system

Abstract

We provide the system. [Solution] A means for inputting data based on conditions desired by the user and storing said data on a server, A means by which the server automatically executes ticket reservations based on the user's desired conditions, based on the date and time of a pre-set event, A means of notifying users when tickets are secured, A system that includes this.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When it is impossible to access the start time of ticket sales, there may be an opportunity loss that the user cannot secure the desired ticket. Also, when competition is fierce immediately after the start of sales, it is very difficult to instantaneously select and secure limited seats. As a result, a situation occurs where many users cannot participate in the event they desire, which is a problem.

Means for Solving the Problems

[0005] This invention provides a system that automatically accesses the sales site at the start of sales and secures tickets based on the user's prior input of desired event information and conditions. Specifically, a server stores and monitors the user's data and connects to the sales site in real time to select the optimal seat and reserve the ticket. Furthermore, it includes means for automatically notifying the user of the secured ticket information. This ensures that users can obtain tickets even if they are unable to access the site at the start of sales.

[0006] A "user" refers to an individual or group that wishes to access the system and reserve tickets for an event.

[0007] "Desired conditions" refers to information such as the event name, date and time, seat type, and price range that the user specifies when booking tickets.

[0008] A "server" refers to a computing system that stores data received from users and automatically executes the ticket reservation process.

[0009] "Means of saving" refers to a function that stores data entered by the user in a database on the server and allows it to be retrieved as needed.

[0010] "Event information" refers to information including the date, time, location, and participation requirements for concerts, performances, lectures, etc., for which tickets are sold.

[0011] "Methods for automatically executing ticket reservations" refer to a function where a server accesses the sales site at the start of sales, automatically selects tickets based on the user's preferences, and proceeds with the purchase process.

[0012] "Means of notification" refers to communication methods such as email, app notifications, and micromessage alerts used to inform users when a ticket is secured. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to an automated system for reliably and efficiently obtaining the tickets desired by the user. Specific embodiments are described below.

[0035] First, the user enters details about the event they wish to attend via their device. This information includes the event name, date and time, desired seating category, and the maximum amount they are willing to spend. The entered data is then automatically sent to the server.

[0036] Next, the server stores the user's requested information in a database. Based on this data, the server sets a schedule to monitor the start time of sales and prepares access to the ticket sales site based on the predetermined time. The server periodically uses the sales site's API to update event information and resets the schedule if the start time of sales changes.

[0037] When the ticket sales start time arrives, the server automatically accesses the designated ticket sales website and searches for tickets that match the user's desired conditions. The server utilizes AI and other algorithms to select the best seats in real time. Payment is also automatically completed using the payment method the user registered in advance.

[0038] If the ticket acquisition is successful, the server will promptly notify the user of the successful acquisition. The notification will be sent via the method chosen by the user (e.g., email or in-app notification) and will include detailed information about the acquired ticket.

[0039] This system allows users to secure tickets smoothly even if they are unable to access the site at the start of sales, enabling them to participate in events with peace of mind despite their busy schedules. Specifically, it can reliably obtain tickets on behalf of users for events that are expected to sell out quickly, such as concerts by popular artists or trending sporting events.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses a terminal to enter information about the desired event. This includes the event name, date and time, desired seating category, and payment information. Once the user has finished entering the information, it is sent to the server.

[0043] Step 2:

[0044] The server stores the data received from the user in a database. This ensures that the user's preferences are reliably preserved and form the basis for subsequent processing.

[0045] Step 3:

[0046] The server sets a schedule to monitor the start time of sales for registered events. It also periodically updates event information via the ticket sales site's API and modifies the schedule whenever there are changes to the sales time.

[0047] Step 4:

[0048] As the sales start time approaches, the servers begin preparations. Just before sales begin, they establish a connection to access the sales site, ensuring access with minimal delay.

[0049] Step 5:

[0050] As soon as sales begin, the server immediately starts accessing the sales site. It automatically searches for and selects tickets that match the user's desired conditions. AI algorithms are used to optimize seating.

[0051] Step 6:

[0052] The server proceeds with the purchase of the selected ticket. This process is automated, including payment, using the payment information previously registered by the user.

[0053] Step 7:

[0054] Once the ticket purchase is confirmed, the server notifies the user of the result. The user is then informed of the details of the acquired ticket using email or app notifications.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] When users try to acquire event tickets, popular events often sell out instantly, making it difficult to secure tickets for their desired seats quickly and reliably. Furthermore, users must manually access the ticket market at the start of sales, creating time constraints. Additionally, if payment processing is not completed quickly, there is a risk that the seats they have found may be secured by other buyers.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for inputting information based on the characteristics requested by the user and storing the information in an information processing device; means for the information processing device to automatically acquire admission tickets based on the user's requested characteristics based on the time of a pre-set activity; means for notifying the user when admission tickets are secured; and means for automatically completing payment for admission tickets using a payment method registered by the user. This makes it possible for users to quickly and reliably acquire admission tickets for their desired events without time constraints.

[0060] A "user" is an individual or group that attempts to obtain an admission ticket to an event using this system.

[0061] "Desired characteristics" refer to the conditions that users desire when obtaining tickets for an event, and specifically include the event name, date and time, seat location, and budget.

[0062] An "information processing device" refers to a computer or server that stores and processes information received from users, and is a device that plays a role in automating the entire process of obtaining tickets.

[0063] "Activity time" refers to a specific date and time when ticket sales for an event begin, and is a reference time set in advance within the system.

[0064] "Ticket acquisition" refers to the entire process in which an information processing device automatically accesses a ticket sales website, selects an admission ticket that matches the user's requirements, and purchases it.

[0065] The "automatic completion method" refers to a function where the information processing device independently performs payment processing using the payment method registered in advance by the user, thereby completing the entire purchase procedure.

[0066] This invention is an automated system for reliably obtaining admission tickets to desired events. The system primarily consists of terminals, servers, and associated software.

[0067] First, the user uses a terminal to enter information about the event they wish to attend. This information includes the event name, date and time, desired seat location, and budget. This information is converted into digital data by the terminal and immediately sent to the server.

[0068] Next, the server stores the received information in a database. This database functions as an information processing device and creates a schedule for managing ticket sales start times. The server periodically retrieves and updates event information using the sales site's API, and resets the schedule if the sales start time changes.

[0069] Once ticket sales begin, the server automatically accesses the ticket sales website and searches for tickets that match the user's criteria. At this stage, an AI algorithm is used to select the optimal seat in real time.

[0070] Subsequently, the server automatically completes the purchase and payment of the admission ticket using the payment method previously registered by the user, such as credit card information. If the purchase is successful, the details of the admission ticket are saved, and the purchase record is added to the database.

[0071] Finally, after the purchase process is complete, the server sends a notification to the user confirming that they have successfully obtained their admission ticket. This notification will be sent via email or in-app notification and will include ticket details (e.g., seat number and admission method).

[0072] Specific examples of this system include concerts by popular artists and sporting events. Tickets for these events usually sell out quickly, but by using this invention, users can be guaranteed to obtain tickets.

[0073] (Example prompts for a generative AI model)

[0074] "Please explain step-by-step how to automate a concert ticket purchasing program. The user enters their information, the server prepares before sales begin, and then purchases the best tickets once sales start."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user uses their device to enter event details (e.g., event name, date and time, desired seating, budget limit). The device retrieves this information as digital data and prepares it for transmission to the server. The entered data is packaged in a format such as JSON. The output is the packaged information ready for data transmission.

[0078] Step 2:

[0079] The terminal sends the information received from the user to the server. The server stores the received data in a database. The data processing performed here involves formatting the input information into a format suitable for storage in the database. The output is the user's request data stored in the database.

[0080] Step 3:

[0081] The server generates a schedule to monitor ticket sales start times based on stored user information. The server uses the sales site's API to retrieve relevant event information and utilizes it in the schedule setting. Inputs are the user's desired conditions and information obtained from the API, while output is the schedule setting.

[0082] Step 4:

[0083] The server periodically calls the sales site's API to update event information. If there are any changes to the sales start time, the schedule is reset. This step involves real-time updates based on information retrieved from the API. The input is event information from the API, and the output is the updated schedule data.

[0084] Step 5:

[0085] When sales begin, the server automatically accesses the ticket sales website. Using an AI algorithm, it selects the best seats that match the user's preferences. The server then performs data analysis to find the optimal data points. The inputs are the user's preferences and seat information from the sales website, and the output is the selected seat data.

[0086] Step 6:

[0087] The server processes the payment. It automatically completes the purchase of admission tickets using the user's registered payment method. The data calculations include verifying payment information and executing the payment process. The input is the user's payment information, and the output is payment completion confirmation data.

[0088] Step 7:

[0089] After a successful purchase, the server sends a notification to the user. This notification includes details about the admission ticket and is sent via email or app notification. Ultimately, the input data is the result of a successful purchase process, and the output is the notification sent to the user.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In today's society, it has become extremely difficult for users to reliably and efficiently obtain tickets for popular events. In particular, users' busy schedules and the surge in access at the start of sales often cause them to miss the opportunity to acquire their desired tickets. Furthermore, manual ticket acquisition is time-consuming and laborious, so there is a need for an efficient system.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] This invention includes a server that receives information based on user-requested conditions and stores that information in a data storage device; a server that automatically monitors the event sales date and time via communication and executes reservations on the sales site based on the user's requirements; and a server that uses real-time optimization technology to select the optimal conditions when the event sales begin and completes the payment process. This makes it possible for users to quickly and reliably obtain tickets for their desired events and use them comfortably.

[0095] A "user" refers to an individual or group who wishes to obtain a ticket and is the entity that enters their desired conditions into the system.

[0096] "Conditions" refers to data that includes information such as details of the event the user wants, the characteristics of the ticket they want, the seating category, and the maximum purchase amount.

[0097] "Information" refers to all data that users provide to the system, and specifically includes event names, dates and times, seating categories, and purchase limits.

[0098] A "data storage device" is an internal storage device used to store information received by users.

[0099] "Via communication" refers to methods of exchanging data via the internet or a network.

[0100] "Event sales date and time" refers to the time and date when tickets for a specific event go on sale, and is important information for users.

[0101] A "sales site" refers to a website or platform that sells tickets over the internet.

[0102] "Executing a reservation" means completing the process of purchasing a ticket based on the user's desired conditions.

[0103] "In real time" means that processing and decisions are made immediately, indicating that we will respond without delay when event sales begin.

[0104] "Optimization technology" refers to techniques that use mathematical or algorithmic methods to select the most desirable seats and conditions for the user.

[0105] "Payment" refers to the act of providing money through a payment method as consideration for purchasing a ticket.

[0106] This invention provides an automated system for efficiently obtaining tickets to events desired by users. The system consists of a terminal for users to input their desired conditions and a server for processing the data.

[0107] Users use their devices to enter information about the event they want to attend, such as the event name, date and time, desired seating category, and maximum purchase amount. This information is transmitted to a server via the internet and stored in a database. Based on this information, the server monitors the event's sales start date and automatically accesses the ticket sales website once sales begin.

[0108] The programs on the server are developed using programming languages ​​such as Python and Java (registered trademark), and are designed to select the optimal seat using AI algorithms (e.g., TENSORFLOW (registered trademark)). Payments are also processed automatically through secure online payment services (e.g., PayPal and Stripe).

[0109] For example, in the case of concert tickets for a famous artist, the server can monitor sales trends and secure tickets even while the user is busy with work. In this way, users can participate in their desired events with peace of mind, even in highly competitive ticket acquisition situations.

[0110] An example of a prompt using a generative AI model is, "Please tell me how to build an app that automatically retrieves concert tickets for a popular artist and, if successful, displays payment details and seat information within the app." Based on this prompt, the generative AI model helps develop an application that suits the user.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user uses a device to enter information about the event they wish to attend. This information includes the event name, date and time, desired seating category, and maximum purchase amount. The entered information is then sent from the device to the server.

[0114] Step 2:

[0115] The server saves the received information to a database. This ensures that the user's preferences are recorded so they can be used in future processing. This process utilizes a database management system to properly manage and store the information.

