system

Abstract

We provide the system. [Solution] Means for automatically accessing and obtaining a user's email address, A means of analyzing the content of acquired emails and extracting event information, A means of registering the extracted event information in a database and listing it, A means for sending a notification to the user based on the aforementioned event information, A means of providing information about tickets intended for resale to other potential buyers, 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, the method 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 obtaining a plurality of tickets for entertainment events, it is often difficult to manage them efficiently, and it takes time to check the date, location, and seat information. Especially in a highly competitive ticket market, not only obtaining tickets but also subsequent management becomes a burden, and users may feel stressed. Furthermore, reselling tickets due to schedule changes or the like is also a troublesome task for users.

Means for Solving the Problems

[0005] This invention provides a means for automatically acquiring a user's email address and analyzing and extracting ticket-related information using natural language processing technology. It also registers the analysis results in a database, allowing for the intuitive management of event information in a list format. Furthermore, it reduces the burden of ticket management by providing reminder notifications based on event information and offering an optimal seating suggestion function. It also includes a means for providing information on tickets intended for resale to other potential buyers, thereby automating and simplifying the process.

[0006] A "user" refers to an individual or group that uses the system to acquire and manage event tickets.

[0007] "Email" refers to digital messages sent and received over the internet, and is a means of communication stored in the user's mailbox.

[0008] "Means of automatically accessing and retrieving" refers to a function that connects to an email server with the user's permission and selects and collects the necessary emails.

[0009] "Methods for analyzing and extracting event information" refers to a system that uses natural language processing technology to identify and extract event-related information from the content of emails.

[0010] "Methods for registering and listing event information in a database" refers to methods for storing extracted event information in a structured format, making it easy for users to access and review it.

[0011] "Means of sending notifications" refers to communication methods used to inform users of information such as reminders based on event information.

[0012] "A means of providing information on tickets that users wish to resell" refers to a system function that allows users to present information about tickets they want to sell to other potential buyers, thereby facilitating smooth transactions.

[0013] The "optimal seat recommendation feature" is an algorithm that, based on analyzed information, suggests the most suitable seat according to the user's preferences and the characteristics of the event. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for efficiently managing tickets for entertainment events, in which three elements—a server, a user terminal, and the user—work together. First, the server accesses the user's email inbox. Based on the access rights previously granted by the user, the server retrieves emails and filters them to include ticket-related emails based on specific keywords.

[0036] Next, the server analyzes the filtered emails using natural language processing techniques to extract important information such as the event name, date, location, time, and seat information. The extracted information is then registered in a database by the server and stored in an organized format. This database is kept accessible to users via their terminals. Users can log in to view their ticket list.

[0037] Furthermore, the server has a function to send reminder notifications as the event date approaches. These notifications are delivered to the user's device via email or push notification, prompting the user to take the necessary action.

[0038] In addition, the server analyzes the user's past event participation history and makes suggestions recommending the best seat for the next event reservation. These suggestions are calculated based on interpreted database information and contribute to improving user convenience.

[0039] If a user is unable to attend an event, they can use their device to notify the server of their desire to resell the ticket. The server will then provide the resale ticket information to other potential buyers and support the successful completion of a transaction.

[0040] As a concrete example, suppose user A purchases a musical ticket online. The server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the performance date approaches, the server sends a reminder to user A's device, prompting them to prepare to attend. In the event that user A is unable to attend, they can send a resale request to the server, allowing the ticket to be sold to another user, B. This allows user A to manage and utilize their ticket without unnecessary stress.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server accesses the email inbox based on the user's permission and retrieves new emails. This involves communicating with the email server using the IMAP or POP3 protocol and downloading the emails.

[0044] Step 2:

[0045] The server filters the retrieved emails using specific keywords in the subject line and body to identify emails related to tickets. This ensures that only emails related to tickets are displayed.

[0046] Step 3:

[0047] The server analyzes filtered emails using natural language processing techniques. Information such as event name, date, time, location, and seat number is extracted from the email body.

[0048] Step 4:

[0049] The server registers the extracted event information in a database. The information is organized and stored for each user, and structured in a way that allows for later access.

