from past browsing and purchase history acquired by the sales unit. The notification unit notifies the user of the expected odds of purchasing the ticket and the start date of sales based on the information acquired by the acquisition unit. The question unit asks the user about their purchase in a Yes / No question format. If Yes is selected by the question unit, the application unit automatically submits the application using a generating AI, following the normal application flow. [Effects of the Invention]

[0007] The system according to this embodiment can make it easy for prospective buyers to obtain tickets. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The ticket sales and resale system according to an embodiment of the present invention is a system for solving problems related to the purchase and resale of tickets for live performances and sporting events. This system provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. This platform acquires ticket information that the user may be interested in, the type of seat they are likely to purchase, and its price information from their past browsing and purchase history, and notifies them of the expected odds of obtaining the ticket and the start date of sales. The user is asked a Yes / No question about purchasing. If the user selects Yes, a system is built that automatically applies for the ticket while following the normal application flow using a generation AI. Next, a platform is provided that restricts transactions at fair prices on the resale site. This platform uses image and character recognition AI to find the face value from the ticket information registered on the site, and uses generation AI to prevent prices from being set higher than that price plus a 10% commission. In addition, a function to detect fraudulent ticket sales and counterfeit ticket sales is also built and implemented using generation AI. This system enables event tickets to reach those in demand, and realizes a market movement that is satisfactory to organizers, participants, or applicants. This allows ticket sales and resale systems to ensure that event tickets reach those who need them, resulting in a market dynamic that is satisfactory to organizers, participants, and applicants alike.

[0029] The ticket sales and resale system according to the embodiment comprises a sales unit, an acquisition unit, a notification unit, a question unit, and an application unit. The sales unit provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. For example, the sales unit simplifies operation by providing an intuitive interface and minimizing the number of clicks. The sales unit can also display relevant ticket information based on the types and price ranges of tickets the user has previously purchased. The acquisition unit acquires ticket information that the user may be interested in, as well as seat types and their prices, from the past browsing and purchase history acquired by the sales unit. For example, the acquisition unit acquires relevant ticket information based on information about events the user has previously viewed. The acquisition unit can also analyze the user's purchase history and prioritize the acquisition of ticket information for specific artists or sports teams. The notification unit notifies the user of the expected multiplier and the start date of sales based on the information acquired by the acquisition unit. For example, the notification unit calculates the expected multiplier using past sales data and a demand forecasting model and notifies the user. The notification unit also notifies the user of the start date of sales, enabling the user to purchase tickets at the appropriate time. The questioning unit asks the user about their purchase using a Yes / No question format. For example, the questioning unit displays the user the question, "Do you want to buy this ticket?" and provides the option of Yes or No. The questioning unit can also guide the user to the next step based on their answer. If Yes is selected by the questioning unit, the application unit automatically completes the application process using a generation AI, following the normal application flow. For example, the application unit automatically inputs the user's information and completes the application procedure. The application unit can also use the generation AI to select the most suitable ticket based on the user's desired seat type and price range. As a result, the ticket sales and resale system according to this embodiment provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective, and improves user convenience by automating everything from ticket acquisition to application.

[0030] The sales department provides a personal sales platform with a user-friendly UI / UX designed from the buyer's perspective. For example, it simplifies operation by providing an intuitive interface and minimizing the number of clicks. Specifically, it uses visually clear icons and navigation menus to ensure users can navigate without confusion, even on their first visit. The sales department can also display relevant ticket information based on the types and price ranges of tickets a user has previously purchased, allowing them to easily find tickets that match their interests. Furthermore, the sales department analyzes user behavior history and displays personalized ticket recommendations. For example, users who frequently purchase tickets for a particular artist or sports team will be given priority in seeing new event information from that artist or team. The sales department also offers one-click purchase and auto-fill features to simplify the ticket purchase process, allowing users to quickly purchase tickets without cumbersome procedures. Finally, the sales department provides ticket management features, making it easy to manage purchase history, download tickets, and print them. In this way, the sales department provides a user-friendly and efficient ticket purchasing experience.

[0031] The acquisition unit retrieves ticket information likely to interest users, along with information on the types of seats they are likely to purchase and their prices, based on past browsing and purchase history acquired by the sales unit. For example, the acquisition unit retrieves relevant ticket information based on information about events the user has previously viewed. Specifically, it analyzes information such as the genre, artist, and sports team of events the user has previously viewed and prioritizes retrieving ticket information for similar events. The acquisition unit can also analyze the user's purchase history and prioritize retrieving ticket information for specific artists or sports teams. For example, if a user has purchased concert tickets for a particular artist multiple times in the past, it will prioritize retrieving and providing information about that artist's new concerts to the user. Furthermore, the acquisition unit can analyze the user's past purchase patterns and predict the types of seats and price ranges the user prefers. This allows for the efficient retrieval and provision of ticket information that is likely to interest the user. The acquisition unit updates this information in real time, providing users with the latest ticket information. The acquisition unit can also collaborate with external data sources to collect a wide range of ticket information. This allows the acquisition unit to provide optimal ticket information tailored to the user's interests and needs, thereby improving user satisfaction.

[0032] The notification unit notifies users of the expected ticket availability and sales start date based on the information acquired by the acquisition unit. For example, the notification unit calculates the expected availability using past sales data and demand forecasting models and notifies the user. Specifically, it analyzes past sales data to predict the sales rate at which tickets for a particular event or artist will sell out. It also uses demand forecasting models to calculate the expected availability, taking into account current market trends and user interest. This allows users to understand the difficulty of obtaining tickets in advance. Furthermore, the notification unit notifies users of the sales start date, enabling them to purchase tickets at the appropriate time. For example, it sends push notifications or email reminders as the sales start date for an event the user is interested in approaches. The notification unit also provides a function to automatically add the sales start date to the user's calendar app. This ensures that users do not forget the sales start date and can purchase tickets reliably. In addition, the notification unit can also provide real-time notifications of ticket sales status and inventory information. For example, it immediately notifies users when tickets are running low or when additional tickets are sold. This allows the notification system to provide users with timely and accurate information, helping them avoid missing out on ticket purchase opportunities.

[0033] The question section asks users about their purchase intentions using a Yes / No question format. For example, it might display a question like, "Do you want to buy this ticket?" and offer a Yes or No option. Specifically, when ticket information that the user is interested in is displayed, it confirms the user's intention to purchase through a pop-up window or notification message. The question section can also guide the user to the next step based on their answer. For example, if the user selects Yes, the next step is to proceed to a payment information input screen or a confirmation screen. On the other hand, if the user selects No, it displays other related ticket information or a confirmation message indicating that the purchase will be postponed. Furthermore, the question section can record the user's answer history and use it to predict future purchase intentions. For example, users who frequently answer Yes to a particular event or artist will be given priority in displaying similar event information. The question section can also provide personalized recommendations based on the user's answers. In this way, the question section can efficiently confirm the user's purchase intention and guide them to the appropriate next step, providing a smooth purchase experience.

[0034] If "Yes" is selected in the question section, the application section automatically completes the application process using a generating AI, following the normal application flow. For example, the application section automatically inputs user information and completes the application procedure. Specifically, it automatically retrieves personal and payment information previously entered by the user and inputs it into the necessary fields. The application section can also use the generating AI to select the most suitable ticket based on the user's desired seat type and price range. The generating AI analyzes the user's past purchase history and current interests to suggest the most suitable ticket. For example, if the user has previously preferred purchasing tickets in a specific price range, it will prioritize selecting tickets within that price range. The generating AI can also consider real-time inventory status to select the most suitable seat type. This allows users to purchase the most suitable ticket without any hassle. Furthermore, the application section displays the progress of the application procedure in real time, providing users with peace of mind. For example, it sends a confirmation message or email notification when the application is complete. The application section also has support functions to quickly respond in the event of any errors or problems. This allows the application process to provide users with a smooth and efficient application experience, significantly improving the convenience of ticket purchases.