[0116] Step 3:

[0117] The server periodically uses the ticket sales website's API to retrieve the latest information, such as the event's sales date and time. Based on the retrieved data, it identifies the sales date and time and sets a schedule. The schedule is updated as needed.

[0118] Step 4:

[0119] When the ticket sales start time for the user's desired event, the server automatically accesses the specified ticket sales website. Here, using the user's pre-saved criteria, it accurately and quickly finds the desired tickets even in situations where many people are competing for them.

[0120] Step 5:

[0121] The server uses an AI algorithm to select the optimal seat in real time. This process uses a generative AI model to evaluate seats and determine and select the option that best suits the user's conditions.

[0122] Step 6:

[0123] The server automatically completes payment for the selected ticket using the user's pre-registered payment method. Secure transactions are conducted using an online payment API, with careful attention paid to information security.

[0124] Step 7:

[0125] The server verifies whether the ticket was successfully secured and notifies the user of the result. The notification is delivered quickly and reliably to the user via email or the device's notification function. The notification includes detailed information about the acquired ticket.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention is an automated system that, by combining an emotion engine, enables users to obtain the tickets they desire more appropriately and efficiently. A specific example is shown below.

[0128] First, the user enters information about their desired event via the device. This information includes the event name, date and time, preferred seating category, and price range. The device also collects information such as the user's typing speed, pressure, and facial expressions, and provides this information to the emotion engine.

[0129] Next, the emotion engine analyzes the user's input data and behavioral characteristics to estimate the user's current emotional state. Based on this emotion analysis, it provides optimal suggestions to the user, for example, prioritizing popular seats if the user is excited, or focusing on comfortable seats if the user is relaxed.

[0130] Based on the sentiment analysis results, the server searches for tickets according to the user's preferences stored in the database. When sales begin, the server automatically connects to the sales site and reserves tickets under the optimal conditions, aided by the sentiment engine. At this time, an AI algorithm is used to select seats optimized according to the user's emotions. Payment is also completed automatically using the payment method registered by the user in advance.

[0131] Finally, when the server successfully secures a ticket, it appropriately notifies the user of this information. The emotion engine is also involved in the content of this notification, ensuring that it is delivered in a way that matches the user's emotions (for example, using positive language for good results).

[0132] This system ensures users can secure tickets to their desired events while receiving advanced support based on emotion recognition. For example, even for free events, the system can improve user satisfaction by providing suggestions based on their perceived emotions. For instance, if a user is feeling down, a relaxing concert might be suggested.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uses a device to enter information about the event they wish to attend. This information includes details such as the event name, desired date, seating category, and price restrictions. The device also transmits the user's facial expressions, voice tone, and typing speed to the emotion engine.

[0136] Step 2:

[0137] The device sends the acquired user behavior data to the emotion engine. The emotion engine analyzes this data to estimate the user's current emotional state. In this process, past emotional history, if any, is also taken into consideration.

[0138] Step 3:

[0139] The server receives the user's desired conditions and the results of the emotion engine's analysis, and stores them in a database. At the same time, the server develops an action plan leading up to the sales start time based on the ticket selection that the user is most likely to enjoy.

[0140] Step 4:

[0141] As the start time for sales approaches, the servers begin preparations and complete the setup for accessing the sales site according to emotion-based suggestions.

[0142] Step 5:

[0143] As soon as sales begin, the server accesses the sales site according to a predetermined strategy. Here, it searches for and selects tickets while optimizing choices based on the user's emotional state, desired seats, and price range.

[0144] Step 6:

[0145] The server automatically proceeds with the ticket purchase process. The user completes the purchase using the payment information they have previously set.

[0146] Step 7:

[0147] Once the purchase is complete, the server immediately notifies the user of the ticket acquisition result. Based on the analysis of the emotion engine, the notification uses language and content that is most appropriate for the user's current emotions. This further enhances user satisfaction.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Traditional ticket booking systems have the problem of not being able to make suggestions optimized for user needs because bookings are made without considering the user's emotional state. Furthermore, there is a challenge in that the entire process from booking to notification does not involve emotion-based customization to enhance user satisfaction.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] This invention includes a server that inputs information based on conditions desired by the user via a terminal and provides this information and user behavior data to an emotion analysis device; a means for the emotion analysis device to analyze the user's behavior data, estimate the emotional state, and generate optimal suggestions; and a means for providing the user with an emotionally appropriate notification when acquiring a ticket. This automates the optimal seat selection and reservation process according to the user's emotional state, enabling ticket reservations that improve user satisfaction.

[0153] A "user" refers to anyone who wishes to use the system to make a ticket reservation.

[0154] A "terminal" refers to an electronic device used by a user to input information.

[0155] "Information" refers to data such as event name, date and time, seating category, and price range, which are entered by the user based on their desired conditions.

[0156] "Behavioral data" refers to interaction information such as the user's input speed, the force with which they press keys, and their facial expressions.

[0157] An "emotion analysis device" refers to a device or system that analyzes user behavior data, estimates their emotional state, and generates optimal suggestions.

[0158] A "server" refers to a central processing unit that automatically performs ticket search, reservation, and notification based on user preferences and sentiment analysis results.

[0159] "Suggestions" refer to seating and event options generated by an emotion analysis device that are adapted to the user's emotional state.

[0160] "Notification" refers to the act of informing users of ticket acquisition results or other information using appropriate language that reflects their emotions.

[0161] This invention is a system that enables users to obtain tickets to their desired events in an efficient and satisfying manner. The system analyzes the user's emotional state and provides optimal suggestions and automated ticket reservations based on that analysis. The specific implementation method is described below.

[0162] The user uses a terminal to input information such as the event name, date and time, seating category, and price range. During this process, the terminal collects user behavior data such as input speed, pressure, and facial expressions. This data is transmitted to an emotion analysis device. The emotion analysis device uses a generative AI model to analyze the user's behavior data and estimate the user's emotional state. Based on this analysis, the emotion analysis device generates optimal suggestions for seat selection and event information.

[0163] The server searches the database for suitable tickets based on suggestions from the sentiment analysis device and the user's preferences. When sales begin, the server automatically connects to the ticket sales site and executes the reservation. Payment is automated using the payment method registered by the user. If a ticket is secured, the server notifies the user of the result in an appropriate expression based on their emotions.

[0164] As a concrete example, suppose a user expresses interest in a music event at a free local event, and the emotion analysis device estimates that the user is in a relaxed emotional state. In this case, the system would prioritize selecting comfortable seating and propose the most suitable outing plan for the user.

[0165] An example of a prompt for a generative AI model is: "I want to design a ticket reservation system that estimates the user's emotions from their input speed and facial expressions, and suggests appropriate seats. Please tell me what points I should consider and what improvements I can make to this system."

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user inputs event information through the terminal. This input includes the event name, date and time, seating category, and price range. The terminal receives this information and collects input speed, force, and facial expression data via the camera. The collected data becomes primitive behavioral data based on the user's input. This is the input data for the next processing step.

[0169] Step 2:

[0170] The terminal transmits collected behavioral data to an emotion analysis device. The emotion analysis device analyzes this data using a generative AI model. Specifically, it estimates the user's emotional state (excitement, relaxation, etc.) using input speed and facial expression data. The output is the user's emotional state data, which forms the basis for suggesting optimal seating and events.

[0171] Step 3:

[0172] The server receives emotional state data from the emotion analysis device. Using this data and the user's preferences, it performs a database search. Specifically, it identifies seats suitable for the user's emotions and searches for available tickets. It also utilizes an AI algorithm to select the optimal seat. The output of this process is information on available seats.

[0173] Step 4:

[0174] The server automatically connects to the sales site once the conditions for ticket booking are met. It attempts to book tickets with the best seating conditions, reflecting the sentiment analysis results. If the booking is successful, the process of completing payment using the set payment method begins. After the payment process is complete, it proceeds to prepare a booking confirmation notification. The output of this step is the ticket booking confirmation information.

[0175] Step 5:

[0176] The server notifies the user that the reservation is complete. Taking into account the results of the sentiment analysis device, it generates a notification message that matches the user's emotions. For example, it might send a message celebrating the successful securing of the expected event. This notification is displayed on the user's device. The final output is a reservation completion notification message to the user.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In today's retail industry, providing optimal product recommendations tailored to a customer's emotional state is a crucial element in improving customer satisfaction. However, traditional systems have struggled to efficiently perform real-time emotional analysis and personalized recommendations, resulting in insufficient improvements in the customer experience. There is a need to address this challenge and develop methods to provide optimal product recommendations to customers visiting stores, thereby increasing their purchasing intent.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for inputting information based on conditions desired by the user and storing said information in a storage device, means for performing sentiment analysis based on the acquired user input information and behavioral characteristics, and means for generating optimal suggestions for the user based on the sentiment analysis results. This makes it possible to analyze the emotional state of customers in stores in real time and propose optimal products and services based on the results.

[0182] A "user" refers to an individual or group that uses the system, and is the entity that inputs information or uses the service.

[0183] A "memory device" is a device used to store user input information, behavioral characteristics, and sentiment analysis results, and is a medium used for information retention and retrieval within a system.

[0184] "Emotion analysis" is a process that estimates the user's current emotional state based on their input information and behavioral characteristics, and is a necessary computational process for optimizing suggestions.

[0185] A "suggestion" is a selection of products or services offered to the user based on the results of sentiment analysis, reflecting the optimal selection according to the user's emotional state.

[0186] "Reservation" refers to a registration process to secure the activities or transactions desired by the user, and is an action automatically performed by the storage device based on the user's conditions.

[0187] "Notification" refers to a means of communication used to inform users when a reservation has been secured, and it has the function of reporting the details of the proposal and the status of the transaction.

[0188] The system for realizing this application consists of a user terminal, an emotion analysis engine, a memory device, a suggestion generation function, and a notification function. The user inputs information about their desired product via the terminal. This information includes product name, category, and price range. The terminal also collects emotion-related data such as the user's input speed, facial expressions, and voice tone, and provides this data to the emotion analysis engine.

[0189] The server uses high-performance computing power and pre-prepared emotion analysis software (e.g., Emotion API) to estimate and classify the emotional state from the user's input data. This analysis result is stored in memory and used as a dataset based on the user's preferences and emotions.

[0190] The suggestion generation function selects the most suitable product or service candidates for the user from a database on the server based on the sentiment analysis results. The suggested content changes according to the user's emotional state; for example, if the user is relaxed, products that offer a relaxing experience will be selected.

[0191] The server provides users with selected suggestions through a notification function. In this process, a generative AI model is used to create notification messages with positive language tailored to the user's emotions. For example, offers can be sent with warm messages designed to increase purchase intent.

[0192] Specific example:

[0193] For example, if a user expresses excitement in a clothing store, the server will prioritize suggesting information on new products and popular items exclusive to that store. Electronic payment services are also connected to ensure a seamless user purchasing process.

[0194] Example of a prompt:

[0195] "Analyze customer facial expression data to determine their current emotional state. Then, generate a list of recommended clothing items based on that emotion. Combine the emotional state and the recommendation list to create a positive notification message."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] Users enter their desired criteria via a terminal. The entered data includes product name, category, price range, and sentiment-related data. The terminal collects this information based on input speed, facial expressions, and voice data, and sends it to the server.

[0199] Step 2:

[0200] The server passes the received data to the emotion analysis engine. The emotion analysis engine analyzes facial image and audio data to estimate the user's current emotional state. For example, it processes facial expressions to quantify levels of happiness or excitement. The analysis results are recorded as an emotional state, which is then used in the next step.

[0201] Step 3:

[0202] The server uses the results of emotion analysis to generate optimal product suggestions for the user. Specifically, it selects products and services from a database in its memory that match the estimated emotional state. In this process, if the user is relaxed, it suggests comfortable and calming products; if the user is highly agitated, it suggests trendy products.

[0203] Step 4:

[0204] The server uses a generative AI model to transform the selected suggestions into positive expressions appropriate to the emotional state. In this process, the generative AI uses prompt sentences to create a message suitable for notification.