[0050] Step 5:

[0051] The user's terminal displays a list of tickets based on information received from the server. The user then uses this list to check their event participation schedule.

[0052] Step 6:

[0053] The server monitors the event date and automatically generates and sends a reminder notification a few days before the scheduled date. The notification is received on the user's device, prompting the user to prepare for the event.

[0054] Step 7:

[0055] The server analyzes the user's past event participation history to calculate the optimal seat for new reservations. The analysis results are notified to the user's device and provided as reference information when making reservations.

[0056] Step 8:

[0057] The user selects the ticket they wish to resell and sends a resale request to the server. The server processes this information and lists it on the appropriate resale platform to provide it to potential buyers.

[0058] (Example 1)

[0059] 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."

[0060] To improve user convenience in managing event tickets, automated organization of digitized information, optimal seat recommendations, and resale support for those unable to attend are necessary. However, traditional methods often involve manual processes, requiring users to proactively manage their own communication records, which is burdensome. This has resulted in a lower quality user experience and made event participation cumbersome.

[0061] 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.

[0062] In this invention, the server includes means for automatically accessing and acquiring user communication records, means for analyzing the acquired communication records to extract event information, means for registering and listing the extracted event information in an information aggregation device, means for sending notifications to the user based on the event information, means for providing information on tickets that the user wishes to resell to other prospective buyers, and means for analyzing the user's past event participation history to recommend the best seat for the next event. This enables the user to efficiently and comfortably participate in events and manage their tickets.

[0063] "Communication records" refer to a collection of digital information, including a user's email and message history.

[0064] "Event information" refers to details such as the event name, date, location, time, and seating arrangements.

[0065] An "information storage device" refers to a system or database for storing and managing data.

[0066] "Notifications" refer to messages or alerts used to inform users of information.

[0067] "Intending to resell" refers to a situation where a user intends to sell their admission ticket to someone else.

[0068] "Ticket information" refers to information about digital or physical identification that grants permission to participate in a specific event.

[0069] "Optimal seating" refers to the ideal seating location at an event, recommended based on the user's past history and preferences.

[0070] This invention is an information processing system for efficiently managing user event admission tickets. The server accesses the user's communication records and automatically retrieves relevant information. This process involves retrieving data from a mail server using the IMAP protocol. The server also uses natural language processing (NLP) techniques to analyze the retrieved communication records and extract event information. This analysis utilizes a script written in Python and an NLP library (e.g., SpaCy).

[0071] The extracted event information is registered in an SQL database by the server and stored in an organized format. MySQL® is used as the specific database system. Users can access this database via their terminals to check their admission ticket information. The user interface is provided as a web portal using PHP and JavaScript®.

[0072] As an event approaches, the server sends a reminder notification to the user. This notification is delivered via email or push notification. Firebase Cloud Messaging is used for push notifications. Furthermore, the server analyzes past event attendance history to recommend the best seat for future event reservations. This analysis utilizes machine learning techniques based on historical data. For example, when user A purchases a musical ticket online, the server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the day of the event approaches, the server sends a reminder to the user's device to encourage preparation.

[0073] If a user is unable to attend an event, they can notify the server via their device of their desire to resell their ticket. The server then provides this resale information to other potential buyers, and the ticket is resold through an e-commerce platform. This functionality allows users to manage and use their tickets without any hassle.

[0074] An example of a prompt to a generative AI model would be: "Analyze the musical ticket confirmation email and extract the event name, date, location, time, and seat information." This would initiate the server's process of retrieving the appropriate information and saving it in a well-organized format.

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

[0076] Step 1:

[0077] The server accesses and retrieves the user's communication records. The input is the user's mail server account information, and the output is the data of the received emails. The server uses the IMAP protocol to log in to the specified email account and retrieve email information from the inbox. As a result, the complete text of the emails is collected on the server.

[0078] Step 2:

[0079] The server filters the acquired email data to identify emails containing event information. The input is all email data acquired in step 1, and the output is event-related email data. The server filters the emails using specific keywords (e.g., "ticket purchase confirmation," "event," etc.) to extract only relevant emails. This narrows down the target of subsequent analysis.