[0035] The resale site includes a restriction section that limits transactions to fair prices. The restriction section uses image and character recognition AI to determine the face value from the ticket information registered on the site, and uses a generation AI to prevent prices from being set higher than that price plus a 10% commission. For example, the restriction section can analyze the ticket image to determine the face value. It can also read the ticket information using character recognition technology to confirm the face value. Furthermore, the restriction section uses a generation AI to prevent prices from being set higher than that price plus a 10% commission. For example, if the face value of a ticket is 5000 yen, prices higher than 5500 yen cannot be set. This ensures that transactions on the resale site are conducted at fair prices and prevents the sale of fraudulent or counterfeit tickets. Some or all of the above processing in the restriction section may be performed using AI, for example, or without AI. For example, the restriction section can input ticket image data into a generation AI and have the generation AI determine the face value.

[0036] The restriction unit uses image and character recognition AI to determine the face value from the ticket information registered on the site, and uses a generation AI to prevent the price from exceeding that amount plus a 10% commission. For example, the restriction unit analyzes the ticket image to determine the face value. It can also read the ticket information using character recognition technology to confirm the face value. Furthermore, the restriction unit uses a generation AI to prevent the price from exceeding that amount plus a 10% commission. For example, if the face value of a ticket is 5000 yen, the price cannot be set above 5500 yen. This ensures fair pricing by preventing the price from exceeding that amount plus a 10% commission. Some or all of the above processing in the restriction unit may be performed using AI, or not. For example, the restriction unit can input ticket image data into a generation AI and have the generation AI determine the face value.

[0037] The restriction unit incorporates a function built and implemented by generating AI to detect fraudulent and counterfeit ticket sales. For example, the restriction unit can analyze ticket images and text information to detect fraudulent tickets. It can also analyze ticket transaction history to detect abnormal transaction patterns. Furthermore, the restriction unit can monitor ticket sales information in real time and immediately detect fraudulent transactions. This helps maintain the health of the resale market by detecting fraudulent and counterfeit ticket sales. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input ticket image data into the generating AI and have the generating AI perform fraudulent ticket detection.

[0038] The sales department can analyze users' past purchase history and automatically generate optimal sales strategies. For example, the sales department can analyze the types and price ranges of tickets users have previously purchased and suggest similar events. Furthermore, the sales department can prioritize displaying tickets for specific artists or sports teams based on the user's purchase history. In addition, the sales department can implement promotions tailored to specific times of day or days of the week based on the user's purchase history. This allows for the automatic generation of optimal sales strategies by analyzing users' past purchase history, thereby improving sales efficiency. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department can input user purchase history data into a generating AI and have the AI ​​generate the optimal sales strategy.

[0039] The sales department can collect real-time user behavior data and instantly adjust sales strategies. For example, the sales department can analyze the pages a user is currently viewing and the links they have clicked and display relevant ticket information. Furthermore, if a user spends a long time viewing a particular event page, the sales department can display special offers for that event. Additionally, if a user is comparing multiple events, the sales department can prioritize displaying tickets for the event of greatest interest. This allows for instant adjustment of sales strategies and maximizes sales effectiveness by collecting real-time user behavior data. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department could input user behavior data into a generating AI and have the generating AI perform adjustments to the sales strategy.

[0040] The sales department can offer region-specific special offers by taking into account the user's geographical location. For example, if the user is in a specific region, the sales department can prioritize displaying tickets for events held in that region. Furthermore, if the user is traveling, the sales department can offer special offers for events held at their travel destination. Additionally, if the user is at home, the sales department can prioritize displaying tickets for nearby events. This allows for the provision of region-specific special offers by considering the user's geographical location, thereby improving sales effectiveness. Some or all of the above processing in the sales department may be performed using AI, for example, or without AI. For instance, the sales department could input the user's location data into a generating AI and have the generating AI generate special offers.

[0041] The sales department can analyze users' social media activity and provide relevant ticket information. For example, if a user posts about a particular artist or sports team, the sales department can prioritize displaying ticket information for that event. Furthermore, if a user uses hashtags related to an event, the sales department can provide ticket information for that event. Additionally, if a user plans to attend an event with friends, the sales department can provide group ticket information for that event. This allows the sales department to improve sales effectiveness by providing relevant ticket information through analysis of users' social media activity. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department could input user social media data into a generating AI and have the generating AI provide relevant ticket information.

[0042] The acquisition unit can analyze the user's past browsing history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring ticket information for events that the user has frequently viewed in the past. Furthermore, the acquisition unit can prioritize acquiring event information of a specific genre based on the user's browsing history. In addition, the acquisition unit can acquire ticket information tailored to a specific time period based on the user's browsing history. This allows the acquisition unit to select the optimal acquisition method by analyzing the user's past browsing history, thereby improving the efficiency of ticket information acquisition. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's browsing history data into a generating AI and have the generating AI select the optimal acquisition method.

[0043] The data acquisition unit can filter based on the user's current areas of interest. For example, the data acquisition unit can prioritize acquiring ticket information for artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the data acquisition unit can prioritize acquiring event information for that genre. Additionally, if the user is interested in a particular region, the data acquisition unit can prioritize acquiring ticket information for events held in that region. This allows the system to provide highly relevant ticket information by filtering based on the user's current areas of interest. Some or all of the processing described above in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0044] The acquisition unit can prioritize the acquisition of highly relevant ticket information by considering the user's geographical location. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring ticket information for events held in that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring ticket information for events held at their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize acquiring ticket information for nearby events. This improves the user experience by prioritizing the acquisition of highly relevant ticket information while considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's location data into a generating AI and have the generating AI acquire highly relevant ticket information.

[0045] The acquisition unit can analyze a user's social media activity and retrieve relevant ticket information. For example, if a user posts about a specific artist or sports team, the acquisition unit will prioritize retrieving that ticket information. Furthermore, if a user uses hashtags related to an event, the acquisition unit can retrieve ticket information for that event. Additionally, if a user plans to attend an event with friends, the acquisition unit can retrieve group ticket information for that event. This allows the acquisition of relevant ticket information by analyzing the user's social media activity, thereby improving the user experience. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI retrieve relevant ticket information.

[0046] The notification unit can adjust the level of detail in notifications based on the importance of the ticket. For example, the notification unit can provide detailed notifications for important ticket information, and concise notifications for general ticket information. Furthermore, it can provide immediate notifications for urgent ticket information. By adjusting the level of detail in notifications based on the importance of the ticket, the system appropriately provides users with important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ticket importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in notifications.

[0047] The notification unit can apply different notification algorithms depending on the ticket category. For example, in the case of concert tickets, the notification unit can provide visually appealing notifications. In the case of sporting event tickets, it can provide detailed notifications regarding the start time and location of the match. Furthermore, in the case of theater or musical tickets, it can provide notifications regarding cast information and performance dates. This improves the user experience by providing optimal notifications according to the ticket category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ticket category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0048] The notification unit can determine the priority of notifications based on the ticket sales start date. For example, the notification unit will prioritize notifications for tickets that are about to go on sale. The notification unit can also lower the priority of notifications for tickets that have been on sale for some time. Furthermore, the notification unit can prioritize notifications for tickets whose sales start date is approaching. In this way, by determining the priority of notifications based on the ticket sales start date, the system appropriately provides users with important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input sales start date data into a generating AI and have the generating AI perform the determination of notification priorities.

[0049] The notification unit can adjust the order of notifications based on the relevance of tickets. For example, the notification unit may prioritize notifications of tickets highly relevant to tickets the user has previously purchased. It can also prioritize notifications of tickets relevant to the user's current areas of interest. Furthermore, the notification unit may prioritize notifications of highly relevant tickets based on the user's geographical location. By adjusting the order of notifications based on the relevance of tickets, the system appropriately provides users with information that is important to them. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input ticket relevance data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0050] The questioning unit can analyze the user's past answer history and select the optimal questioning method. For example, the questioning unit can analyze patterns of questions the user has answered in the past and provide similar questions. Furthermore, the questioning unit can prioritize providing specific question formats (Yes / No, multiple-choice, etc.) based on the user's answer history. In addition, the questioning unit can provide questions tailored to specific times of day or situations based on the user's answer history. This allows the system to select the optimal questioning method by analyzing the user's past answer history, thereby improving the user experience. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the user's answer history data into a generating AI and have the generating AI select the optimal questioning method.