[0205] Step 5:

[0206] The server creates a final suggestion message and sends it to the user via the notification function. This notification includes a list of suggested products along with a message tailored to the user's sentiment. The user receives this notification and can then view further details.

[0207] 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.

[0208] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0214] 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.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0216] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] 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.

[0218] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The 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.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention relates to an automated system for reliably and efficiently obtaining the tickets desired by the user. Specific embodiments are described below.

[0224] First, the user enters details about the event they wish to attend via their device. This information includes the event name, date and time, desired seating category, and the maximum amount they are willing to spend. The entered data is then automatically sent to the server.

[0225] Next, the server stores the user's requested information in a database. Based on this data, the server sets a schedule to monitor the start time of sales and prepares access to the ticket sales site based on the predetermined time. The server periodically uses the sales site's API to update event information and resets the schedule if the start time of sales changes.

[0226] When the ticket sales start time arrives, the server automatically accesses the designated ticket sales website and searches for tickets that match the user's desired conditions. The server utilizes AI and other algorithms to select the best seats in real time. Payment is also automatically completed using the payment method the user registered in advance.

[0227] If the ticket acquisition is successful, the server will promptly notify the user of the successful acquisition. The notification will be sent via the method chosen by the user (e.g., email or in-app notification) and will include detailed information about the acquired ticket.

[0228] This system allows users to secure tickets smoothly even if they are unable to access the site at the start of sales, enabling them to participate in events with peace of mind despite their busy schedules. Specifically, it can reliably obtain tickets on behalf of users for events that are expected to sell out quickly, such as concerts by popular artists or trending sporting events.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user uses a terminal to enter information about the desired event. This includes the event name, date and time, desired seating category, and payment information. Once the user has finished entering the information, it is sent to the server.

[0232] Step 2:

[0233] The server stores the data received from the user in a database. This ensures that the user's preferences are reliably preserved and form the basis for subsequent processing.

[0234] Step 3:

[0235] The server sets a schedule to monitor the start time of sales for registered events. It also periodically updates event information via the ticket sales site's API and modifies the schedule whenever there are changes to the sales time.

[0236] Step 4:

[0237] As the sales start time approaches, the servers begin preparations. Just before sales begin, they establish a connection to access the sales site, ensuring access with minimal delay.

[0238] Step 5:

[0239] As soon as sales begin, the server immediately starts accessing the sales site. It automatically searches for and selects tickets that match the user's desired conditions. AI algorithms are used to optimize seating.

[0240] Step 6:

[0241] The server proceeds with the purchase of the selected ticket. This process is automated, including payment, using the payment information previously registered by the user.

[0242] Step 7:

[0243] Once the ticket purchase is confirmed, the server notifies the user of the result. The user is then informed of the details of the acquired ticket using email or app notifications.

[0244] (Example 1)

[0245] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0246] When users try to acquire event tickets, popular events often sell out instantly, making it difficult to secure tickets for their desired seats quickly and reliably. Furthermore, users must manually access the ticket market at the start of sales, creating time constraints. Additionally, if payment processing is not completed quickly, there is a risk that the seats they have found may be secured by other buyers.

[0247] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0248] In this invention, the server includes means for inputting information based on the characteristics requested by the user and storing the information in an information processing device; means for the information processing device to automatically acquire admission tickets based on the user's requested characteristics based on the time of a pre-set activity; means for notifying the user when admission tickets are secured; and means for automatically completing payment for admission tickets using a payment method registered by the user. This makes it possible for users to quickly and reliably acquire admission tickets for their desired events without time constraints.

[0249] A "user" is an individual or group that attempts to obtain an admission ticket to an event using this system.

[0250] "Desired characteristics" refer to the conditions that users desire when obtaining tickets for an event, and specifically include the event name, date and time, seat location, and budget.

[0251] An "information processing device" refers to a computer or server that stores and processes information received from users, and is a device that plays a role in automating the entire process of obtaining tickets.

[0252] "Activity time" refers to a specific date and time when ticket sales for an event begin, and is a reference time set in advance within the system.

[0253] "Ticket acquisition" refers to the entire process in which an information processing device automatically accesses a ticket sales website, selects an admission ticket that matches the user's requirements, and purchases it.

[0254] The "automatic completion method" refers to a function where the information processing device independently performs payment processing using the payment method registered in advance by the user, thereby completing the entire purchase procedure.

[0255] This invention is an automated system for reliably obtaining admission tickets to desired events. The system primarily consists of terminals, servers, and associated software.

[0256] First, the user uses a terminal to enter information about the event they wish to attend. This information includes the event name, date and time, desired seat location, and budget. This information is converted into digital data by the terminal and immediately sent to the server.

[0257] Next, the server stores the received information in a database. This database functions as an information processing device and creates a schedule for managing ticket sales start times. The server periodically retrieves and updates event information using the sales site's API, and resets the schedule if the sales start time changes.

[0258] Once ticket sales begin, the server automatically accesses the ticket sales website and searches for tickets that match the user's criteria. At this stage, an AI algorithm is used to select the optimal seat in real time.

[0259] Subsequently, the server automatically completes the purchase and payment of the admission ticket using the payment method previously registered by the user, such as credit card information. If the purchase is successful, the details of the admission ticket are saved, and the purchase record is added to the database.

[0260] Finally, after the purchase process is complete, the server sends a notification to the user confirming that they have successfully obtained their admission ticket. This notification will be sent via email or in-app notification and will include ticket details (e.g., seat number and admission method).

[0261] Specific examples of this system include concerts by popular artists and sporting events. Tickets for these events usually sell out quickly, but by using this invention, users can be guaranteed to obtain tickets.

[0262] (Example prompts for a generative AI model)

[0263] "Please explain step-by-step how to automate a concert ticket purchasing program. The user enters their information, the server prepares before sales begin, and then purchases the best tickets once sales start."

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user uses their device to enter event details (e.g., event name, date and time, desired seating, budget limit). The device retrieves this information as digital data and prepares it for transmission to the server. The entered data is packaged in a format such as JSON. The output is the packaged information ready for data transmission.

[0267] Step 2:

[0268] The terminal sends the information received from the user to the server. The server stores the received data in a database. The data processing performed here involves formatting the input information into a format suitable for storage in the database. The output is the user's request data stored in the database.

[0269] Step 3:

[0270] The server generates a schedule to monitor ticket sales start times based on stored user information. The server uses the sales site's API to retrieve relevant event information and utilizes it in the schedule setting. Inputs are the user's desired conditions and information obtained from the API, while output is the schedule setting.

[0271] Step 4:

[0272] The server periodically calls the sales site's API to update event information. If there are any changes to the sales start time, the schedule is reset. This step involves real-time updates based on information retrieved from the API. The input is event information from the API, and the output is the updated schedule data.

[0273] Step 5:

[0274] When sales begin, the server automatically accesses the ticket sales website. Using an AI algorithm, it selects the best seats that match the user's preferences. The server then performs data analysis to find the optimal data points. The inputs are the user's preferences and seat information from the sales website, and the output is the selected seat data.

[0275] Step 6:

[0276] The server processes the payment. It automatically completes the purchase of admission tickets using the user's registered payment method. The data calculations include verifying payment information and executing the payment process. The input is the user's payment information, and the output is payment completion confirmation data.

[0277] Step 7:

[0278] After a successful purchase, the server sends a notification to the user. This notification includes details about the admission ticket and is sent via email or app notification. Ultimately, the input data is the result of a successful purchase process, and the output is the notification sent to the user.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] In today's society, it has become extremely difficult for users to reliably and efficiently obtain tickets for popular events. In particular, users' busy schedules and the surge in access at the start of sales often cause them to miss the opportunity to acquire their desired tickets. Furthermore, manual ticket acquisition is time-consuming and laborious, so there is a need for an efficient system.

[0282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] This invention includes a server that receives information based on user-requested conditions and stores that information in a data storage device; a server that automatically monitors the event sales date and time via communication and executes reservations on the sales site based on the user's requirements; and a server that uses real-time optimization technology to select the optimal conditions when the event sales begin and completes the payment process. This makes it possible for users to quickly and reliably obtain tickets for their desired events and use them comfortably.

[0284] "User" refers to an individual or a group who wishes to obtain tickets and is the entity that inputs their desired conditions into the system.

[0285] "Condition" refers to data that includes detailed information about the event desired by the user, characteristics of the tickets desired, seat categories, upper limit of the purchase amount, and other such information.

[0286] "Information" refers to all the data provided by the user to the system, specifically including the event name, date and time, seat category, upper limit of the purchase amount, and so on.

[0287] "Data storage device" refers to the storage device inside the server used to save the received user information.

[0288] "Via communication" means a method of exchanging data via the Internet or a network.

[0289] "Event sale date and time" refers to the time and date when tickets for a specific event go on sale and is important information for users.

[0290] "Sales site" refers to a website or platform that sells tickets on the Internet.

[0291] "Execute a reservation" means to complete the procedure of purchasing tickets based on the desired conditions of the user.

[0292] "In real time" means that processing and judgment are carried out immediately, indicating that it can respond without delay at the start of event sales.

[0293] "Optimization technology" refers to a technology that uses mathematical or algorithmic methods to select the most desirable seats and conditions for users.

[0294] "Payment" refers to the act of providing money through a payment method as consideration for purchasing a ticket.

[0295] This invention provides an automated system for efficiently obtaining tickets to events desired by users. The system consists of a terminal for users to input their desired conditions and a server for processing the data.

[0296] Users use their devices to enter information about the event they want to attend, such as the event name, date and time, desired seating category, and maximum purchase amount. This information is transmitted to a server via the internet and stored in a database. Based on this information, the server monitors the event's sales start date and automatically accesses the ticket sales website once sales begin.

[0297] The programs on the server are developed using programming languages ​​such as Python and Java, and are designed to select the optimal seat using AI algorithms (e.g., TensorFlow). Payments are processed automatically through secure online payment services (e.g., PayPal and Stripe).

[0298] For example, in the case of concert tickets for a famous artist, the server can monitor sales trends and secure tickets even while the user is busy with work. In this way, users can participate in their desired events with peace of mind, even in highly competitive ticket acquisition situations.

[0299] An example of a prompt using a generative AI model is, "Please tell me how to build an app that automatically retrieves concert tickets for a popular artist and, if successful, displays payment details and seat information within the app." Based on this prompt, the generative AI model helps develop an application that suits the user.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user uses the terminal to input information about the desired event. This information includes the event name, date and time, desired seat category, and the upper limit of the purchasable amount. The input information is sent from the terminal to the server.

[0303] Step 2:

[0304] The server saves the received information in the database. As a result, the user's desired conditions are recorded so that they can be used in future processing. In this process, a database management system is utilized to appropriately manage and save the information.

[0305] Step 3:

[0306] The server periodically uses the API of the ticket sales site to obtain the latest information such as the event's sale date and time. Based on the obtained data, the sale date and time are specified, and a schedule is set. If necessary, the schedule is updated.

[0307] Step 4:

[0308] When the sale start time of the event desired by the user arrives, the server automatically accesses the specified ticket sales site. Here, even in a sales scenario where many desired tickets are in competition, the tickets are accurately and quickly searched for using the previously saved user conditions.

[0309] Step 5:

[0310] The server uses an AI algorithm to select the optimal seats in real time. In this process, a generated AI model is used to evaluate the seats and determine and select the option that best meets the user's conditions.

[0311] Step 6:

[0312] The server automatically completes payment for the selected ticket using the user's pre-registered payment method. Secure transactions are conducted using an online payment API, with careful attention paid to information security.

[0313] Step 7:

[0314] The server verifies whether the ticket was successfully secured and notifies the user of the result. The notification is delivered quickly and reliably to the user via email or the device's notification function. The notification includes detailed information about the acquired ticket.

[0315] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0316] This invention is an automated system that, by combining an emotion engine, enables users to obtain the tickets they desire more appropriately and efficiently. A specific example is shown below.

[0317] First, the user enters information about their desired event via the device. This information includes the event name, date and time, preferred seating category, and price range. The device also collects information such as the user's typing speed, pressure, and facial expressions, and provides this information to the emotion engine.