[0080] Step 3:

[0081] The server analyzes filtered emails using natural language processing (NLP) techniques to extract event information. The input is the filtered email data obtained in step 2, and the output is structured event information such as event name, date, location, time, and seating information. The server analyzes the email body using an NLP library (e.g., SpaCy) to extract this data. Specifically, it uses text parsing and regular expressions to identify the necessary information.

[0082] Step 4:

[0083] The server extracts event information and registers it in an information aggregation device, i.e., a database. The input is the structured event information obtained in step 3, and the output is the event information stored in the database. The server uses SQL statements to insert the data into the MySQL database. This ensures that the event information is stored in an organized format, allowing users to review it later.

[0084] Step 5:

[0085] Users access and view event information via their devices. Input is login credentials, and output is a list of their own event information. Users log in to the web portal and view the event information displayed on the screen. This interface is built with PHP and JavaScript, enabling smooth information searching and display.

[0086] Step 6:

[0087] The server sends a reminder notification as the event date approaches. The input is the event information and current date registered in the database, and the output is the reminder notification sent to the user. The server periodically checks the database and creates and sends a reminder a few days before the event. The notification is delivered as an email or push notification, using Firebase Cloud Messaging for push notifications.

[0088] Step 7:

[0089] The server analyzes the user's past event attendance history and recommends the best seat for the next event. The input is the past attendance history stored in the database, and the output is recommended seating information. The server uses machine learning algorithms to analyze past historical data and calculate the best seat for the next event based on trends. This process allows users to have a better event experience.

[0090] Step 8:

[0091] If a user is unable to participate, they notify the server of their resale request via their device. The input is the user's resale request information, and the output is the resale information to other potential buyers. The server accepts the resale request, registers the relevant information on the resale platform, and provides it to other potential buyers, thereby facilitating a quick resale.

[0092] (Application Example 1)

[0093] 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."

[0094] While systems existed to manage event tickets held by users, they were limited to managing ticket information and lacked the functionality to provide relevant product information based on the user's location and interests. Furthermore, features such as resale and optimal seating recommendations were limited, failing to adequately enhance user convenience. As a result, users were unable to obtain relevant product information until just before the event, making it difficult for them to take appropriate action.

[0095] 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.

[0096] In this invention, the server includes means for automatically accessing and acquiring user information, means for analyzing the acquired information and extracting activity information, means for registering and listing the extracted activity information in an information base, and means for detecting the user's location and transmitting product information based on their interests. This enables users to receive event-related information and products in a timely manner, thereby improving the event experience.

[0097] "User information" refers to electronic data related to a user, including, in particular, their email address and location information.

[0098] "Activity information" refers to events and related information that users are interested in, and specifically includes the event name, date, location, time, and seating information.

[0099] An "information base" is a database for storing extracted activity information, organized in a format accessible to users.

[0100] "Users" refers to individuals or groups who utilize this system and who have specific interests or concerns.

[0101] "Means of detecting location" refers to technologies for determining the user's current location, such as GPS and beacon technology, which are used to acquire location information.

[0102] "Interest-based product information" refers to information about products or services related to a user's specific areas of interest, and is provided based on the user's past behavior and preferences.

[0103] In an embodiment of this invention, the server first obtains necessary information from the user's email. This includes a process of collecting email data from the mail server using an API, based on the access rights previously authenticated by the user. At this time, the server uses filtering technology to identify event-related information from the email and extracts that data.

[0104] Next, the server analyzes the extracted data using natural language processing techniques. The software used for this is a natural language processing library such as SpaCy or NLTK, which is used to extract detailed information such as event name, date, location, time, and seat number. The analyzed information is stored in an information base by the server and listed so that users can easily access it.

[0105] Subsequently, the server uses Bluetooth and beacon technology to detect the user's location. For example, by using Bluetooth beacons, it can confirm whether the user has visited a physical store and immediately notify the device of product information based on their interests. This information includes product recommendations based on the user's past purchase history and interests. Specifically, it is possible to query external promotional information APIs via HTTP requests using the Python requests library and provide real-time information.

[0106] For example, consider a situation where a user plans to attend a concert by artist A, and related merchandise has just arrived in a store. In this case, the system can notify the user of this information on their device to pique their interest. Furthermore, if the user is searching for a specific product or service, a relevant prompt might be, "Generate a notification suggesting related products based on the event the user plans to attend."