[0051] The questioning unit can customize the content of questions based on the user's current areas of interest. For example, the questioning unit can provide questions about artists or sports teams that the user is currently interested in. It can also provide questions related to a specific genre if the user is interested in that genre. Furthermore, if the user is interested in a specific region, the questioning unit can provide questions about events held in that region. This improves the user experience by customizing the questions based on the user's current areas of interest. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the questions.

[0052] The questioning unit can prioritize highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the questioning unit can provide questions about events taking place in that region. If the user is traveling, the questioning unit can provide questions about events taking place at their travel destination. Furthermore, if the user is at home, the questioning unit can provide questions about events in their neighborhood. By considering the user's geographical location, the questioning unit can prioritize highly relevant questions and improve the user experience. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's location data into a generating AI and have the generating AI perform the task of providing highly relevant questions.

[0053] The questioning unit can analyze a user's social media activity and ask relevant questions. For example, if a user posts about a specific artist or sports team, the questioning unit can provide questions about that artist or team. It can also provide questions about an event if a user uses a hashtag related to that event. Furthermore, if a user is planning to attend an event with friends, the questioning unit can provide questions about that event. This improves the user experience by analyzing the user's social media activity and asking relevant questions. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's social media data into a generating AI and have the generating AI provide relevant questions.

[0054] The application unit can analyze a user's past application history and select the most suitable application method. For example, the application unit can prioritize providing application methods that the user has used in the past (online, telephone, etc.). Furthermore, based on the user's application history, the application unit can provide application methods tailored to specific times of day or days of the week. In addition, based on the user's application history, the application unit can provide application methods tailored to specific events. This allows the application unit to analyze the user's past application history, select the most suitable application method, and improve the user experience. Some or all of the above processing in the application unit may be performed using AI, or not. For example, the application unit can input the user's application history data into a generating AI and have the generating AI select the most suitable application method.

[0055] The application section can customize application content based on the user's current areas of interest. For example, the application section can provide application content for events by artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the application section can provide application content for events in that genre. Additionally, if the user is interested in a particular region, the application section can provide application content for events held in that region. This improves the user experience by customizing application content based on the user's current areas of interest. Some or all of the above processing in the application section may be performed using AI, for example, or not. For example, the application section can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the application content.

[0056] The application unit can prioritize applications that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the application unit will prioritize applications for events held in that region. Similarly, if the user is traveling, the application unit can prioritize applications for events held at their travel destination. Furthermore, if the user is at home, the application unit can prioritize applications for nearby events. This improves the user experience by prioritizing highly relevant applications while considering the user's geographical location. Some or all of the above processing in the application unit may be performed using AI, or not. For example, the application unit can input the user's location data into a generating AI, which can then execute the process of selecting highly relevant applications.

[0057] The application unit can analyze a user's social media activity and make relevant applications. For example, if a user posts about a specific artist or sports team, the application unit will prioritize applications for that event. It can also apply for an event if the user uses a relevant hashtag. Furthermore, if a user plans to attend an event with friends, the application unit can make a group application for that event. This improves the user experience by making relevant applications through analysis of the user's social media activity. Some or all of the above processing in the application unit may be performed using AI, for example, or not. For example, the application unit can input the user's social media data into a generating AI and have the generating AI execute the relevant applications.

[0058] The price limiting unit can analyze the past transaction history of tickets and select the optimal price limiting method. For example, the price limiting unit can set a price limit based on the average price from the past transaction history of tickets. The price limiting unit can also analyze the transaction history of tickets and relax the price limit if demand is high. Furthermore, based on the transaction history of tickets, the price limiting unit can tighten the price limit if demand is low. In this way, by analyzing the past transaction history of tickets, the optimal price limiting method is selected, and transactions at a fair price are realized. Some or all of the above processing in the price limiting unit may be performed using AI, for example, or not using AI. For example, the price limiting unit can input the ticket transaction history data into a generating AI and have the generating AI select the optimal price limiting method.

[0059] The price limiting unit can apply different price limiting algorithms depending on the ticket category. For example, in the case of concert tickets, the price limiting unit can set a price limit based on demand. In the case of sporting event tickets, the price limiting unit can set a price limit based on the importance of the match. Furthermore, in the case of theater or musical tickets, the price limiting unit can set a price limit based on the cast and performance dates. This ensures fair pricing by applying the optimal price limit according to the ticket category. Some or all of the above processing in the price limiting unit may be performed using AI, for example, or without AI. For example, the price limiting unit can input ticket category data into a generating AI and have the generating AI execute the application of the price limiting algorithm.

[0060] The restriction unit can determine the priority of price restrictions based on the ticket sales start date. For example, the restriction unit can prioritize setting price restrictions for ticket information immediately before sales start. The restriction unit can also lower the priority of price restrictions for ticket information that has been on sale for some time. Furthermore, the restriction unit can prioritize setting price restrictions for ticket information whose sales start date is approaching. This ensures fair pricing by determining the priority of price restrictions based on the ticket sales start date. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input sales start date data into a generating AI and have the generating AI determine the priority of price restrictions.

[0061] The limiting unit can adjust the order of price limits based on the relevance of tickets. For example, the limiting unit can prioritize price limits for ticket information that is highly relevant to tickets the user has previously purchased. It can also prioritize price limits for ticket information that is relevant to the user's current areas of interest. Furthermore, the limiting unit can prioritize price limits for ticket information that is highly relevant based on the user's geographical location. By adjusting the order of price limits based on the relevance of tickets, it is possible to achieve transactions at a fair price. Some or all of the above processing in the limiting unit may be performed using AI, for example, or not using AI. For example, the limiting unit can input ticket relevance data into a generating AI and have the generating AI perform the adjustment of the order of price limits.

[0062] The restriction unit can incorporate a function built using generating AI to detect fraudulent and counterfeit ticket sales. For example, the restriction unit can analyze ticket images and text information to detect fraudulent tickets. It can also analyze ticket transaction history to detect abnormal transaction patterns. Furthermore, the restriction unit can monitor ticket sales information in real time and immediately detect fraudulent transactions. This helps maintain the health of the resale market by detecting fraudulent and counterfeit ticket sales. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input ticket image data into generating AI and have the generating AI perform fraudulent ticket detection.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The sales department can predict user purchasing behavior and dynamically adjust promotions based on that prediction. For example, if the sales department predicts that a user is likely to purchase tickets for a particular event, it can offer a special discount on that event. Furthermore, if a user is comparing multiple events, the sales department can prioritize displaying tickets for the event of greatest interest. In addition, the sales department can suggest tickets for relevant events based on the types and price ranges of tickets the user has previously purchased. This allows for the dynamic adjustment of promotions by predicting user purchasing behavior, thereby maximizing sales effectiveness.

[0065] The notification system can provide region-specific special offers by taking into account the user's geographical location. For example, if the user is in a specific region, the notification system can prioritize notifying them of tickets for events held in that region. Furthermore, if the user is traveling, the notification system can notify them of special offers for events held at their travel destination. Additionally, if the user is at home, the notification system can prioritize notifying them of tickets for nearby events. This improves notification effectiveness by providing region-specific special offers that take the user's geographical location into consideration.

[0066] The application department can analyze a user's past application history and select the most suitable application method. For example, it can prioritize providing application methods that the user has used in the past (online, telephone, etc.). Furthermore, based on the user's application history, the application department can provide application methods tailored to specific times of day or days of the week. In addition, based on the user's application history, the application department can provide application methods tailored to specific events. By analyzing a user's past application history, the system can select the most suitable application method and improve the user experience.

[0067] The sales department can analyze users' social media activity and provide relevant ticket information. For example, if a user posts about a specific artist or sports team, the sales department will prioritize displaying ticket information for that event. Furthermore, if a user uses hashtags related to an event, the sales department can provide ticket information for that event. Additionally, if a user plans to attend an event with friends, the sales department can provide group ticket information for that event. By analyzing users' social media activity, the sales department can provide relevant ticket information and improve sales effectiveness.