[0318] Next, the emotion engine analyzes the user's input data and behavioral characteristics to estimate the user's current emotional state. Based on this emotion analysis, it provides optimal suggestions to the user, for example, prioritizing popular seats if the user is excited, or focusing on comfortable seats if the user is relaxed.

[0319] Based on the sentiment analysis results, the server searches for tickets according to the user's preferences stored in the database. When sales begin, the server automatically connects to the sales site and reserves tickets under the optimal conditions, aided by the sentiment engine. At this time, an AI algorithm is used to select seats optimized according to the user's emotions. Payment is also completed automatically using the payment method registered by the user in advance.

[0320] Finally, when the server successfully secures a ticket, it appropriately notifies the user of this information. The emotion engine is also involved in the content of this notification, ensuring that it is delivered in a way that matches the user's emotions (for example, using positive language for good results).

[0321] This system ensures users can secure tickets to their desired events while receiving advanced support based on emotion recognition. For example, even for free events, the system can improve user satisfaction by providing suggestions based on their perceived emotions. For instance, if a user is feeling down, a relaxing concert might be suggested.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user uses a device to enter information about the event they wish to attend. This information includes details such as the event name, desired date, seating category, and price restrictions. The device also transmits the user's facial expressions, voice tone, and typing speed to the emotion engine.

[0325] Step 2:

[0326] The device sends the acquired user behavior data to the emotion engine. The emotion engine analyzes this data to estimate the user's current emotional state. In this process, past emotional history, if any, is also taken into consideration.

[0327] Step 3:

[0328] The server receives the user's desired conditions and the results of the emotion engine's analysis, and stores them in a database. At the same time, the server develops an action plan leading up to the sales start time based on the ticket selection that the user is most likely to enjoy.

[0329] Step 4:

[0330] As the start time for sales approaches, the servers begin preparations and complete the setup for accessing the sales site according to emotion-based suggestions.

[0331] Step 5:

[0332] As soon as sales begin, the server accesses the sales site according to a predetermined strategy. Here, it searches for and selects tickets while optimizing choices based on the user's emotional state, desired seats, and price range.

[0333] Step 6:

[0334] The server automatically proceeds with the ticket purchase process. The user completes the purchase using the payment information they have previously set.

[0335] Step 7:

[0336] Once the purchase is complete, the server immediately notifies the user of the ticket acquisition result. Based on the analysis of the emotion engine, the notification uses language and content that is most appropriate for the user's current emotions. This further enhances user satisfaction.

[0337] (Example 2)

[0338] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0339] Traditional ticket booking systems have the problem of not being able to make suggestions optimized for user needs because bookings are made without considering the user's emotional state. Furthermore, there is a challenge in that the entire process from booking to notification does not involve emotion-based customization to enhance user satisfaction.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] This invention includes a server that inputs information based on conditions desired by the user via a terminal and provides this information and user behavior data to an emotion analysis device; a means for the emotion analysis device to analyze the user's behavior data, estimate the emotional state, and generate optimal suggestions; and a means for providing the user with an emotionally appropriate notification when acquiring a ticket. This automates the optimal seat selection and reservation process according to the user's emotional state, enabling ticket reservations that improve user satisfaction.

[0342] A "user" refers to anyone who wishes to use the system to make a ticket reservation.

[0343] A "terminal" refers to an electronic device used by a user to input information.

[0344] "Information" refers to data such as event name, date and time, seating category, and price range, which are entered by the user based on their desired conditions.

[0345] "Behavioral data" refers to interaction information such as the user's input speed, the force with which they press keys, and their facial expressions.

[0346] An "emotion analysis device" refers to a device or system that analyzes user behavior data, estimates their emotional state, and generates optimal suggestions.

[0347] A "server" refers to a central processing unit that automatically performs ticket search, reservation, and notification based on user preferences and sentiment analysis results.

[0348] "Suggestions" refer to seating and event options generated by an emotion analysis device that are adapted to the user's emotional state.

[0349] "Notification" refers to the act of informing users of ticket acquisition results or other information using appropriate language that reflects their emotions.

[0350] This invention is a system that enables users to obtain tickets to their desired events in an efficient and satisfying manner. The system analyzes the user's emotional state and provides optimal suggestions and automated ticket reservations based on that analysis. The specific implementation method is described below.

[0351] The user uses a terminal to input information such as the event name, date and time, seating category, and price range. During this process, the terminal collects user behavior data such as input speed, pressure, and facial expressions. This data is transmitted to an emotion analysis device. The emotion analysis device uses a generative AI model to analyze the user's behavior data and estimate the user's emotional state. Based on this analysis, the emotion analysis device generates optimal suggestions for seat selection and event information.

[0352] The server searches the database for suitable tickets based on suggestions from the sentiment analysis device and the user's preferences. When sales begin, the server automatically connects to the ticket sales site and executes the reservation. Payment is automated using the payment method registered by the user. If a ticket is secured, the server notifies the user of the result in an appropriate expression based on their emotions.

[0353] As a concrete example, suppose a user expresses interest in a music event at a free local event, and the emotion analysis device estimates that the user is in a relaxed emotional state. In this case, the system would prioritize selecting comfortable seating and propose the most suitable outing plan for the user.

[0354] An example of a prompt for a generative AI model is: "I want to design a ticket reservation system that estimates the user's emotions from their input speed and facial expressions, and suggests appropriate seats. Please tell me what points I should consider and what improvements I can make to this system."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user inputs event information through the terminal. This input includes the event name, date and time, seating category, and price range. The terminal receives this information and collects input speed, force, and facial expression data via the camera. The collected data becomes primitive behavioral data based on the user's input. This is the input data for the next processing step.

[0358] Step 2:

[0359] The terminal transmits collected behavioral data to an emotion analysis device. The emotion analysis device analyzes this data using a generative AI model. Specifically, it estimates the user's emotional state (excitement, relaxation, etc.) using input speed and facial expression data. The output is the user's emotional state data, which forms the basis for suggesting optimal seating and events.

[0360] Step 3:

[0361] The server receives emotional state data from the emotion analysis device. Using this data and the user's preferences, it performs a database search. Specifically, it identifies seats suitable for the user's emotions and searches for available tickets. It also utilizes an AI algorithm to select the optimal seat. The output of this process is information on available seats.

[0362] Step 4:

[0363] The server automatically connects to the sales site once the conditions for ticket booking are met. It attempts to book tickets with the best seating conditions, reflecting the sentiment analysis results. If the booking is successful, the process of completing payment using the set payment method begins. After the payment process is complete, it proceeds to prepare a booking confirmation notification. The output of this step is the ticket booking confirmation information.

[0364] Step 5:

[0365] The server notifies the user that the reservation is complete. Taking into account the results of the sentiment analysis device, it generates a notification message that matches the user's emotions. For example, it might send a message celebrating the successful securing of the expected event. This notification is displayed on the user's device. The final output is a reservation completion notification message to the user.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0368] In today's retail industry, providing optimal product recommendations tailored to a customer's emotional state is a crucial element in improving customer satisfaction. However, traditional systems have struggled to efficiently perform real-time emotional analysis and personalized recommendations, resulting in insufficient improvements in the customer experience. There is a need to address this challenge and develop methods to provide optimal product recommendations to customers visiting stores, thereby increasing their purchasing intent.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes means for inputting information based on conditions desired by the user and storing said information in a storage device, means for performing sentiment analysis based on the acquired user input information and behavioral characteristics, and means for generating optimal suggestions for the user based on the sentiment analysis results. This makes it possible to analyze the emotional state of customers in stores in real time and propose optimal products and services based on the results.

[0371] A "user" refers to an individual or group that uses the system, and is the entity that inputs information or uses the service.

[0372] A "memory device" is a device used to store user input information, behavioral characteristics, and sentiment analysis results, and is a medium used for information retention and retrieval within a system.

[0373] "Emotion analysis" is a process that estimates the user's current emotional state based on their input information and behavioral characteristics, and is a necessary computational process for optimizing suggestions.

[0374] A "suggestion" is a selection of products or services offered to the user based on the results of sentiment analysis, reflecting the optimal selection according to the user's emotional state.

[0375] "Reservation" refers to a registration process to secure the activities or transactions desired by the user, and is an action automatically performed by the storage device based on the user's conditions.

[0376] "Notification" refers to a means of communication used to inform users when a reservation has been secured, and it has the function of reporting the details of the proposal and the status of the transaction.

[0377] The system for realizing this application consists of a user terminal, an emotion analysis engine, a memory device, a suggestion generation function, and a notification function. The user inputs information about their desired product via the terminal. This information includes product name, category, and price range. The terminal also collects emotion-related data such as the user's input speed, facial expressions, and voice tone, and provides this data to the emotion analysis engine.

[0378] The server uses high-performance computing power and pre-prepared emotion analysis software (e.g., Emotion API) to estimate and classify the emotional state from the user's input data. This analysis result is stored in memory and used as a dataset based on the user's preferences and emotions.

[0379] The suggestion generation function selects the most suitable product or service candidates for the user from a database on the server based on the sentiment analysis results. The suggested content changes according to the user's emotional state; for example, if the user is relaxed, products that offer a relaxing experience will be selected.

[0380] The server provides users with selected suggestions through a notification function. In this process, a generative AI model is used to create notification messages with positive language tailored to the user's emotions. For example, offers can be sent with warm messages designed to increase purchase intent.

[0381] Specific example:

[0382] For example, if a user expresses excitement in a clothing store, the server will prioritize suggesting information on new products and popular items exclusive to that store. Electronic payment services are also connected to ensure a seamless user purchasing process.

[0383] Example of a prompt:

[0384] "Analyze customer facial expression data to determine their current emotional state. Then, generate a list of recommended clothing items based on that emotion. Combine the emotional state and the recommendation list to create a positive notification message."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] Users enter their desired criteria via a terminal. The entered data includes product name, category, price range, and sentiment-related data. The terminal collects this information based on input speed, facial expressions, and voice data, and sends it to the server.

[0388] Step 2:

[0389] The server passes the received data to the emotion analysis engine. The emotion analysis engine analyzes facial image and audio data to estimate the user's current emotional state. For example, it processes facial expressions to quantify levels of happiness or excitement. The analysis results are recorded as an emotional state, which is then used in the next step.

[0390] Step 3:

[0391] The server uses the results of emotion analysis to generate optimal product suggestions for the user. Specifically, it selects products and services from a database in its memory that match the estimated emotional state. In this process, if the user is relaxed, it suggests comfortable and calming products; if the user is highly agitated, it suggests trendy products.

[0392] Step 4:

[0393] The server uses a generative AI model to transform the selected suggestions into positive expressions appropriate to the emotional state. In this process, the generative AI uses prompt sentences to create a message suitable for notification.

[0394] Step 5:

[0395] The server creates a final suggestion message and sends it to the user via the notification function. This notification includes a list of suggested products along with a message tailored to the user's sentiment. The user receives this notification and can then view further details.

[0396] 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.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0403] 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.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0405] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] 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.

[0407] 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.

[0408] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The 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.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This invention relates to an automated system for reliably and efficiently obtaining the tickets desired by the user. Specific embodiments are described below.

[0413] First, the user enters details about the event they wish to attend via their device. This information includes the event name, date and time, desired seating category, and the maximum amount they are willing to spend. The entered data is then automatically sent to the server.

[0414] Next, the server stores the user's requested information in a database. Based on this data, the server sets a schedule to monitor the start time of sales and prepares access to the ticket sales site based on the predetermined time. The server periodically uses the sales site's API to update event information and resets the schedule if the start time of sales changes.

[0415] When the ticket sales start time arrives, the server automatically accesses the designated ticket sales website and searches for tickets that match the user's desired conditions. The server utilizes AI and other algorithms to select the best seats in real time. Payment is also automatically completed using the payment method the user registered in advance.

[0416] If the ticket acquisition is successful, the server will promptly notify the user of the successful acquisition. The notification will be sent via the method chosen by the user (e.g., email or in-app notification) and will include detailed information about the acquired ticket.