[0107] In this way, users can quickly receive all relevant information when participating in an event, leading to a more fulfilling experience.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server makes an API call to the user's email server to retrieve emails related to the event. The input is the user's email account information, and the output is the retrieved email data. This data includes information such as the subject, body, sender, and date and time of sending.

[0111] Step 2:

[0112] The server filters the retrieved emails, specifically identifying event-related emails. This step involves detecting specific keywords in the email subject and body, and using them to select event emails. The input is the retrieved email data, and the output is the event-related email data.

[0113] Step 3:

[0114] The server analyzes filtered event emails using natural language processing techniques. Specifically, it uses SpaCy and NLTK to extract event names, dates, locations, times, and seat information from the email body. The input is filtered email data, and the output is detailed event information.

[0115] Step 4:

[0116] The server registers and lists detailed information about the analyzed events in an information base. The information base is built using a database system, and the information is stored in a table format. The input is the detailed event information, and the output is the updated information base.

[0117] Step 5:

[0118] The server detects the location of the user's device using a Bluetooth beacon. When the device enters the beacon's range, it collects the user's location information. The input is signal data from the beacon, and the output is the user's current location information.

[0119] Step 6:

[0120] The server generates relevant product information based on the user's current location and interests, and notifies the device. This process uses the Python requests library to retrieve promotional data from an external API, processing it to match the user's interests before sending the notification. The input is the user's current location and interests, and the output is the notification content.

[0121] 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.

[0122] This invention provides a more personalized experience by incorporating an emotion engine into an event ticket management system that utilizes users' email addresses. The system operates with three main components: a server, user terminals, and users.

[0123] First, the server accesses the user's email inbox and retrieves new emails. The retrieved emails are analyzed using natural language processing technology to extract detailed event information. This information is stored in a database, and the user can check their event schedule through their device.

[0124] In addition, this invention uses an emotion engine to recognize the user's emotions and adjusts the content provided based on analyzed event information and past history. The emotion engine infers the emotional state from the user's input and past interactions, and plays a role in optimizing the overall system response.

[0125] Specifically, the server evaluates the user's emotions through an emotion engine and sends notifications tailored to that state. The timing and content of the notifications are adjusted to the user's preferences, and event recommendations are also personalized as needed. This process ensures that users receive the most relevant information at the most appropriate time.

[0126] As an example, consider the case where user B purchases a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in the database. At this time, the emotion engine analyzes user B's past behavior and preferences to determine their emotional state. As the event date approaches, the server sends a reminder to user B's terminal that is tailored to their emotional state. If user B prefers to attend the event in a more relaxed state, the reminder content and additional information are adjusted. This allows user B to enjoy the event in a way that best suits their emotional state.

[0127] Thus, the present invention provides a means for optimizing the user's event participation experience by making full use of an emotion engine.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server obtains the user's permission to access their email inbox and download new emails. During this process, the server uses either the IMAP or POP3 protocol to retrieve emails from the specified folder.

[0131] Step 2:

[0132] The server filters downloaded emails. The filtering criteria include extracting emails that contain specific keywords (e.g., "tickets" or "reservations") in the subject or body.

[0133] Step 3:

[0134] The server analyzes filtered emails using natural language processing technology to extract information related to the event. This information includes the event name, date and time, location, and seat number.

[0135] Step 4:

[0136] The server registers the extracted event information in a database. This organizes the event information and saves it in a format that can be accessed later.

[0137] Step 5:

[0138] The user's terminal accesses the database and displays a list of event information related to the user. Through this interface, the user can intuitively check all their appointments.

[0139] Step 6:

[0140] The server uses a sentiment engine to infer the user's emotional state based on past user interaction data and input information. This includes data such as keyboard input, operation speed, and past selection history.

[0141] Step 7:

[0142] Based on the emotional state estimated by the emotion engine, the server adjusts the content and timing of notifications before sending them. For example, if a relaxed emotional state is estimated, a notification with ample time to spare will be sent.