[0068] The data acquisition unit can filter based on the user's current areas of interest. For example, it can prioritize retrieving ticket information for artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the unit can prioritize retrieving event information for that genre. Additionally, if the user is interested in a particular region, the unit can prioritize retrieving ticket information for events held in that region. This allows the system to provide highly relevant ticket information by filtering based on the user's current areas of interest.

[0069] The questioning function can analyze a user's past answer history and select the most appropriate question format. For example, it can analyze patterns in questions a user has answered in the past and provide similar questions. It can also prioritize specific question formats (Yes / No, multiple-choice, etc.) based on the user's answer history. Furthermore, it can provide questions tailored to specific times of day or situations based on the user's answer history. By analyzing a user's past answer history, it can select the most appropriate question format and improve the user experience.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The sales department will provide a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. For example, it will provide an intuitive interface and simplify operation by minimizing the number of clicks. It will also display relevant ticket information based on the types and price ranges of tickets the user has previously purchased. Step 2: The acquisition unit retrieves ticket information that the user might be interested in, as well as seat types and their prices, based on past browsing and purchase history acquired by the sales unit. For example, it retrieves relevant ticket information based on information about events the user has previously viewed. It also analyzes the user's purchase history to prioritize the acquisition of ticket information for specific artists or sports teams. Step 3: The notification unit notifies the user of the expected multiplier and sales start date for the ticket based on the information acquired by the acquisition unit. For example, it calculates the expected multiplier using past sales data and demand forecasting models and notifies the user. It also notifies the user of the sales start date so that the user can purchase the ticket at the appropriate time. Step 4: The question section asks the user about their purchase using a Yes / No question format. For example, it might ask the user, "Do you want to purchase this ticket?" and provide the option to choose Yes or No. It also guides the user to the next step based on their answer. Step 5: If "Yes" is selected in the question section, the application section will automatically complete the application process using a generating AI, following the normal application flow. For example, it will automatically input the user's information and complete the application procedure. It will also use the generating AI to select the most suitable ticket based on the user's desired seat type and price range.

[0072] (Example of form 2) The ticket sales and resale system according to an embodiment of the present invention is a system for solving problems related to the purchase and resale of tickets for live performances and sporting events. This system provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. This platform acquires ticket information that the user may be interested in, the type of seat they are likely to purchase, and its price information from their past browsing and purchase history, and notifies them of the expected odds of obtaining the ticket and the start date of sales. The user is asked a Yes / No question about purchasing. If the user selects Yes, a system is built that automatically applies for the ticket while following the normal application flow using a generation AI. Next, a platform is provided that restricts transactions at fair prices on the resale site. This platform uses image and character recognition AI to find the face value from the ticket information registered on the site, and uses generation AI to prevent prices from being set higher than that price plus a 10% commission. In addition, a function to detect fraudulent ticket sales and counterfeit ticket sales is also built and implemented using generation AI. This system enables event tickets to reach those in demand, and realizes a market movement that is satisfactory to organizers, participants, or applicants. This allows ticket sales and resale systems to ensure that event tickets reach those who need them, resulting in a market dynamic that is satisfactory to organizers, participants, and applicants alike.

[0073] The ticket sales and resale system according to the embodiment comprises a sales unit, an acquisition unit, a notification unit, a question unit, and an application unit. The sales unit provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. For example, the sales unit simplifies operation by providing an intuitive interface and minimizing the number of clicks. The sales unit can also display relevant ticket information based on the types and price ranges of tickets the user has previously purchased. The acquisition unit acquires ticket information that the user may be interested in, as well as seat types and their prices, from the past browsing and purchase history acquired by the sales unit. For example, the acquisition unit acquires relevant ticket information based on information about events the user has previously viewed. The acquisition unit can also analyze the user's purchase history and prioritize the acquisition of ticket information for specific artists or sports teams. The notification unit notifies the user of the expected multiplier and the start date of sales based on the information acquired by the acquisition unit. For example, the notification unit calculates the expected multiplier using past sales data and a demand forecasting model and notifies the user. The notification unit also notifies the user of the start date of sales, enabling the user to purchase tickets at the appropriate time. The questioning unit asks the user about their purchase using a Yes / No question format. For example, the questioning unit displays the user the question, "Do you want to buy this ticket?" and provides the option of Yes or No. The questioning unit can also guide the user to the next step based on their answer. If Yes is selected by the questioning unit, the application unit automatically completes the application process using a generation AI, following the normal application flow. For example, the application unit automatically inputs the user's information and completes the application procedure. The application unit can also use the generation AI to select the most suitable ticket based on the user's desired seat type and price range. As a result, the ticket sales and resale system according to this embodiment provides a personal sales platform with an easy-to-use UI / UX from the buyer's perspective, and improves user convenience by automating everything from ticket acquisition to application.

[0074] The sales department provides a personal sales platform with a user-friendly UI / UX designed from the buyer's perspective. For example, it simplifies operation by providing an intuitive interface and minimizing the number of clicks. Specifically, it uses visually clear icons and navigation menus to ensure users can navigate without confusion, even on their first visit. The sales department can also display relevant ticket information based on the types and price ranges of tickets a user has previously purchased, allowing them to easily find tickets that match their interests. Furthermore, the sales department analyzes user behavior history and displays personalized ticket recommendations. For example, users who frequently purchase tickets for a particular artist or sports team will be given priority in seeing new event information from that artist or team. The sales department also offers one-click purchase and auto-fill features to simplify the ticket purchase process, allowing users to quickly purchase tickets without cumbersome procedures. Finally, the sales department provides ticket management features, making it easy to manage purchase history, download tickets, and print them. In this way, the sales department provides a user-friendly and efficient ticket purchasing experience.

[0075] The acquisition unit retrieves ticket information likely to interest users, along with information on the types of seats they are likely to purchase and their prices, based on past browsing and purchase history acquired by the sales unit. For example, the acquisition unit retrieves relevant ticket information based on information about events the user has previously viewed. Specifically, it analyzes information such as the genre, artist, and sports team of events the user has previously viewed and prioritizes retrieving ticket information for similar events. The acquisition unit can also analyze the user's purchase history and prioritize retrieving ticket information for specific artists or sports teams. For example, if a user has purchased concert tickets for a particular artist multiple times in the past, it will prioritize retrieving and providing information about that artist's new concerts to the user. Furthermore, the acquisition unit can analyze the user's past purchase patterns and predict the types of seats and price ranges the user prefers. This allows for the efficient retrieval and provision of ticket information that is likely to interest the user. The acquisition unit updates this information in real time, providing users with the latest ticket information. The acquisition unit can also collaborate with external data sources to collect a wide range of ticket information. This allows the acquisition unit to provide optimal ticket information tailored to the user's interests and needs, thereby improving user satisfaction.

[0076] The notification unit notifies users of the expected ticket availability and sales start date based on the information acquired by the acquisition unit. For example, the notification unit calculates the expected availability using past sales data and demand forecasting models and notifies the user. Specifically, it analyzes past sales data to predict the sales rate at which tickets for a particular event or artist will sell out. It also uses demand forecasting models to calculate the expected availability, taking into account current market trends and user interest. This allows users to understand the difficulty of obtaining tickets in advance. Furthermore, the notification unit notifies users of the sales start date, enabling them to purchase tickets at the appropriate time. For example, it sends push notifications or email reminders as the sales start date for an event the user is interested in approaches. The notification unit also provides a function to automatically add the sales start date to the user's calendar app. This ensures that users do not forget the sales start date and can purchase tickets reliably. In addition, the notification unit can also provide real-time notifications of ticket sales status and inventory information. For example, it immediately notifies users when tickets are running low or when additional tickets are sold. This allows the notification system to provide users with timely and accurate information, helping them avoid missing out on ticket purchase opportunities.

[0077] The question section asks users about their purchase intentions using a Yes / No question format. For example, it might display a question like, "Do you want to buy this ticket?" and offer a Yes or No option. Specifically, when ticket information that the user is interested in is displayed, it confirms the user's intention to purchase through a pop-up window or notification message. The question section can also guide the user to the next step based on their answer. For example, if the user selects Yes, the next step is to proceed to a payment information input screen or a confirmation screen. On the other hand, if the user selects No, it displays other related ticket information or a confirmation message indicating that the purchase will be postponed. Furthermore, the question section can record the user's answer history and use it to predict future purchase intentions. For example, users who frequently answer Yes to a particular event or artist will be given priority in displaying similar event information. The question section can also provide personalized recommendations based on the user's answers. In this way, the question section can efficiently confirm the user's purchase intention and guide them to the appropriate next step, providing a smooth purchase experience.