[0417] This system allows users to secure tickets smoothly even if they are unable to access the site at the start of sales, enabling them to participate in events with peace of mind despite their busy schedules. Specifically, it can reliably obtain tickets on behalf of users for events that are expected to sell out quickly, such as concerts by popular artists or trending sporting events.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user uses a terminal to enter information about the desired event. This includes the event name, date and time, desired seating category, and payment information. Once the user has finished entering the information, it is sent to the server.

[0421] Step 2:

[0422] The server stores the data received from the user in a database. This ensures that the user's preferences are reliably preserved and form the basis for subsequent processing.

[0423] Step 3:

[0424] The server sets a schedule to monitor the start time of sales for registered events. It also periodically updates event information via the ticket sales site's API and modifies the schedule whenever there are changes to the sales time.

[0425] Step 4:

[0426] As the sales start time approaches, the servers begin preparations. Just before sales begin, they establish a connection to access the sales site, ensuring access with minimal delay.

[0427] Step 5:

[0428] As soon as sales begin, the server immediately starts accessing the sales site. It automatically searches for and selects tickets that match the user's desired conditions. AI algorithms are used to optimize seating.

[0429] Step 6:

[0430] The server proceeds with the purchase of the selected ticket. This process is automated, including payment, using the payment information previously registered by the user.

[0431] Step 7:

[0432] Once the ticket purchase is confirmed, the server notifies the user of the result. The user is then informed of the details of the acquired ticket using email or app notifications.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] When users try to acquire event tickets, popular events often sell out instantly, making it difficult to secure tickets for their desired seats quickly and reliably. Furthermore, users must manually access the ticket market at the start of sales, creating time constraints. Additionally, if payment processing is not completed quickly, there is a risk that the seats they have found may be secured by other buyers.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for inputting information based on the characteristics requested by the user and storing the information in an information processing device; means for the information processing device to automatically acquire admission tickets based on the user's requested characteristics based on the time of a pre-set activity; means for notifying the user when admission tickets are secured; and means for automatically completing payment for admission tickets using a payment method registered by the user. This makes it possible for users to quickly and reliably acquire admission tickets for their desired events without time constraints.

[0438] A "user" is an individual or group that attempts to obtain an admission ticket to an event using this system.

[0439] "Desired characteristics" refer to the conditions that users desire when obtaining tickets for an event, and specifically include the event name, date and time, seat location, and budget.

[0440] An "information processing device" refers to a computer or server that stores and processes information received from users, and is a device that plays a role in automating the entire process of obtaining tickets.

[0441] "Activity time" refers to a specific date and time when ticket sales for an event begin, and is a reference time set in advance within the system.

[0442] "Ticket acquisition" refers to the entire process in which an information processing device automatically accesses a ticket sales website, selects an admission ticket that matches the user's requirements, and purchases it.

[0443] The "automatic completion method" refers to a function where the information processing device independently performs payment processing using the payment method registered in advance by the user, thereby completing the entire purchase procedure.

[0444] This invention is an automated system for reliably obtaining admission tickets to desired events. The system primarily consists of terminals, servers, and associated software.

[0445] First, the user uses a terminal to enter information about the event they wish to attend. This information includes the event name, date and time, desired seat location, and budget. This information is converted into digital data by the terminal and immediately sent to the server.

[0446] Next, the server stores the received information in a database. This database functions as an information processing device and creates a schedule for managing ticket sales start times. The server periodically retrieves and updates event information using the sales site's API, and resets the schedule if the sales start time changes.

[0447] Once ticket sales begin, the server automatically accesses the ticket sales website and searches for tickets that match the user's criteria. At this stage, an AI algorithm is used to select the optimal seat in real time.

[0448] Subsequently, the server automatically completes the purchase and payment of the admission ticket using the payment method previously registered by the user, such as credit card information. If the purchase is successful, the details of the admission ticket are saved, and the purchase record is added to the database.

[0449] Finally, after the purchase process is complete, the server sends a notification to the user confirming that they have successfully obtained their admission ticket. This notification will be sent via email or in-app notification and will include ticket details (e.g., seat number and admission method).

[0450] Specific examples of this system include concerts by popular artists and sporting events. Tickets for these events usually sell out quickly, but by using this invention, users can be guaranteed to obtain tickets.

[0451] (Example prompts for a generative AI model)

[0452] "Please explain step-by-step how to automate a concert ticket purchasing program. The user enters their information, the server prepares before sales begin, and then purchases the best tickets once sales start."

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The user uses their device to enter event details (e.g., event name, date and time, desired seating, budget limit). The device retrieves this information as digital data and prepares it for transmission to the server. The entered data is packaged in a format such as JSON. The output is the packaged information ready for data transmission.

[0456] Step 2:

[0457] The terminal sends the information received from the user to the server. The server stores the received data in a database. The data processing performed here involves formatting the input information into a format suitable for storage in the database. The output is the user's request data stored in the database.

[0458] Step 3:

[0459] The server generates a schedule to monitor ticket sales start times based on stored user information. The server uses the sales site's API to retrieve relevant event information and utilizes it in the schedule setting. Inputs are the user's desired conditions and information obtained from the API, while output is the schedule setting.

[0460] Step 4:

[0461] The server periodically calls the sales site's API to update event information. If there are any changes to the sales start time, the schedule is reset. This step involves real-time updates based on information retrieved from the API. The input is event information from the API, and the output is the updated schedule data.

[0462] Step 5:

[0463] When sales begin, the server automatically accesses the ticket sales website. Using an AI algorithm, it selects the best seats that match the user's preferences. The server then performs data analysis to find the optimal data points. The inputs are the user's preferences and seat information from the sales website, and the output is the selected seat data.

[0464] Step 6:

[0465] The server processes the payment. It automatically completes the purchase of admission tickets using the user's registered payment method. The data calculations include verifying payment information and executing the payment process. The input is the user's payment information, and the output is payment completion confirmation data.

[0466] Step 7:

[0467] After a successful purchase, the server sends a notification to the user. This notification includes details about the admission ticket and is sent via email or app notification. Ultimately, the input data is the result of a successful purchase process, and the output is the notification sent to the user.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] In today's society, it has become extremely difficult for users to reliably and efficiently obtain tickets for popular events. In particular, users' busy schedules and the surge in access at the start of sales often cause them to miss the opportunity to acquire their desired tickets. Furthermore, manual ticket acquisition is time-consuming and laborious, so there is a need for an efficient system.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] This invention includes a server that receives information based on user-requested conditions and stores that information in a data storage device; a server that automatically monitors the event sales date and time via communication and executes reservations on the sales site based on the user's requirements; and a server that uses real-time optimization technology to select the optimal conditions when the event sales begin and completes the payment process. This makes it possible for users to quickly and reliably obtain tickets for their desired events and use them comfortably.

[0473] A "user" refers to an individual or group who wishes to obtain a ticket and is the entity that enters their desired conditions into the system.

[0474] "Conditions" refers to data that includes information such as details of the event the user wants, the characteristics of the ticket they want, the seating category, and the maximum purchase amount.

[0475] "Information" refers to all data that users provide to the system, and specifically includes event names, dates and times, seating categories, and purchase limits.

[0476] A "data storage device" is an internal storage device used to store information received by users.

[0477] "Via communication" refers to methods of exchanging data via the internet or a network.

[0478] "Event sales date and time" refers to the time and date when tickets for a specific event go on sale, and is important information for users.

[0479] A "sales site" refers to a website or platform that sells tickets over the internet.

[0480] "Executing a reservation" means completing the process of purchasing a ticket based on the user's desired conditions.

[0481] "In real time" means that processing and decisions are made immediately, indicating that we will respond without delay when event sales begin.

[0482] "Optimization technology" refers to techniques that use mathematical or algorithmic methods to select the most desirable seats and conditions for the user.

[0483] "Payment" refers to the act of providing money through a payment method as consideration for purchasing a ticket.

[0484] This invention provides an automated system for efficiently obtaining tickets to events desired by users. The system consists of a terminal for users to input their desired conditions and a server for processing the data.

[0485] Users use their devices to enter information about the event they want to attend, such as the event name, date and time, desired seating category, and maximum purchase amount. This information is transmitted to a server via the internet and stored in a database. Based on this information, the server monitors the event's sales start date and automatically accesses the ticket sales website once sales begin.

[0486] The programs on the server are developed using programming languages ​​such as Python and Java, and are designed to select the optimal seat using AI algorithms (e.g., TensorFlow). Payments are processed automatically through secure online payment services (e.g., PayPal and Stripe).

[0487] For example, in the case of concert tickets for a famous artist, the server can monitor sales trends and secure tickets even while the user is busy with work. In this way, users can participate in their desired events with peace of mind, even in highly competitive ticket acquisition situations.

[0488] An example of a prompt using a generative AI model is, "Please tell me how to build an app that automatically retrieves concert tickets for a popular artist and, if successful, displays payment details and seat information within the app." Based on this prompt, the generative AI model helps develop an application that suits the user.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user uses a device to enter information about the event they wish to attend. This information includes the event name, date and time, desired seating category, and maximum purchase amount. The entered information is then sent from the device to the server.

[0492] Step 2:

[0493] The server saves the received information to a database. This ensures that the user's preferences are recorded so they can be used in future processing. This process utilizes a database management system to properly manage and store the information.

[0494] Step 3:

[0495] The server periodically uses the ticket sales website's API to retrieve the latest information, such as the event's sales date and time. Based on the retrieved data, it identifies the sales date and time and sets a schedule. The schedule is updated as needed.

[0496] Step 4:

[0497] When the ticket sales start time for the user's desired event, the server automatically accesses the specified ticket sales website. Here, using the user's pre-saved criteria, it accurately and quickly finds the desired tickets even in situations where many people are competing for them.

[0498] Step 5:

[0499] The server uses an AI algorithm to select the optimal seat in real time. This process uses a generative AI model to evaluate seats and determine and select the option that best suits the user's conditions.

[0500] Step 6:

[0501] The server automatically completes payment for the selected ticket using the user's pre-registered payment method. Secure transactions are conducted using an online payment API, with careful attention paid to information security.

[0502] Step 7:

[0503] The server verifies whether the ticket was successfully secured and notifies the user of the result. The notification is delivered quickly and reliably to the user via email or the device's notification function. The notification includes detailed information about the acquired ticket.

[0504] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0505] This invention is an automated system that, by combining an emotion engine, enables users to obtain the tickets they desire more appropriately and efficiently. A specific example is shown below.

[0506] First, the user enters information about their desired event via the device. This information includes the event name, date and time, preferred seating category, and price range. The device also collects information such as the user's typing speed, pressure, and facial expressions, and provides this information to the emotion engine.

[0507] Next, the emotion engine analyzes the user's input data and behavioral characteristics to estimate the user's current emotional state. Based on this emotion analysis, it provides optimal suggestions to the user, for example, prioritizing popular seats if the user is excited, or focusing on comfortable seats if the user is relaxed.

[0508] Based on the sentiment analysis results, the server searches for tickets according to the user's preferences stored in the database. When sales begin, the server automatically connects to the sales site and reserves tickets under the optimal conditions, aided by the sentiment engine. At this time, an AI algorithm is used to select seats optimized according to the user's emotions. Payment is also completed automatically using the payment method registered by the user in advance.

[0509] Finally, when the server successfully secures a ticket, it appropriately notifies the user of this information. The emotion engine is also involved in the content of this notification, ensuring that it is delivered in a way that matches the user's emotions (for example, using positive language for good results).

[0510] This system ensures users can secure tickets to their desired events while receiving advanced support based on emotion recognition. For example, even for free events, the system can improve user satisfaction by providing suggestions based on their perceived emotions. For instance, if a user is feeling down, a relaxing concert might be suggested.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user uses a device to enter information about the event they wish to attend. This information includes details such as the event name, desired date, seating category, and price restrictions. The device also transmits the user's facial expressions, voice tone, and typing speed to the emotion engine.

[0514] Step 2:

[0515] The device sends the acquired user behavior data to the emotion engine. The emotion engine analyzes this data to estimate the user's current emotional state. In this process, past emotional history, if any, is also taken into consideration.