[0143] Step 8:

[0144] When a user selects a ticket they wish to resell, they send a resale request to the server via their device. The server processes this request, lists the ticket on the appropriate resale platform, and provides information to potential buyers.

[0145] This system is designed to provide information that resonates with the user's emotions and improve the user experience.

[0146] (Example 2)

[0147] 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".

[0148] In today's world, users receive a vast amount of electronic communication documents daily, making it difficult to efficiently extract and manage useful event information from them. Furthermore, providing appropriate information tailored to users' emotions and preferences is a challenging task for typical systems. This project aims to improve this situation and realize information delivery optimized for individual users.

[0149] 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.

[0150] In this invention, the server includes means for automatically acquiring the user's electronic communication documents, means for analyzing the content of the acquired electronic communication documents to extract event information, and means for analyzing the user's emotional state using emotion evaluation technology and adjusting the notification content. This enables efficient management of event information useful to the user and the provision of personalized information according to emotions and preferences.

[0151] "Electronic communication documents" refer to digital communication information sent and received via email or messaging applications.

[0152] "Event information" refers to detailed data about a specific event or activity, including information such as the date, time, location, and participation requirements.

[0153] An "information storage device" is a collection of hardware or software that stores digital information and makes it accessible later.

[0154] "Emotional assessment technology" is a technology that analyzes a user's verbal expressions and behavioral data to infer or judge their emotional state.

[0155] A "notification" is a message or alert sent to inform a user of specific information.

[0156] The "optimal choice" refers to suggestions and recommendations that best match the user's preferences and needs.

[0157] This invention achieves personalized information presentation by incorporating sentiment evaluation technology into an event information management system that uses users' electronic communication documents. The system operates with three main components: a server, a user terminal, and the user.

[0158] The server accesses the user's electronic communications documents and retrieves new documents. This includes using the mail server's API. The retrieved documents are parsed using tools such as the Google® Cloud Natural Language API, and event information is extracted. This information is stored in data storage devices such as MySQL or PostgreSQL.

[0159] Furthermore, the server uses sentiment assessment technology based on the analyzed event information. Specifically, it utilizes software such as IBM Watson® Tone Analyzer to infer the user's emotional state from their past data and document content. Based on this inferred sentiment information, notifications are generated for the user at the optimal time. The notification content is adjusted according to the user's emotional state and sent to the terminal.

[0160] As an example, consider a user purchasing a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in its data storage. After sentiment evaluation technology analyzes the user's past behavior and preferences, as the event approaches, the server sends a reminder to the user's device that is tailored to their emotional state. For users who want to relax, this might include suggestions for relaxing music or activities.

[0161] An example of a prompt might be: "Suggest a reminder tailored to the user's emotional state to optimize their participation in a music event. Assume user B prefers a relaxed state."

[0162] This type of system allows users to receive optimized information tailored to their emotions and preferences, thereby improving their event participation experience.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server uses an API to retrieve the user's electronic communications documents. The input is the user's mailbox, and the output is the retrieved raw message data. The server uses polling and real-time push notifications to detect when an email has arrived.

[0166] Step 2:

[0167] The server converts the acquired message data into text format and sends it to the natural language processing engine. The input is raw message data, and the output is text data. Specifically, this involves extracting text and metadata from the message format.

[0168] Step 3:

[0169] The server utilizes a natural language processing engine to analyze and extract event information from text data. The input is text data, and the output is structured data such as event name, date and time, and location. Here, natural language processing techniques are used to analyze specific keywords and context.

[0170] Step 4:

[0171] The server records the extracted event information in its information storage device. The input is the parsed event information, and the output is a success message for the database entry. Specifically, this involves inserting records into the database using SQL statements.

[0172] Step 5:

[0173] The server retrieves data from an information storage device and evaluates the user's emotional state using sentiment assessment technology. The input consists of past event information and email content, while the output is evaluation information indicating the user's emotional state. The server uses pattern recognition technology to infer emotions.

[0174] Step 6:

[0175] The server generates optimized notifications based on the user's emotional state and event information, and sends them to the device. The input is emotional state evaluation information and event information, and the output is the notification displayed on the user's device. Here, the content and timing of the notification are adjusted, and it is sent via push notification or email.