[0078] If "Yes" is selected in the question section, the application section automatically completes the application process using a generating AI, following the normal application flow. For example, the application section automatically inputs user information and completes the application procedure. Specifically, it automatically retrieves personal and payment information previously entered by the user and inputs it into the necessary fields. The application section can also use the generating AI to select the most suitable ticket based on the user's desired seat type and price range. The generating AI analyzes the user's past purchase history and current interests to suggest the most suitable ticket. For example, if the user has previously preferred purchasing tickets in a specific price range, it will prioritize selecting tickets within that price range. The generating AI can also consider real-time inventory status to select the most suitable seat type. This allows users to purchase the most suitable ticket without any hassle. Furthermore, the application section displays the progress of the application procedure in real time, providing users with peace of mind. For example, it sends a confirmation message or email notification when the application is complete. The application section also has support functions to quickly respond in the event of any errors or problems. This allows the application process to provide users with a smooth and efficient application experience, significantly improving the convenience of ticket purchases.

[0079] The resale site includes a restriction section that limits transactions to fair prices. The restriction section uses image and character recognition AI to determine the face value from the ticket information registered on the site, and uses a generation AI to prevent prices from being set higher than that price plus a 10% commission. For example, the restriction section can analyze the ticket image to determine the face value. It can also read the ticket information using character recognition technology to confirm the face value. Furthermore, the restriction section uses a generation AI to prevent prices from being set higher than that price plus a 10% commission. For example, if the face value of a ticket is 5000 yen, prices higher than 5500 yen cannot be set. This ensures that transactions on the resale site are conducted at fair prices and prevents the sale of fraudulent or counterfeit tickets. Some or all of the above processing in the restriction section may be performed using AI, for example, or without AI. For example, the restriction section can input ticket image data into a generation AI and have the generation AI determine the face value.

[0080] The restriction unit uses image and character recognition AI to determine the face value from the ticket information registered on the site, and uses a generation AI to prevent the price from exceeding that amount plus a 10% commission. For example, the restriction unit analyzes the ticket image to determine the face value. It can also read the ticket information using character recognition technology to confirm the face value. Furthermore, the restriction unit uses a generation AI to prevent the price from exceeding that amount plus a 10% commission. For example, if the face value of a ticket is 5000 yen, the price cannot be set above 5500 yen. This ensures fair pricing by preventing the price from exceeding that amount plus a 10% commission. Some or all of the above processing in the restriction unit may be performed using AI, or not. For example, the restriction unit can input ticket image data into a generation AI and have the generation AI determine the face value.

[0081] The restriction unit incorporates a function built and implemented by generating AI to detect fraudulent and counterfeit ticket sales. For example, the restriction unit can analyze ticket images and text information to detect fraudulent tickets. It can also analyze ticket transaction history to detect abnormal transaction patterns. Furthermore, the restriction unit can monitor ticket sales information in real time and immediately detect fraudulent transactions. This helps maintain the health of the resale market by detecting fraudulent and counterfeit ticket sales. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input ticket image data into the generating AI and have the generating AI perform fraudulent ticket detection.

[0082] The sales department can estimate the user's emotions and dynamically change the UI / UX design based on the estimated emotions. For example, if the user is excited, the sales department can design the UI using bright colors and dynamic animations. If the user is stressed, the sales department can provide a simple UI with calming colors and simplify operation. Furthermore, if the user is relaxed, the sales department can provide a UI with more detailed information and more customizable options. This improves the user experience by dynamically changing the UI / UX design according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sales department may be performed using AI or not. For example, the sales department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The sales department can analyze users' past purchase history and automatically generate optimal sales strategies. For example, the sales department can analyze the types and price ranges of tickets users have previously purchased and suggest similar events. Furthermore, the sales department can prioritize displaying tickets for specific artists or sports teams based on the user's purchase history. In addition, the sales department can implement promotions tailored to specific times of day or days of the week based on the user's purchase history. This allows for the automatic generation of optimal sales strategies by analyzing users' past purchase history, thereby improving sales efficiency. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department can input user purchase history data into a generating AI and have the AI ​​generate the optimal sales strategy.

[0084] The sales department can collect real-time user behavior data and instantly adjust sales strategies. For example, the sales department can analyze the pages a user is currently viewing and the links they have clicked and display relevant ticket information. Furthermore, if a user spends a long time viewing a particular event page, the sales department can display special offers for that event. Additionally, if a user is comparing multiple events, the sales department can prioritize displaying tickets for the event of greatest interest. This allows for instant adjustment of sales strategies and maximizes sales effectiveness by collecting real-time user behavior data. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department could input user behavior data into a generating AI and have the generating AI perform adjustments to the sales strategy.

[0085] The sales department can estimate the user's emotions and customize the layout of the sales page based on those emotions. For example, if the user is excited, the sales department can provide a visually stimulating layout. If the user is stressed, the sales department can provide a simple and highly visible layout. Furthermore, if the user is relaxed, the sales department can provide a layout that includes detailed information. This improves the user experience by customizing the sales page layout according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sales department may be performed using AI or not. For example, the sales department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The sales department can offer region-specific special offers by taking into account the user's geographical location. For example, if the user is in a specific region, the sales department can prioritize displaying tickets for events held in that region. Furthermore, if the user is traveling, the sales department can offer special offers for events held at their travel destination. Additionally, if the user is at home, the sales department can prioritize displaying tickets for nearby events. This allows for the provision of region-specific special offers by considering the user's geographical location, thereby improving sales effectiveness. Some or all of the above processing in the sales department may be performed using AI, for example, or without AI. For instance, the sales department could input the user's location data into a generating AI and have the generating AI generate special offers.

[0087] The sales department can analyze users' social media activity and provide relevant ticket information. For example, if a user posts about a particular artist or sports team, the sales department can prioritize displaying ticket information for that event. Furthermore, if a user uses hashtags related to an event, the sales department can provide ticket information for that event. Additionally, if a user plans to attend an event with friends, the sales department can provide group ticket information for that event. This allows the sales department to improve sales effectiveness by providing relevant ticket information through analysis of users' social media activity. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For instance, the sales department could input user social media data into a generating AI and have the generating AI provide relevant ticket information.

[0088] The acquisition unit can estimate the user's emotions and adjust the timing of ticket information acquisition based on the estimated emotions. For example, if the user is excited, the acquisition unit can immediately acquire and provide ticket information. If the user is relaxed, the acquisition unit can provide ticket information at an appropriate time. Furthermore, if the user is stressed, the acquisition unit can reduce the frequency of information provision and provide only essential information. This improves the user experience by adjusting the timing of ticket information acquisition according to 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The acquisition unit can analyze the user's past browsing history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring ticket information for events that the user has frequently viewed in the past. Furthermore, the acquisition unit can prioritize acquiring event information of a specific genre based on the user's browsing history. In addition, the acquisition unit can acquire ticket information tailored to a specific time period based on the user's browsing history. This allows the acquisition unit to select the optimal acquisition method by analyzing the user's past browsing history, thereby improving the efficiency of ticket information acquisition. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's browsing history data into a generating AI and have the generating AI select the optimal acquisition method.

[0090] The data acquisition unit can filter based on the user's current areas of interest. For example, the data acquisition unit can prioritize acquiring ticket information for artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the data acquisition unit can prioritize acquiring event information for that genre. Additionally, if the user is interested in a particular region, the data acquisition unit can prioritize acquiring ticket information for events held in that region. This allows the system to provide highly relevant ticket information by filtering based on the user's current areas of interest. Some or all of the processing described above in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0091] The acquisition unit can estimate the user's emotions and determine the priority of ticket information to acquire based on the estimated emotions. For example, if the user is excited, the acquisition unit will prioritize acquiring the most popular ticket information. If the user is relaxed, the acquisition unit can acquire a balanced selection of ticket information of interest. Furthermore, if the user is stressed, the acquisition unit can prioritize acquiring only important ticket information. This improves the user experience by prioritizing the ticket information to acquire according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The acquisition unit can prioritize the acquisition of highly relevant ticket information by considering the user's geographical location. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring ticket information for events held in that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring ticket information for events held at their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize acquiring ticket information for nearby events. This improves the user experience by prioritizing the acquisition of highly relevant ticket information while considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's location data into a generating AI and have the generating AI acquire highly relevant ticket information.