[0516] Step 3:

[0517] The server receives the user's desired conditions and the results of the emotion engine's analysis, and stores them in a database. At the same time, the server develops an action plan leading up to the sales start time based on the ticket selection that the user is most likely to enjoy.

[0518] Step 4:

[0519] As the start time for sales approaches, the servers begin preparations and complete the setup for accessing the sales site according to emotion-based suggestions.

[0520] Step 5:

[0521] As soon as sales begin, the server accesses the sales site according to a predetermined strategy. Here, it searches for and selects tickets while optimizing choices based on the user's emotional state, desired seats, and price range.

[0522] Step 6:

[0523] The server automatically proceeds with the ticket purchase process. The user completes the purchase using the payment information they have previously set.

[0524] Step 7:

[0525] Once the purchase is complete, the server immediately notifies the user of the ticket acquisition result. Based on the analysis of the emotion engine, the notification uses language and content that is most appropriate for the user's current emotions. This further enhances user satisfaction.

[0526] (Example 2)

[0527] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] Traditional ticket booking systems have the problem of not being able to make suggestions optimized for user needs because bookings are made without considering the user's emotional state. Furthermore, there is a challenge in that the entire process from booking to notification does not involve emotion-based customization to enhance user satisfaction.

[0529] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0530] This invention includes a server that inputs information based on conditions desired by the user via a terminal and provides this information and user behavior data to an emotion analysis device; a means for the emotion analysis device to analyze the user's behavior data, estimate the emotional state, and generate optimal suggestions; and a means for providing the user with an emotionally appropriate notification when acquiring a ticket. This automates the optimal seat selection and reservation process according to the user's emotional state, enabling ticket reservations that improve user satisfaction.

[0531] A "user" refers to anyone who wishes to use the system to make a ticket reservation.

[0532] A "terminal" refers to an electronic device used by a user to input information.

[0533] "Information" refers to data such as event name, date and time, seating category, and price range, which are entered by the user based on their desired conditions.

[0534] "Behavioral data" refers to interaction information such as the user's input speed, the force with which they press keys, and their facial expressions.

[0535] An "emotion analysis device" refers to a device or system that analyzes user behavior data, estimates their emotional state, and generates optimal suggestions.

[0536] A "server" refers to a central processing unit that automatically performs ticket search, reservation, and notification based on user preferences and sentiment analysis results.

[0537] "Suggestions" refer to seating and event options generated by an emotion analysis device that are adapted to the user's emotional state.

[0538] "Notification" refers to the act of informing users of ticket acquisition results or other information using appropriate language that reflects their emotions.

[0539] This invention is a system that enables users to obtain tickets to their desired events in an efficient and satisfying manner. The system analyzes the user's emotional state and provides optimal suggestions and automated ticket reservations based on that analysis. The specific implementation method is described below.

[0540] The user uses a terminal to input information such as the event name, date and time, seating category, and price range. During this process, the terminal collects user behavior data such as input speed, pressure, and facial expressions. This data is transmitted to an emotion analysis device. The emotion analysis device uses a generative AI model to analyze the user's behavior data and estimate the user's emotional state. Based on this analysis, the emotion analysis device generates optimal suggestions for seat selection and event information.

[0541] The server searches the database for suitable tickets based on suggestions from the sentiment analysis device and the user's preferences. When sales begin, the server automatically connects to the ticket sales site and executes the reservation. Payment is automated using the payment method registered by the user. If a ticket is secured, the server notifies the user of the result in an appropriate expression based on their emotions.

[0542] As a concrete example, suppose a user expresses interest in a music event at a free local event, and the emotion analysis device estimates that the user is in a relaxed emotional state. In this case, the system would prioritize selecting comfortable seating and propose the most suitable outing plan for the user.

[0543] An example of a prompt for a generative AI model is: "I want to design a ticket reservation system that estimates the user's emotions from their input speed and facial expressions, and suggests appropriate seats. Please tell me what points I should consider and what improvements I can make to this system."

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] The user inputs event information through the terminal. This input includes the event name, date and time, seating category, and price range. The terminal receives this information and collects input speed, force, and facial expression data via the camera. The collected data becomes primitive behavioral data based on the user's input. This is the input data for the next processing step.

[0547] Step 2:

[0548] The terminal transmits collected behavioral data to an emotion analysis device. The emotion analysis device analyzes this data using a generative AI model. Specifically, it estimates the user's emotional state (excitement, relaxation, etc.) using input speed and facial expression data. The output is the user's emotional state data, which forms the basis for suggesting optimal seating and events.

[0549] Step 3:

[0550] The server receives emotional state data from the emotion analysis device. Using this data and the user's preferences, it performs a database search. Specifically, it identifies seats suitable for the user's emotions and searches for available tickets. It also utilizes an AI algorithm to select the optimal seat. The output of this process is information on available seats.

[0551] Step 4:

[0552] The server automatically connects to the sales site once the conditions for ticket booking are met. It attempts to book tickets with the best seating conditions, reflecting the sentiment analysis results. If the booking is successful, the process of completing payment using the set payment method begins. After the payment process is complete, it proceeds to prepare a booking confirmation notification. The output of this step is the ticket booking confirmation information.

[0553] Step 5:

[0554] The server notifies the user that the reservation is complete. Taking into account the results of the sentiment analysis device, it generates a notification message that matches the user's emotions. For example, it might send a message celebrating the successful securing of the expected event. This notification is displayed on the user's device. The final output is a reservation completion notification message to the user.

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] In today's retail industry, providing optimal product recommendations tailored to a customer's emotional state is a crucial element in improving customer satisfaction. However, traditional systems have struggled to efficiently perform real-time emotional analysis and personalized recommendations, resulting in insufficient improvements in the customer experience. There is a need to address this challenge and develop methods to provide optimal product recommendations to customers visiting stores, thereby increasing their purchasing intent.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for inputting information based on conditions desired by the user and storing said information in a storage device, means for performing sentiment analysis based on the acquired user input information and behavioral characteristics, and means for generating optimal suggestions for the user based on the sentiment analysis results. This makes it possible to analyze the emotional state of customers in stores in real time and propose optimal products and services based on the results.

[0560] A "user" refers to an individual or group that uses the system, and is the entity that inputs information or uses the service.

[0561] A "memory device" is a device used to store user input information, behavioral characteristics, and sentiment analysis results, and is a medium used for information retention and retrieval within a system.

[0562] "Emotion analysis" is a process that estimates the user's current emotional state based on their input information and behavioral characteristics, and is a necessary computational process for optimizing suggestions.

[0563] A "suggestion" is a selection of products or services offered to the user based on the results of sentiment analysis, reflecting the optimal selection according to the user's emotional state.

[0564] "Reservation" refers to a registration process to secure the activities or transactions desired by the user, and is an action automatically performed by the storage device based on the user's conditions.

[0565] "Notification" refers to a means of communication used to inform users when a reservation has been secured, and it has the function of reporting the details of the proposal and the status of the transaction.

[0566] The system for realizing this application consists of a user terminal, an emotion analysis engine, a memory device, a suggestion generation function, and a notification function. The user inputs information about their desired product via the terminal. This information includes product name, category, and price range. The terminal also collects emotion-related data such as the user's input speed, facial expressions, and voice tone, and provides this data to the emotion analysis engine.

[0567] The server uses high-performance computing power and pre-prepared emotion analysis software (e.g., Emotion API) to estimate and classify the emotional state from the user's input data. This analysis result is stored in memory and used as a dataset based on the user's preferences and emotions.

[0568] The suggestion generation function selects the most suitable product or service candidates for the user from a database on the server based on the sentiment analysis results. The suggested content changes according to the user's emotional state; for example, if the user is relaxed, products that offer a relaxing experience will be selected.

[0569] The server provides users with selected suggestions through a notification function. In this process, a generative AI model is used to create notification messages with positive language tailored to the user's emotions. For example, offers can be sent with warm messages designed to increase purchase intent.

[0570] Specific example:

[0571] For example, if a user expresses excitement in a clothing store, the server will prioritize suggesting information on new products and popular items exclusive to that store. Electronic payment services are also connected to ensure a seamless user purchasing process.

[0572] Example of a prompt:

[0573] "Analyze customer facial expression data to determine their current emotional state. Then, generate a list of recommended clothing items based on that emotion. Combine the emotional state and the recommendation list to create a positive notification message."

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] Users enter their desired criteria via a terminal. The entered data includes product name, category, price range, and sentiment-related data. The terminal collects this information based on input speed, facial expressions, and voice data, and sends it to the server.

[0577] Step 2:

[0578] The server passes the received data to the emotion analysis engine. The emotion analysis engine analyzes facial image and audio data to estimate the user's current emotional state. For example, it processes facial expressions to quantify levels of happiness or excitement. The analysis results are recorded as an emotional state, which is then used in the next step.

[0579] Step 3:

[0580] The server uses the results of emotion analysis to generate optimal product suggestions for the user. Specifically, it selects products and services from a database in its memory that match the estimated emotional state. In this process, if the user is relaxed, it suggests comfortable and calming products; if the user is highly agitated, it suggests trendy products.

[0581] Step 4:

[0582] The server uses a generative AI model to transform the selected suggestions into positive expressions appropriate to the emotional state. In this process, the generative AI uses prompt sentences to create a message suitable for notification.

[0583] Step 5:

[0584] The server creates a final suggestion message and sends it to the user via the notification function. This notification includes a list of suggested products along with a message tailored to the user's sentiment. The user receives this notification and can then view further details.

[0585] 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.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0592] 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.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0594] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] 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.

[0596] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] 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.

[0598] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The 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.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention relates to an automated system for reliably and efficiently obtaining the tickets desired by the user. Specific embodiments are described below.

[0603] First, the user enters details about the event they wish to attend via their device. This information includes the event name, date and time, desired seating category, and the maximum amount they are willing to spend. The entered data is then automatically sent to the server.

[0604] Next, the server stores the user's requested information in a database. Based on this data, the server sets a schedule to monitor the start time of sales and prepares access to the ticket sales site based on the predetermined time. The server periodically uses the sales site's API to update event information and resets the schedule if the start time of sales changes.

[0605] When the ticket sales start time arrives, the server automatically accesses the designated ticket sales website and searches for tickets that match the user's desired conditions. The server utilizes AI and other algorithms to select the best seats in real time. Payment is also automatically completed using the payment method the user registered in advance.

[0606] If the ticket acquisition is successful, the server will promptly notify the user of the successful acquisition. The notification will be sent via the method chosen by the user (e.g., email or in-app notification) and will include detailed information about the acquired ticket.

[0607] This system allows users to secure tickets smoothly even if they are unable to access the site at the start of sales, enabling them to participate in events with peace of mind despite their busy schedules. Specifically, it can reliably obtain tickets on behalf of users for events that are expected to sell out quickly, such as concerts by popular artists or trending sporting events.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user uses a terminal to enter information about the desired event. This includes the event name, date and time, desired seating category, and payment information. Once the user has finished entering the information, it is sent to the server.

[0611] Step 2:

[0612] The server stores the data received from the user in a database. This ensures that the user's preferences are reliably preserved and form the basis for subsequent processing.

[0613] Step 3:

[0614] The server sets a schedule to monitor the start time of sales for registered events. It also periodically updates event information via the ticket sales site's API and modifies the schedule whenever there are changes to the sales time.

[0615] Step 4:

[0616] As the sales start time approaches, the servers begin preparations. Just before sales begin, they establish a connection to access the sales site, ensuring access with minimal delay.

[0617] Step 5:

[0618] As soon as sales begin, the server immediately starts accessing the sales site. It automatically searches for and selects tickets that match the user's desired conditions. AI algorithms are used to optimize seating.

[0619] Step 6:

[0620] The server proceeds with the purchase of the selected ticket. This process is automated, including payment, using the payment information previously registered by the user.

[0621] Step 7:

[0622] Once the ticket purchase is confirmed, the server notifies the user of the result. The user is then informed of the details of the acquired ticket using email or app notifications.