[0176] Step 7:

[0177] The device displays received notifications to the user. The input is the notification message from the server, and the output is a visual or audible notification displayed in the user interface. Specifically, it adds a new item to the device's notification center and provides an alert to the user.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] In modern times, food delivery services are used by many people, but they lack personalized suggestions that take into account the user's emotional state. Traditional systems have struggled to consider a user's order history and emotional state to provide the optimal menu and delivery options. As a result, users are unable to receive services that match their emotional state and preferences at any given time.

[0181] 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.

[0182] In this invention, the server includes means for automatically acquiring user information, means for analyzing the content of the acquired information and extracting activity information, and means for evaluating the user's emotional state using emotion analysis means and personalizing information appropriate to the user's state. This makes it possible to recommend the most suitable food menu and delivery options according to the user's emotional state.

[0183] "User" refers to an individual or group that uses this system and is the subject of information acquisition, activity information extraction, and sentiment analysis.

[0184] "Information" refers to data related to a user, existing in the form of emails, order history, or other digital communications.

[0185] "Activity information" refers to data related to events and orders extracted by analyzing the content of the acquired information.

[0186] A "data storage device" is a physical or virtual device used to systematically register and manage data such as activity information.

[0187] "Communication" refers to the method of transmitting information from a system to a user, and can take the form of email or push notifications.

[0188] "Emotional analysis methods" refer to technologies that use information obtained from users to evaluate the user's emotional state through natural language processing and other analytical techniques.

[0189] "Options" refer to multiple possible actions or suggestions presented to the user, allowing them to choose the one that is most suitable.

[0190] The system for implementing this invention mainly consists of a server, a user terminal, and the user. The server is responsible for automatically acquiring user information, which includes data such as the user's emails and order history. The server utilizes natural language processing technology to extract activity information using the acquired information. Specifically, the Google Cloud Natural Language API may be used. Through this natural language processing technology, the server can analyze the content of the information and extract relevant activity data.

[0191] Next, the server uses sentiment analysis. Sentiment analysis is a technology that estimates a user's emotional state from their past behavior and current information, and Microsoft® Azure®'s Text Analytics for Sentiment Analysis can be used. Through this analysis, the user's emotional state is evaluated, and data is generated to adjust the content provided based on the results.

[0192] The user's device receives information sent from the server and performs optimized communication with the user. This includes providing information via push notifications and in-app messages.

[0193] As a concrete example, if a user is experiencing a particular emotion at the beginning of the week, the system will analyze the results and offer suggestions for relaxation. For instance, if the emotion engine detects that the user is feeling stressed, the user's device will be notified of an option to receive a free relaxing herbal tea.

[0194] An example of a prompt might be, "If the user's emotional state is one of relaxation, what menu item would you recommend?" This prompt uses a generative AI model to generate suggestions that best suit the user's needs.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server accesses the user's email account and retrieves new emails. In this process, the server takes unprocessed emails received by the user as input. It receives this email data as input and saves its contents.

[0198] Step 2:

[0199] The server analyzes the content of the retrieved emails using natural language processing techniques. This step uses technologies such as the Google Cloud Natural Language API to extract event-related activity information from the email body. The output of this process generates information about the extracted events and orders.

[0200] Step 3:

[0201] The server registers the extracted activity information in the data storage device and organizes it as configuration information. The input for this step is the activity information obtained in the previous step, and the output is the configured information registered in the database. This information is also listed so that the user can check it later.

[0202] Step 4:

[0203] The server evaluates the user's emotional state using sentiment analysis tools. The input for this step is the user's past behavior history and current order information, which is analyzed using Microsoft Azure's Text Analytics. The output generates data representing the user's emotional state.

[0204] Step 5:

[0205] The server generates and sends user-appropriate communications based on the sentiment analysis results to the user's device. The input consists of the sentiment analysis results and registered activity information, while the output is a personalized notification sent to the user's device. This notification is displayed as a push notification or in-app message.