[0093] The acquisition unit can analyze a user's social media activity and retrieve relevant ticket information. For example, if a user posts about a specific artist or sports team, the acquisition unit will prioritize retrieving that ticket information. Furthermore, if a user uses hashtags related to an event, the acquisition unit can retrieve ticket information for that event. Additionally, if a user plans to attend an event with friends, the acquisition unit can retrieve group ticket information for that event. This allows the acquisition of relevant ticket information by analyzing the user's social media activity, thereby improving the user experience. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI retrieve relevant ticket information.

[0094] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the user is excited, the notification unit can provide a visually stimulating notification. If the user is stressed, the notification unit can provide a simple and easily visible notification. Furthermore, if the user is relaxed, the notification unit can provide a notification containing detailed information. This improves the user experience by adjusting the way notifications are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The notification unit can adjust the level of detail in notifications based on the importance of the ticket. For example, the notification unit can provide detailed notifications for important ticket information, and concise notifications for general ticket information. Furthermore, it can provide immediate notifications for urgent ticket information. By adjusting the level of detail in notifications based on the importance of the ticket, the system appropriately provides users with important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ticket importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in notifications.

[0096] The notification unit can apply different notification algorithms depending on the ticket category. For example, in the case of concert tickets, the notification unit can provide visually appealing notifications. In the case of sporting event tickets, it can provide detailed notifications regarding the start time and location of the match. Furthermore, in the case of theater or musical tickets, it can provide notifications regarding cast information and performance dates. This improves the user experience by providing optimal notifications according to the ticket category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ticket category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0097] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is excited, the notification unit can provide an immediate notification. If the user is relaxed, the notification unit can provide a notification at an appropriate time. Furthermore, if the user is stressed, the notification unit can reduce the frequency of notifications and provide only important information. This improves the user experience by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The notification unit can determine the priority of notifications based on the ticket sales start date. For example, the notification unit will prioritize notifications for tickets that are about to go on sale. The notification unit can also lower the priority of notifications for tickets that have been on sale for some time. Furthermore, the notification unit can prioritize notifications for tickets whose sales start date is approaching. In this way, by determining the priority of notifications based on the ticket sales start date, the system appropriately provides users with important information. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input sales start date data into a generating AI and have the generating AI perform the determination of notification priorities.

[0099] The notification unit can adjust the order of notifications based on the relevance of tickets. For example, the notification unit may prioritize notifications of tickets highly relevant to tickets the user has previously purchased. It can also prioritize notifications of tickets relevant to the user's current areas of interest. Furthermore, the notification unit may prioritize notifications of highly relevant tickets based on the user's geographical location. By adjusting the order of notifications based on the relevance of tickets, the system appropriately provides users with information that is important to them. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input ticket relevance data into a generating AI and have the generating AI perform the adjustment of the notification order.

[0100] The questioning unit can estimate the user's emotions and adjust the wording of the questions based on the estimated emotions. For example, if the user is excited, the questioning unit can provide visually stimulating questions. If the user is stressed, the questioning unit can provide simple and highly visible questions. Furthermore, if the user is relaxed, the questioning unit can provide questions containing detailed information. This improves the user experience by adjusting the wording of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using AI or not using AI. For example, the questioning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The questioning unit can analyze the user's past answer history and select the optimal questioning method. For example, the questioning unit can analyze patterns of questions the user has answered in the past and provide similar questions. Furthermore, the questioning unit can prioritize providing specific question formats (Yes / No, multiple-choice, etc.) based on the user's answer history. In addition, the questioning unit can provide questions tailored to specific times of day or situations based on the user's answer history. This allows the system to select the optimal questioning method by analyzing the user's past answer history, thereby improving the user experience. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the user's answer history data into a generating AI and have the generating AI select the optimal questioning method.

[0102] The questioning unit can customize the content of questions based on the user's current areas of interest. For example, the questioning unit can provide questions about artists or sports teams that the user is currently interested in. It can also provide questions related to a specific genre if the user is interested in that genre. Furthermore, if the user is interested in a specific region, the questioning unit can provide questions about events held in that region. This improves the user experience by customizing the questions based on the user's current areas of interest. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the questions.

[0103] The questioning unit can estimate the user's emotions and adjust the timing of questions based on the estimated emotions. For example, if the user is excited, the questioning unit can provide questions immediately. If the user is relaxed, the questioning unit can provide questions at an appropriate time. Furthermore, if the user is stressed, the questioning unit can reduce the frequency of questions and provide only important questions. This improves the user experience by adjusting the timing of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using AI or not using AI. For example, the questioning unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The questioning unit can prioritize highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the questioning unit can provide questions about events taking place in that region. If the user is traveling, the questioning unit can provide questions about events taking place at their travel destination. Furthermore, if the user is at home, the questioning unit can provide questions about events in their neighborhood. By considering the user's geographical location, the questioning unit can prioritize highly relevant questions and improve the user experience. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's location data into a generating AI and have the generating AI perform the task of providing highly relevant questions.

[0105] The questioning unit can analyze a user's social media activity and ask relevant questions. For example, if a user posts about a specific artist or sports team, the questioning unit can provide questions about that artist or team. It can also provide questions about an event if a user uses a hashtag related to that event. Furthermore, if a user is planning to attend an event with friends, the questioning unit can provide questions about that event. This improves the user experience by analyzing the user's social media activity and asking relevant questions. Some or all of the processing described above in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can input the user's social media data into a generating AI and have the generating AI provide relevant questions.

[0106] The application unit can estimate the user's emotions and adjust the application procedure based on the estimated emotions. For example, if the user is excited, the application unit can provide a quick and concise application procedure. If the user is relaxed, the application unit can provide an application procedure that includes detailed explanations. Furthermore, if the user is stressed, the application unit can provide a simple and highly visible application procedure. This improves the user experience by adjusting the application procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The application unit can analyze a user's past application history and select the most suitable application method. For example, the application unit can prioritize providing application methods that the user has used in the past (online, telephone, etc.). Furthermore, based on the user's application history, the application unit can provide application methods tailored to specific times of day or days of the week. In addition, based on the user's application history, the application unit can provide application methods tailored to specific events. This allows the application unit to analyze the user's past application history, select the most suitable application method, and improve the user experience. Some or all of the above processing in the application unit may be performed using AI, or not. For example, the application unit can input the user's application history data into a generating AI and have the generating AI select the most suitable application method.

[0108] The application section can customize application content based on the user's current areas of interest. For example, the application section can provide application content for events by artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the application section can provide application content for events in that genre. Additionally, if the user is interested in a particular region, the application section can provide application content for events held in that region. This improves the user experience by customizing application content based on the user's current areas of interest. Some or all of the above processing in the application section may be performed using AI, for example, or not. For example, the application section can input the user's areas of interest data into a generating AI and have the generating AI perform the customization of the application content.

[0109] The application unit can estimate the user's emotions and adjust the timing of applications based on the estimated emotions. For example, if the user is excited, the application unit can make an application immediately. If the user is relaxed, the application unit can make an application at an appropriate time. Furthermore, if the user is stressed, the application unit can reduce the frequency of applications and only make important applications. This improves the user experience by adjusting the timing of applications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI or not using AI. For example, the application unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The application unit can prioritize applications that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the application unit will prioritize applications for events held in that region. Similarly, if the user is traveling, the application unit can prioritize applications for events held at their travel destination. Furthermore, if the user is at home, the application unit can prioritize applications for nearby events. This improves the user experience by prioritizing highly relevant applications while considering the user's geographical location. Some or all of the above processing in the application unit may be performed using AI, or not. For example, the application unit can input the user's location data into a generating AI, which can then execute the process of selecting highly relevant applications.