[0623] (Example 1)

[0624] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] When users try to acquire event tickets, popular events often sell out instantly, making it difficult to secure tickets for their desired seats quickly and reliably. Furthermore, users must manually access the ticket market at the start of sales, creating time constraints. Additionally, if payment processing is not completed quickly, there is a risk that the seats they have found may be secured by other buyers.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0627] In this invention, the server includes means for inputting information based on the characteristics requested by the user and storing the information in an information processing device; means for the information processing device to automatically acquire admission tickets based on the user's requested characteristics based on the time of a pre-set activity; means for notifying the user when admission tickets are secured; and means for automatically completing payment for admission tickets using a payment method registered by the user. This makes it possible for users to quickly and reliably acquire admission tickets for their desired events without time constraints.

[0628] A "user" is an individual or group that attempts to obtain an admission ticket to an event using this system.

[0629] "Desired characteristics" refer to the conditions that users desire when obtaining tickets for an event, and specifically include the event name, date and time, seat location, and budget.

[0630] An "information processing device" refers to a computer or server that stores and processes information received from users, and is a device that plays a role in automating the entire process of obtaining tickets.

[0631] "Activity time" refers to a specific date and time when ticket sales for an event begin, and is a reference time set in advance within the system.

[0632] "Ticket acquisition" refers to the entire process in which an information processing device automatically accesses a ticket sales website, selects an admission ticket that matches the user's requirements, and purchases it.

[0633] The "automatic completion method" refers to a function where the information processing device independently performs payment processing using the payment method registered in advance by the user, thereby completing the entire purchase procedure.

[0634] This invention is an automated system for reliably obtaining admission tickets to desired events. The system primarily consists of terminals, servers, and associated software.

[0635] First, the user uses a terminal to enter information about the event they wish to attend. This information includes the event name, date and time, desired seat location, and budget. This information is converted into digital data by the terminal and immediately sent to the server.

[0636] Next, the server stores the received information in a database. This database functions as an information processing device and creates a schedule for managing ticket sales start times. The server periodically retrieves and updates event information using the sales site's API, and resets the schedule if the sales start time changes.

[0637] Once ticket sales begin, the server automatically accesses the ticket sales website and searches for tickets that match the user's criteria. At this stage, an AI algorithm is used to select the optimal seat in real time.

[0638] Subsequently, the server automatically completes the purchase and payment of the admission ticket using the payment method previously registered by the user, such as credit card information. If the purchase is successful, the details of the admission ticket are saved, and the purchase record is added to the database.

[0639] Finally, after the purchase process is complete, the server sends a notification to the user confirming that they have successfully obtained their admission ticket. This notification will be sent via email or in-app notification and will include ticket details (e.g., seat number and admission method).

[0640] Specific examples of this system include concerts by popular artists and sporting events. Tickets for these events usually sell out quickly, but by using this invention, users can be guaranteed to obtain tickets.

[0641] (Example prompts for a generative AI model)

[0642] "Please explain step-by-step how to automate a concert ticket purchasing program. The user enters their information, the server prepares before sales begin, and then purchases the best tickets once sales start."

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The user uses their device to enter event details (e.g., event name, date and time, desired seating, budget limit). The device retrieves this information as digital data and prepares it for transmission to the server. The entered data is packaged in a format such as JSON. The output is the packaged information ready for data transmission.

[0646] Step 2:

[0647] The terminal sends the information received from the user to the server. The server stores the received data in a database. The data processing performed here involves formatting the input information into a format suitable for storage in the database. The output is the user's request data stored in the database.

[0648] Step 3:

[0649] The server generates a schedule to monitor ticket sales start times based on stored user information. The server uses the sales site's API to retrieve relevant event information and utilizes it in the schedule setting. Inputs are the user's desired conditions and information obtained from the API, while output is the schedule setting.

[0650] Step 4:

[0651] The server periodically calls the sales site's API to update event information. If there are any changes to the sales start time, the schedule is reset. This step involves real-time updates based on information retrieved from the API. The input is event information from the API, and the output is the updated schedule data.

[0652] Step 5:

[0653] When sales begin, the server automatically accesses the ticket sales website. Using an AI algorithm, it selects the best seats that match the user's preferences. The server then performs data analysis to find the optimal data points. The inputs are the user's preferences and seat information from the sales website, and the output is the selected seat data.

[0654] Step 6:

[0655] The server processes the payment. It automatically completes the purchase of admission tickets using the user's registered payment method. The data calculations include verifying payment information and executing the payment process. The input is the user's payment information, and the output is payment completion confirmation data.

[0656] Step 7:

[0657] After a successful purchase, the server sends a notification to the user. This notification includes details about the admission ticket and is sent via email or app notification. Ultimately, the input data is the result of a successful purchase process, and the output is the notification sent to the user.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In today's society, it has become extremely difficult for users to reliably and efficiently obtain tickets for popular events. In particular, users' busy schedules and the surge in access at the start of sales often cause them to miss the opportunity to acquire their desired tickets. Furthermore, manual ticket acquisition is time-consuming and laborious, so there is a need for an efficient system.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] This invention includes a server that receives information based on user-requested conditions and stores that information in a data storage device; a server that automatically monitors the event sales date and time via communication and executes reservations on the sales site based on the user's requirements; and a server that uses real-time optimization technology to select the optimal conditions when the event sales begin and completes the payment process. This makes it possible for users to quickly and reliably obtain tickets for their desired events and use them comfortably.

[0663] A "user" refers to an individual or group who wishes to obtain a ticket and is the entity that enters their desired conditions into the system.

[0664] "Conditions" refers to data that includes information such as details of the event the user wants, the characteristics of the ticket they want, the seating category, and the maximum purchase amount.

[0665] "Information" refers to all data that users provide to the system, and specifically includes event names, dates and times, seating categories, and purchase limits.

[0666] A "data storage device" is an internal storage device used to store information received by users.

[0667] "Via communication" refers to methods of exchanging data via the internet or a network.

[0668] "Event sales date and time" refers to the time and date when tickets for a specific event go on sale, and is important information for users.

[0669] A "sales site" refers to a website or platform that sells tickets over the internet.

[0670] "Executing a reservation" means completing the process of purchasing a ticket based on the user's desired conditions.

[0671] "In real time" means that processing and decisions are made immediately, indicating that we will respond without delay when event sales begin.

[0672] "Optimization technology" refers to techniques that use mathematical or algorithmic methods to select the most desirable seats and conditions for the user.

[0673] "Payment" refers to the act of providing money through a payment method as consideration for purchasing a ticket.

[0674] This invention provides an automated system for efficiently obtaining tickets to events desired by users. The system consists of a terminal for users to input their desired conditions and a server for processing the data.

[0675] Users use their devices to enter information about the event they want to attend, such as the event name, date and time, desired seating category, and maximum purchase amount. This information is transmitted to a server via the internet and stored in a database. Based on this information, the server monitors the event's sales start date and automatically accesses the ticket sales website once sales begin.

[0676] The programs on the server are developed using programming languages ​​such as Python and Java, and are designed to select the optimal seat using AI algorithms (e.g., TensorFlow). Payments are processed automatically through secure online payment services (e.g., PayPal and Stripe).

[0677] For example, in the case of concert tickets for a famous artist, the server can monitor sales trends and secure tickets even while the user is busy with work. In this way, users can participate in their desired events with peace of mind, even in highly competitive ticket acquisition situations.

[0678] An example of a prompt using a generative AI model is, "Please tell me how to build an app that automatically retrieves concert tickets for a popular artist and, if successful, displays payment details and seat information within the app." Based on this prompt, the generative AI model helps develop an application that suits the user.

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] The user uses a device to enter information about the event they wish to attend. This information includes the event name, date and time, desired seating category, and maximum purchase amount. The entered information is then sent from the device to the server.

[0682] Step 2:

[0683] The server saves the received information to a database. This ensures that the user's preferences are recorded so they can be used in future processing. This process utilizes a database management system to properly manage and store the information.

[0684] Step 3:

[0685] The server periodically uses the ticket sales website's API to retrieve the latest information, such as the event's sales date and time. Based on the retrieved data, it identifies the sales date and time and sets a schedule. The schedule is updated as needed.

[0686] Step 4:

[0687] When the ticket sales start time for the user's desired event, the server automatically accesses the specified ticket sales website. Here, using the user's pre-saved criteria, it accurately and quickly finds the desired tickets even in situations where many people are competing for them.

[0688] Step 5:

[0689] The server uses an AI algorithm to select the optimal seat in real time. This process uses a generative AI model to evaluate seats and determine and select the option that best suits the user's conditions.

[0690] Step 6:

[0691] The server automatically completes payment for the selected ticket using the user's pre-registered payment method. Secure transactions are conducted using an online payment API, with careful attention paid to information security.

[0692] Step 7:

[0693] The server verifies whether the ticket was successfully secured and notifies the user of the result. The notification is delivered quickly and reliably to the user via email or the device's notification function. The notification includes detailed information about the acquired ticket.

[0694] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0695] This invention is an automated system that, by combining an emotion engine, enables users to obtain the tickets they desire more appropriately and efficiently. A specific example is shown below.

[0696] First, the user enters information about their desired event via the device. This information includes the event name, date and time, preferred seating category, and price range. The device also collects information such as the user's typing speed, pressure, and facial expressions, and provides this information to the emotion engine.

[0697] Next, the emotion engine analyzes the user's input data and behavioral characteristics to estimate the user's current emotional state. Based on this emotion analysis, it provides optimal suggestions to the user, for example, prioritizing popular seats if the user is excited, or focusing on comfortable seats if the user is relaxed.

[0698] Based on the sentiment analysis results, the server searches for tickets according to the user's preferences stored in the database. When sales begin, the server automatically connects to the sales site and reserves tickets under the optimal conditions, aided by the sentiment engine. At this time, an AI algorithm is used to select seats optimized according to the user's emotions. Payment is also completed automatically using the payment method registered by the user in advance.

[0699] Finally, when the server successfully secures a ticket, it appropriately notifies the user of this information. The emotion engine is also involved in the content of this notification, ensuring that it is delivered in a way that matches the user's emotions (for example, using positive language for good results).

[0700] This system ensures users can secure tickets to their desired events while receiving advanced support based on emotion recognition. For example, even for free events, the system can improve user satisfaction by providing suggestions based on their perceived emotions. For instance, if a user is feeling down, a relaxing concert might be suggested.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The user uses a device to enter information about the event they wish to attend. This information includes details such as the event name, desired date, seating category, and price restrictions. The device also transmits the user's facial expressions, voice tone, and typing speed to the emotion engine.

[0704] Step 2:

[0705] The device sends the acquired user behavior data to the emotion engine. The emotion engine analyzes this data to estimate the user's current emotional state. In this process, past emotional history, if any, is also taken into consideration.

[0706] Step 3:

[0707] The server receives the user's desired conditions and the results of the emotion engine's analysis, and stores them in a database. At the same time, the server develops an action plan leading up to the sales start time based on the ticket selection that the user is most likely to enjoy.

[0708] Step 4:

[0709] As the start time for sales approaches, the servers begin preparations and complete the setup for accessing the sales site according to emotion-based suggestions.

[0710] Step 5:

[0711] As soon as sales begin, the server accesses the sales site according to a predetermined strategy. Here, it searches for and selects tickets while optimizing choices based on the user's emotional state, desired seats, and price range.

[0712] Step 6:

[0713] The server automatically proceeds with the ticket purchase process. The user completes the purchase using the payment information they have previously set.

[0714] Step 7:

[0715] Once the purchase is complete, the server immediately notifies the user of the ticket acquisition result. Based on the analysis of the emotion engine, the notification uses language and content that is most appropriate for the user's current emotions. This further enhances user satisfaction.

[0716] (Example 2)

[0717] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] Traditional ticket booking systems have the problem of not being able to make suggestions optimized for user needs because bookings are made without considering the user's emotional state. Furthermore, there is a challenge in that the entire process from booking to notification does not involve emotion-based customization to enhance user satisfaction.