[0206] Step 6:

[0207] The user's device receives the sent notification and presents it to the user. The input for this process is notification data from the server, and the output is recommended information and perks options presented to the user. For example, a user seeking relaxation might be presented with menu options that have a relaxing effect.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention is a system for efficiently managing tickets for entertainment events, in which three elements—a server, a user terminal, and the user—work together. First, the server accesses the user's email inbox. Based on the access rights previously granted by the user, the server retrieves emails and filters them to include ticket-related emails based on specific keywords.

[0225] Next, the server analyzes the filtered emails using natural language processing techniques to extract important information such as the event name, date, location, time, and seat information. The extracted information is then registered in a database by the server and stored in an organized format. This database is kept accessible to users via their terminals. Users can log in to view their ticket list.

[0226] Furthermore, the server has a function to send reminder notifications as the event date approaches. These notifications are delivered to the user's device via email or push notification, prompting the user to take the necessary action.

[0227] In addition, the server analyzes the user's past event participation history and makes suggestions recommending the best seat for the next event reservation. These suggestions are calculated based on interpreted database information and contribute to improving user convenience.

[0228] If a user is unable to attend an event, they can use their device to notify the server of their desire to resell the ticket. The server will then provide the resale ticket information to other potential buyers and support the successful completion of a transaction.

[0229] As a concrete example, suppose user A purchases a musical ticket online. The server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the performance date approaches, the server sends a reminder to user A's device, prompting them to prepare to attend. In the event that user A is unable to attend, they can send a resale request to the server, allowing the ticket to be sold to another user, B. This allows user A to manage and utilize their ticket without unnecessary stress.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server accesses the email inbox based on the user's permission and retrieves new emails. This involves communicating with the email server using the IMAP or POP3 protocol and downloading the emails.

[0233] Step 2:

[0234] The server filters the retrieved emails using specific keywords in the subject line and body to identify emails related to tickets. This ensures that only emails related to tickets are displayed.

[0235] Step 3:

[0236] The server analyzes filtered emails using natural language processing techniques. Information such as event name, date, time, location, and seat number is extracted from the email body.

[0237] Step 4:

[0238] The server registers the extracted event information in a database. The information is organized and stored for each user, and structured in a way that allows for later access.

[0239] Step 5:

[0240] The user's terminal displays a list of tickets based on information received from the server. The user then uses this list to check their event participation schedule.

[0241] Step 6:

[0242] The server monitors the event date and automatically generates and sends a reminder notification a few days before the scheduled date. The notification is received on the user's device, prompting the user to prepare for the event.

[0243] Step 7:

[0244] The server analyzes the user's past event participation history to calculate the optimal seat for new reservations. The analysis results are notified to the user's device and provided as reference information when making reservations.

[0245] Step 8:

[0246] The user selects the ticket they wish to resell and sends a resale request to the server. The server processes this information and lists it on the appropriate resale platform to provide it to potential buyers.

[0247] (Example 1)

[0248] 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."

[0249] To improve user convenience in managing event tickets, automated organization of digitized information, optimal seat recommendations, and resale support for those unable to attend are necessary. However, traditional methods often involve manual processes, requiring users to proactively manage their own communication records, which is burdensome. This has resulted in a lower quality user experience and made event participation cumbersome.

[0250] 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.

[0251] In this invention, the server includes means for automatically accessing and acquiring user communication records, means for analyzing the acquired communication records to extract event information, means for registering and listing the extracted event information in an information aggregation device, means for sending notifications to the user based on the event information, means for providing information on tickets that the user wishes to resell to other prospective buyers, and means for analyzing the user's past event participation history to recommend the best seat for the next event. This enables the user to efficiently and comfortably participate in events and manage their tickets.

[0252] "Communication records" refer to a collection of digital information, including a user's email and message history.

[0253] "Event information" refers to details such as the event name, date, location, time, and seating arrangements.

[0254] An "information storage device" refers to a system or database for storing and managing data.

[0255] "Notifications" refer to messages or alerts used to inform users of information.

[0256] "Intending to resell" refers to a situation where a user intends to sell their admission ticket to someone else.

[0257] "Ticket information" refers to information about digital or physical identification that grants permission to participate in a specific event.

[0258] "Optimal seating" refers to the ideal seating location at an event, recommended based on the user's past history and preferences.