[0111] The application unit can analyze a user's social media activity and make relevant applications. For example, if a user posts about a specific artist or sports team, the application unit will prioritize applications for that event. It can also apply for an event if the user uses a relevant hashtag. Furthermore, if a user plans to attend an event with friends, the application unit can make a group application for that event. This improves the user experience by making relevant applications through analysis of the user's social media activity. Some or all of the above processing in the application unit may be performed using AI, for example, or not. For example, the application unit can input the user's social media data into a generating AI and have the generating AI execute the relevant applications.

[0112] The limiting unit can estimate the user's emotions and adjust the price limit criteria based on the estimated user emotions. For example, if the user is excited, the limiting unit can relax the price limit and allow trading at a higher price. It can also set an appropriate price limit if the user is relaxed. Furthermore, if the user is stressed, the limiting unit can tighten the price limit and prioritize trading at a lower price. This improves the user experience by adjusting the price limit criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the limiting unit may be performed using AI or not. For example, the limiting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The price limiting unit can analyze the past transaction history of tickets and select the optimal price limiting method. For example, the price limiting unit can set a price limit based on the average price from the past transaction history of tickets. The price limiting unit can also analyze the transaction history of tickets and relax the price limit if demand is high. Furthermore, based on the transaction history of tickets, the price limiting unit can tighten the price limit if demand is low. In this way, by analyzing the past transaction history of tickets, the optimal price limiting method is selected, and transactions at a fair price are realized. Some or all of the above processing in the price limiting unit may be performed using AI, for example, or not using AI. For example, the price limiting unit can input the ticket transaction history data into a generating AI and have the generating AI select the optimal price limiting method.

[0114] The price limiting unit can apply different price limiting algorithms depending on the ticket category. For example, in the case of concert tickets, the price limiting unit can set a price limit based on demand. In the case of sporting event tickets, the price limiting unit can set a price limit based on the importance of the match. Furthermore, in the case of theater or musical tickets, the price limiting unit can set a price limit based on the cast and performance dates. This ensures fair pricing by applying the optimal price limit according to the ticket category. Some or all of the above processing in the price limiting unit may be performed using AI, for example, or without AI. For example, the price limiting unit can input ticket category data into a generating AI and have the generating AI execute the application of the price limiting algorithm.

[0115] The limiting unit can estimate the user's emotions and adjust the timing of price limits based on the estimated emotions. For example, if the user is excited, the limiting unit can set a price limit immediately. If the user is relaxed, the limiting unit can set a price limit at an appropriate time. Furthermore, if the user is stressed, the limiting unit can reduce the frequency of price limits and limit only important trades. This improves the user experience by adjusting the timing of price limits according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the limiting unit may be performed using AI or not using AI. For example, the limiting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The restriction unit can determine the priority of price restrictions based on the ticket sales start date. For example, the restriction unit can prioritize setting price restrictions for ticket information immediately before sales start. The restriction unit can also lower the priority of price restrictions for ticket information that has been on sale for some time. Furthermore, the restriction unit can prioritize setting price restrictions for ticket information whose sales start date is approaching. This ensures fair pricing by determining the priority of price restrictions based on the ticket sales start date. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input sales start date data into a generating AI and have the generating AI determine the priority of price restrictions.

[0117] The limiting unit can adjust the order of price limits based on the relevance of tickets. For example, the limiting unit can prioritize price limits for ticket information that is highly relevant to tickets the user has previously purchased. It can also prioritize price limits for ticket information that is relevant to the user's current areas of interest. Furthermore, the limiting unit can prioritize price limits for ticket information that is highly relevant based on the user's geographical location. By adjusting the order of price limits based on the relevance of tickets, it is possible to achieve transactions at a fair price. Some or all of the above processing in the limiting unit may be performed using AI, for example, or not using AI. For example, the limiting unit can input ticket relevance data into a generating AI and have the generating AI perform the adjustment of the order of price limits.

[0118] The restriction unit can incorporate a function built using generating AI to detect fraudulent and counterfeit ticket sales. For example, the restriction unit can analyze ticket images and text information to detect fraudulent tickets. It can also analyze ticket transaction history to detect abnormal transaction patterns. Furthermore, the restriction unit can monitor ticket sales information in real time and immediately detect fraudulent transactions. This helps maintain the health of the resale market by detecting fraudulent and counterfeit ticket sales. Some or all of the above processing in the restriction unit may be performed using AI, for example, or without AI. For example, the restriction unit can input ticket image data into generating AI and have the generating AI perform fraudulent ticket detection.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The sales department can predict user purchasing behavior and dynamically adjust promotions based on that prediction. For example, if the sales department predicts that a user is likely to purchase tickets for a particular event, it can offer a special discount on that event. Furthermore, if a user is comparing multiple events, the sales department can prioritize displaying tickets for the event of greatest interest. In addition, the sales department can suggest tickets for relevant events based on the types and price ranges of tickets the user has previously purchased. This allows for the dynamic adjustment of promotions by predicting user purchasing behavior, thereby maximizing sales effectiveness.

[0121] The data acquisition unit can estimate the user's emotions and adjust how ticket information is acquired based on those emotions. For example, if the user is excited, the unit can acquire and provide ticket information immediately. If the user is relaxed, the unit can provide ticket information at an appropriate time. Furthermore, if the user is stressed, the unit can reduce the frequency of information provision and provide only essential information. This improves the user experience by adjusting how ticket information is acquired according to the user's emotions.

[0122] The notification system can provide region-specific special offers by taking into account the user's geographical location. For example, if the user is in a specific region, the notification system can prioritize notifying them of tickets for events held in that region. Furthermore, if the user is traveling, the notification system can notify them of special offers for events held at their travel destination. Additionally, if the user is at home, the notification system can prioritize notifying them of tickets for nearby events. This improves notification effectiveness by providing region-specific special offers that take the user's geographical location into consideration.

[0123] The questioning function can estimate the user's emotions and adjust the wording of the questions based on those emotions. For example, if the user is excited, the questioning function can provide visually stimulating questions. If the user is stressed, it can provide simple, highly visible questions. Furthermore, if the user is relaxed, it can provide questions containing detailed information. This improves the user experience by adjusting the wording of questions according to the user's emotions.

[0124] The application department can analyze a user's past application history and select the most suitable application method. For example, it can prioritize providing application methods that the user has used in the past (online, telephone, etc.). Furthermore, based on the user's application history, the application department can provide application methods tailored to specific times of day or days of the week. In addition, based on the user's application history, the application department can provide application methods tailored to specific events. By analyzing a user's past application history, the system can select the most suitable application method and improve the user experience.

[0125] The price limiting unit can estimate the user's emotions and adjust the price limit criteria based on those emotions. For example, if the user is excited, the unit can relax the price limit and allow trading at a higher price. If the user is relaxed, the unit can set an appropriate price limit. Furthermore, if the user is stressed, the unit can tighten the price limit and prioritize trading at a lower price. This improves the user experience by adjusting the price limit criteria according to the user's emotions.

[0126] The sales department can analyze users' social media activity and provide relevant ticket information. For example, if a user posts about a specific artist or sports team, the sales department will prioritize displaying ticket information for that event. Furthermore, if a user uses hashtags related to an event, the sales department can provide ticket information for that event. Additionally, if a user plans to attend an event with friends, the sales department can provide group ticket information for that event. By analyzing users' social media activity, the sales department can provide relevant ticket information and improve sales effectiveness.

[0127] The data acquisition unit can filter based on the user's current areas of interest. For example, it can prioritize retrieving ticket information for artists or sports teams that the user is currently interested in. Furthermore, if the user is interested in a particular genre, the unit can prioritize retrieving event information for that genre. Additionally, if the user is interested in a particular region, the unit can prioritize retrieving ticket information for events held in that region. This allows the system to provide highly relevant ticket information by filtering based on the user's current areas of interest.

[0128] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is excited, the notification unit can provide a visually stimulating notification. If the user is stressed, it can provide a simple and easily visible notification. Furthermore, if the user is relaxed, it can provide a notification containing detailed information. This improves the user experience by adjusting the way notifications are presented according to the user's emotions.