[0719] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0720] This invention includes a server that inputs information based on conditions desired by the user via a terminal and provides this information and user behavior data to an emotion analysis device; a means for the emotion analysis device to analyze the user's behavior data, estimate the emotional state, and generate optimal suggestions; and a means for providing the user with an emotionally appropriate notification when acquiring a ticket. This automates the optimal seat selection and reservation process according to the user's emotional state, enabling ticket reservations that improve user satisfaction.

[0721] A "user" refers to anyone who wishes to use the system to make a ticket reservation.

[0722] A "terminal" refers to an electronic device used by a user to input information.

[0723] "Information" refers to data such as event name, date and time, seating category, and price range, which are entered by the user based on their desired conditions.

[0724] "Behavioral data" refers to interaction information such as the user's input speed, the force with which they press keys, and their facial expressions.

[0725] An "emotion analysis device" refers to a device or system that analyzes user behavior data, estimates their emotional state, and generates optimal suggestions.

[0726] A "server" refers to a central processing unit that automatically performs ticket search, reservation, and notification based on user preferences and sentiment analysis results.

[0727] "Suggestions" refer to seating and event options generated by an emotion analysis device that are adapted to the user's emotional state.

[0728] "Notification" refers to the act of informing users of ticket acquisition results or other information using appropriate language that reflects their emotions.

[0729] This invention is a system that enables users to obtain tickets to their desired events in an efficient and satisfying manner. The system analyzes the user's emotional state and provides optimal suggestions and automated ticket reservations based on that analysis. The specific implementation method is described below.

[0730] The user uses a terminal to input information such as the event name, date and time, seating category, and price range. During this process, the terminal collects user behavior data such as input speed, pressure, and facial expressions. This data is transmitted to an emotion analysis device. The emotion analysis device uses a generative AI model to analyze the user's behavior data and estimate the user's emotional state. Based on this analysis, the emotion analysis device generates optimal suggestions for seat selection and event information.

[0731] The server searches the database for suitable tickets based on suggestions from the sentiment analysis device and the user's preferences. When sales begin, the server automatically connects to the ticket sales site and executes the reservation. Payment is automated using the payment method registered by the user. If a ticket is secured, the server notifies the user of the result in an appropriate expression based on their emotions.

[0732] As a concrete example, suppose a user expresses interest in a music event at a free local event, and the emotion analysis device estimates that the user is in a relaxed emotional state. In this case, the system would prioritize selecting comfortable seating and propose the most suitable outing plan for the user.

[0733] An example of a prompt for a generative AI model is: "I want to design a ticket reservation system that estimates the user's emotions from their input speed and facial expressions, and suggests appropriate seats. Please tell me what points I should consider and what improvements I can make to this system."

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] The user inputs event information through the terminal. This input includes the event name, date and time, seating category, and price range. The terminal receives this information and collects input speed, force, and facial expression data via the camera. The collected data becomes primitive behavioral data based on the user's input. This is the input data for the next processing step.

[0737] Step 2:

[0738] The terminal transmits collected behavioral data to an emotion analysis device. The emotion analysis device analyzes this data using a generative AI model. Specifically, it estimates the user's emotional state (excitement, relaxation, etc.) using input speed and facial expression data. The output is the user's emotional state data, which forms the basis for suggesting optimal seating and events.

[0739] Step 3:

[0740] The server receives emotional state data from the emotion analysis device. Using this data and the user's preferences, it performs a database search. Specifically, it identifies seats suitable for the user's emotions and searches for available tickets. It also utilizes an AI algorithm to select the optimal seat. The output of this process is information on available seats.

[0741] Step 4:

[0742] The server automatically connects to the sales site once the conditions for ticket booking are met. It attempts to book tickets with the best seating conditions, reflecting the sentiment analysis results. If the booking is successful, the process of completing payment using the set payment method begins. After the payment process is complete, it proceeds to prepare a booking confirmation notification. The output of this step is the ticket booking confirmation information.

[0743] Step 5:

[0744] The server notifies the user that the reservation is complete. Taking into account the results of the sentiment analysis device, it generates a notification message that matches the user's emotions. For example, it might send a message celebrating the successful securing of the expected event. This notification is displayed on the user's device. The final output is a reservation completion notification message to the user.

[0745] (Application Example 2)

[0746] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] In today's retail industry, providing optimal product recommendations tailored to a customer's emotional state is a crucial element in improving customer satisfaction. However, traditional systems have struggled to efficiently perform real-time emotional analysis and personalized recommendations, resulting in insufficient improvements in the customer experience. There is a need to address this challenge and develop methods to provide optimal product recommendations to customers visiting stores, thereby increasing their purchasing intent.

[0748] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0749] In this invention, the server includes means for inputting information based on conditions desired by the user and storing said information in a storage device, means for performing sentiment analysis based on the acquired user input information and behavioral characteristics, and means for generating optimal suggestions for the user based on the sentiment analysis results. This makes it possible to analyze the emotional state of customers in stores in real time and propose optimal products and services based on the results.

[0750] A "user" refers to an individual or group that uses the system, and is the entity that inputs information or uses the service.

[0751] A "memory device" is a device used to store user input information, behavioral characteristics, and sentiment analysis results, and is a medium used for information retention and retrieval within a system.

[0752] "Emotion analysis" is a process that estimates the user's current emotional state based on their input information and behavioral characteristics, and is a necessary computational process for optimizing suggestions.

[0753] A "suggestion" is a selection of products or services offered to the user based on the results of sentiment analysis, reflecting the optimal selection according to the user's emotional state.

[0754] "Reservation" refers to a registration process to secure the activities or transactions desired by the user, and is an action automatically performed by the storage device based on the user's conditions.

[0755] "Notification" refers to a means of communication used to inform users when a reservation has been secured, and it has the function of reporting the details of the proposal and the status of the transaction.

[0756] The system for realizing this application consists of a user terminal, an emotion analysis engine, a memory device, a suggestion generation function, and a notification function. The user inputs information about their desired product via the terminal. This information includes product name, category, and price range. The terminal also collects emotion-related data such as the user's input speed, facial expressions, and voice tone, and provides this data to the emotion analysis engine.

[0757] The server uses high-performance computing power and pre-prepared emotion analysis software (e.g., Emotion API) to estimate and classify the emotional state from the user's input data. This analysis result is stored in memory and used as a dataset based on the user's preferences and emotions.

[0758] The suggestion generation function selects the most suitable product or service candidates for the user from a database on the server based on the sentiment analysis results. The suggested content changes according to the user's emotional state; for example, if the user is relaxed, products that offer a relaxing experience will be selected.

[0759] The server provides users with selected suggestions through a notification function. In this process, a generative AI model is used to create notification messages with positive language tailored to the user's emotions. For example, offers can be sent with warm messages designed to increase purchase intent.

[0760] Specific example:

[0761] For example, if a user expresses excitement in a clothing store, the server will prioritize suggesting information on new products and popular items exclusive to that store. Electronic payment services are also connected to ensure a seamless user purchasing process.

[0762] Example of a prompt:

[0763] "Analyze customer facial expression data to determine their current emotional state. Then, generate a list of recommended clothing items based on that emotion. Combine the emotional state and the recommendation list to create a positive notification message."

[0764] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0765] Step 1:

[0766] Users enter their desired criteria via a terminal. The entered data includes product name, category, price range, and sentiment-related data. The terminal collects this information based on input speed, facial expressions, and voice data, and sends it to the server.

[0767] Step 2:

[0768] The server passes the received data to the emotion analysis engine. The emotion analysis engine analyzes facial image and audio data to estimate the user's current emotional state. For example, it processes facial expressions to quantify levels of happiness or excitement. The analysis results are recorded as an emotional state, which is then used in the next step.

[0769] Step 3:

[0770] The server uses the results of emotion analysis to generate optimal product suggestions for the user. Specifically, it selects products and services from a database in its memory that match the estimated emotional state. In this process, if the user is relaxed, it suggests comfortable and calming products; if the user is highly agitated, it suggests trendy products.

[0771] Step 4:

[0772] The server uses a generative AI model to transform the selected suggestions into positive expressions appropriate to the emotional state. In this process, the generative AI uses prompt sentences to create a message suitable for notification.

[0773] Step 5:

[0774] The server creates a final suggestion message and sends it to the user via the notification function. This notification includes a list of suggested products along with a message tailored to the user's sentiment. The user receives this notification and can then view further details.

[0775] 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.

[0776] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0778] 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.

[0779] Figure 9 shows an 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.

[0780] 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.

[0781] 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.

[0782] 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, motorcycles, etc., 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, for example, based 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.

[0783] 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."

[0784] 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.

[0785] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0786] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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 the like 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.

[0795] 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.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] A means for inputting data based on conditions desired by the user and storing said data on a server,

[0799] A means by which the server automatically executes ticket reservations based on the user's desired conditions, based on the date and time of a pre-set event,

[0800] A means of notifying users when tickets are secured,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the server periodically retrieves event information and updates the reservation schedule based on the latest information.

[0804] (Claim 3)

[0805] The system according to claim 1, in which the server automatically selects the optimal seat based on the user's preferences and handles the payment process.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means for inputting information based on the characteristics requested by the user and storing said information in an information processing device,

[0809] A means by which an information processing device automatically obtains an admission ticket based on the user's request characteristics, based on the time of a pre-set activity,

[0810] A means of notifying users when admission tickets are secured,

[0811] A method for automatically completing the payment for admission tickets using the payment method registered by the user,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the information processing device periodically acquires activity information, updates the acquisition plan based on the latest information, and changes the sales start time to reset the schedule.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the information processing device automatically selects the most suitable seat based on the user's request and handles the securing and payment of admission tickets.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A means for receiving information based on conditions desired by the user and storing that information in a data storage device,

[0820] A means of automatically monitoring the event sales date and time via communication and executing reservations on the sales site based on user requirements,

[0821] A method to select the best conditions using real-time optimization technology at the start of event sales, and to complete the payment process,

[0822] A means of providing users with the results of the secured reservation,

[0823] A device that includes this.

[0824] (Claim 2)

[0825] The apparatus according to claim 1, wherein the communication device continuously acquires information and updates the reservation procedure based on the latest data.

[0826] (Claim 3)

[0827] The apparatus according to claim 1, wherein the communication device determines the optimal option based on the user's conditions and automatically completes the fund transfer process.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] A means for inputting information based on conditions desired by the user into a terminal and providing that information and the user's behavioral data to an emotion analysis device,

[0831] A means for an emotion analysis device to analyze user behavior data, estimate emotional state, and generate optimal suggestions,

[0832] A server automatically executes ticket reservations based on a pre-configured date and time, using sentiment analysis results and user preferences.

[0833] A means of providing users with emotionally tailored notifications when they acquire a ticket,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the server selects the optimal seat based on event information and sentiment analysis results, and automatically completes the payment.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein an emotion analysis device is involved in the content of the notification and the result notification is made in an appropriate expression based on the user's emotions.

[0839] "Application example 2 when combining with an emotional engine"

[0840] (Claim 1)

[0841] A means for inputting information based on conditions desired by the user and storing said information in a storage device,

[0842] A means of performing sentiment analysis based on acquired user input information and behavioral characteristics,

[0843] A method for generating optimal suggestions for users based on emotion analysis results,

[0844] Based on the proposal, the storage device automatically executes a reservation based on the user's desired conditions, using the date and time of the pre-set activity.

[0845] A means of notifying the user when a reservation is secured,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the storage device periodically acquires activity information and updates the reservation schedule based on the latest information.

[0849] (Claim 3)

[0850] The system according to claim 1, in which a storage device makes the optimal selection based on the user's wishes when generating proposal content based on the emotional state, and automatically performs the transaction processing. [Explanation of Symbols]

[0851] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting data based on conditions desired by the user and storing said data on a server, A means by which the server automatically executes ticket reservations based on the user's desired conditions, based on the date and time of a pre-set event, A means of notifying users when tickets are secured, A system that includes this.

2. The system according to claim 1, wherein the server periodically acquires event information and updates the reservation schedule based on the latest information.

3. The system according to claim 1, in which the server automatically selects the most suitable seat based on the user's preferences and handles the payment process.

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