[0129] The questioning function can analyze a user's past answer history and select the most appropriate question format. For example, it can analyze patterns in questions a user has answered in the past and provide similar questions. It can also prioritize specific question formats (Yes / No, multiple-choice, etc.) based on the user's answer history. Furthermore, it can provide questions tailored to specific times of day or situations based on the user's answer history. By analyzing a user's past answer history, it can select the most appropriate question format and improve the user experience.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The sales department will provide a personal sales platform with an easy-to-use UI / UX from the buyer's perspective. For example, it will provide an intuitive interface and simplify operation by minimizing the number of clicks. It will also display relevant ticket information based on the types and price ranges of tickets the user has previously purchased. Step 2: The acquisition unit retrieves ticket information that the user might be interested in, as well as seat types and their prices, based on past browsing and purchase history acquired by the sales unit. For example, it retrieves relevant ticket information based on information about events the user has previously viewed. It also analyzes the user's purchase history to prioritize the acquisition of ticket information for specific artists or sports teams. Step 3: The notification unit notifies the user of the expected multiplier and sales start date for the ticket based on the information acquired by the acquisition unit. For example, it calculates the expected multiplier using past sales data and demand forecasting models and notifies the user. It also notifies the user of the sales start date so that the user can purchase the ticket at the appropriate time. Step 4: The question section asks the user about their purchase using a Yes / No question format. For example, it might ask the user, "Do you want to purchase this ticket?" and provide the option to choose Yes or No. It also guides the user to the next step based on their answer. Step 5: If "Yes" is selected in the question section, the application section will automatically complete the application process using a generating AI, following the normal application flow. For example, it will automatically input the user's information and complete the application procedure. It will also use the generating AI to select the most suitable ticket based on the user's desired seat type and price range.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the sales unit, acquisition unit, notification unit, question unit, and application unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the sales unit is implemented by the control unit 46A of the smart device 14 and provides the user with an intuitive interface. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and retrieves relevant ticket information from past browsing and purchase history. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user of the expected multiplier and the start date of sales. The question unit is implemented by the control unit 46A of the smart device 14 and presents the user with Yes / No questions. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically makes applications using generating AI. The restriction unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and restricts transactions to fair prices. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the sales unit, acquisition unit, notification unit, question unit, and application unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the sales unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with an intuitive interface. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and retrieves relevant ticket information from past browsing and purchase history. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user of the expected multiplier and the start date of sales. The question unit is implemented by the control unit 46A of the smart glasses 214 and presents the user with Yes / No questions. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically makes applications using generating AI. The restriction unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12 and restricts transactions to fair prices. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the sales unit, acquisition unit, notification unit, question unit, and application unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the sales unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with an intuitive interface. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and retrieves relevant ticket information from past browsing and purchase history. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user of the expected multiplier and the start date of sales. The question unit is implemented by the control unit 46A of the headset terminal 314 and presents the user with Yes / No questions. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically makes applications using generating AI. The restriction unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and restricts transactions to fair prices. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the sales unit, acquisition unit, notification unit, question unit, and application unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the sales unit is implemented by the control unit 46A of the robot 414 and provides the user with an intuitive interface. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and retrieves relevant ticket information from past browsing and purchase history. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user of the expected multiplier and the start date of sales. The question unit is implemented by the control unit 46A of the robot 414 and presents the user with Yes / No questions. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically makes applications using generating AI. The restriction unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12 and restricts transactions to fair prices. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) The sales department provides a consumer-oriented sales platform with an easy-to-use UI / UX from the buyer's perspective, The acquisition unit acquires information on tickets that the customer may be interested in, the types of seats they are likely to purchase, and their prices, based on past browsing and purchase history obtained by the aforementioned sales unit. Based on the information acquired by the acquisition unit, a notification unit notifies the expected multiplier and sales start date of the ticket, A section that asks users about their purchase using a Yes / No question format, If "Yes" is selected in the aforementioned question section, the application section includes an application section that automatically performs the application while following the normal application flow using a generating AI. A system characterized by the following features. (Note 2) The resale site includes a restriction mechanism to limit transactions at fair prices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned limiting section is The system uses image and text recognition AI to determine the original price from the ticket information registered on the site, and then uses a generation AI to prevent the price from exceeding that amount plus a 10% commission. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned limiting section is We will build and implement a function using generation AI to detect fraudulent ticket sales and the sale of counterfeit tickets. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned sales department, It estimates user emotions and dynamically changes the UI / UX design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sales department, Analyzes users' past purchase history and automatically generates the optimal sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sales department, Collect real-time user behavior data and instantly adjust sales strategies. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sales department, It estimates user sentiment and customizes the sales page layout based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sales department, We offer region-specific special offers, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sales department, Analyze users' social media activity and provide relevant ticket information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of ticket information acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, Analyze the user's past browsing history and select the optimal method for obtaining it. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, Filter based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The system estimates the user's emotions and prioritizes the ticket information to retrieve based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, Prioritize retrieving highly relevant ticket information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, Analyze users' social media activity and retrieve relevant ticket information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, Adjust the level of detail in notifications based on the importance of the ticket. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, Apply different notification algorithms depending on the ticket category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, We will prioritize notifications based on when tickets go on sale. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The order of notifications will be adjusted based on the relevance of the tickets. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question section is, Analyze the user's past response history to select the most suitable question format. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question section is, Customize the questions based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question section is, The system estimates the user's emotions and adjusts the timing of questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question section is, Prioritize asking highly relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question section is, Analyze users' social media activity and ask relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned application section is, The system estimates the user's emotions and adjusts the application process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned application section is, Analyze the user's past application history and select the optimal application method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned application section is, Customize the application based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned application section is, The system estimates the user's emotions and adjusts the timing of the application based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned application section is, Prioritize applications that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned application section is, Analyze users' social media activity and generate relevant applications. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned limiting section is We estimate user sentiment and adjust price limit criteria based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned limiting section is Analyze the past transaction history of tickets to select the optimal price restriction method. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned limiting section is Apply different price limit algorithms depending on the ticket category. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned limiting section is The system estimates user sentiment and adjusts the timing of price limits based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned limiting section is Prioritizing price restrictions based on when tickets go on sale. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned limiting section is Adjust the order of price restrictions based on ticket relevance. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned limiting section is We will build and implement a function using generation AI to detect fraudulent ticket sales and the sale of counterfeit tickets. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0204] 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 sales department provides a consumer-oriented sales platform with an easy-to-use UI / UX from the buyer's perspective, The acquisition unit acquires information on tickets that may be of interest, the types of seats that are likely to be purchased, and their prices, based on past browsing and purchase history obtained by the aforementioned sales unit. Based on the information acquired by the acquisition unit, a notification unit notifies the expected multiplier and sales start date of the ticket, A section that asks users about their purchase using a Yes / No question format, If "Yes" is selected in the aforementioned question section, the application section includes an application section that automatically performs the application while following the normal application flow using a generating AI. A system characterized by the following features.

2. The resale site includes a restriction mechanism to limit transactions at fair prices. The system according to feature 1.

3. The aforementioned limiting section is The system uses image and text recognition AI to determine the original price from the ticket information registered on the site, and then uses a generation AI to prevent users from setting a price higher than that price plus a 10% commission. The system according to feature 2.

4. The aforementioned limiting section is We will build and implement a function using generation AI to detect fraudulent ticket sales and the sale of counterfeit tickets. The system according to feature 2.

5. The aforementioned sales department, It estimates user emotions and dynamically changes the UI / UX design based on those estimated emotions. The system according to feature 1.

6. The aforementioned sales department, Analyzes users' past purchase history and automatically generates the optimal sales strategy. The system according to feature 1.

7. The aforementioned sales department, Collect real-time user behavior data and instantly adjust sales strategies. The system according to feature 1.

8. The aforementioned sales department, It estimates user sentiment and customizes the sales page layout based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned sales department, We offer region-specific special offers, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned sales department, Analyze users' social media activity and provide relevant ticket information. The system according to feature 1.

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