[0259] This invention is an information processing system for efficiently managing user event admission tickets. The server accesses the user's communication records and automatically retrieves relevant information. This process involves retrieving data from a mail server using the IMAP protocol. The server also uses natural language processing (NLP) techniques to analyze the retrieved communication records and extract event information. This analysis utilizes a script written in Python and an NLP library (e.g., SpaCy).

[0260] The extracted event information is registered in an SQL database by the server and stored in an organized format. MySQL is used as the specific database system. Users can access this database via their terminals to check their admission ticket information. The user interface is provided as a web portal using PHP and JavaScript.

[0261] As an event approaches, the server sends a reminder notification to the user. This notification is delivered via email or push notification. Firebase Cloud Messaging is used for push notifications. Furthermore, the server analyzes past event attendance history to recommend the best seat for future event reservations. This analysis utilizes machine learning techniques based on historical data. For example, when user A purchases a musical ticket online, the server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the day of the event approaches, the server sends a reminder to the user's device to encourage preparation.

[0262] If a user is unable to attend an event, they can notify the server via their device of their desire to resell their ticket. The server then provides this resale information to other potential buyers, and the ticket is resold through an e-commerce platform. This functionality allows users to manage and use their tickets without any hassle.

[0263] An example of a prompt to a generative AI model would be: "Analyze the musical ticket confirmation email and extract the event name, date, location, time, and seat information." This would initiate the server's process of retrieving the appropriate information and saving it in a well-organized format.

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

[0265] Step 1:

[0266] The server accesses and retrieves the user's communication records. The input is the user's mail server account information, and the output is the data of the received emails. The server uses the IMAP protocol to log in to the specified email account and retrieve email information from the inbox. As a result, the complete text of the emails is collected on the server.

[0267] Step 2:

[0268] The server filters the acquired email data to identify emails containing event information. The input is all email data acquired in step 1, and the output is event-related email data. The server filters the emails using specific keywords (e.g., "ticket purchase confirmation," "event," etc.) to extract only relevant emails. This narrows down the target of subsequent analysis.

[0269] Step 3:

[0270] The server analyzes filtered emails using natural language processing (NLP) techniques to extract event information. The input is the filtered email data obtained in step 2, and the output is structured event information such as event name, date, location, time, and seating information. The server analyzes the email body using an NLP library (e.g., SpaCy) to extract this data. Specifically, it uses text parsing and regular expressions to identify the necessary information.

[0271] Step 4:

[0272] The server extracts event information and registers it in an information aggregation device, i.e., a database. The input is the structured event information obtained in step 3, and the output is the event information stored in the database. The server uses SQL statements to insert the data into the MySQL database. This ensures that the event information is stored in an organized format, allowing users to review it later.

[0273] Step 5:

[0274] Users access and view event information via their devices. Input is login credentials, and output is a list of their own event information. Users log in to the web portal and view the event information displayed on the screen. This interface is built with PHP and JavaScript, enabling smooth information searching and display.

[0275] Step 6:

[0276] The server sends a reminder notification as the event date approaches. The input is the event information and current date registered in the database, and the output is the reminder notification sent to the user. The server periodically checks the database and creates and sends a reminder a few days before the event. The notification is delivered as an email or push notification, using Firebase Cloud Messaging for push notifications.

[0277] Step 7:

[0278] The server analyzes the user's past event attendance history and recommends the best seat for the next event. The input is the past attendance history stored in the database, and the output is recommended seating information. The server uses machine learning algorithms to analyze past historical data and calculate the best seat for the next event based on trends. This process allows users to have a better event experience.

[0279] Step 8:

[0280] If a user is unable to participate, they notify the server of their resale request via their device. The input is the user's resale request information, and the output is the resale information to other potential buyers. The server accepts the resale request, registers the relevant information on the resale platform, and provides it to other potential buyers, thereby facilitating a quick resale.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] Conventionally, there have been systems for managing event tickets held by users, but they have merely managed ticket information and lacked the function of providing related product information based on the user's location information and interests. Also, the functions of resale and optimal seat recommendation are limited, and the convenience of users has not been sufficiently improved. As a result, users could not obtain effective related product information until just before the event, and it was difficult to appropriately take necessary actions.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for automatically accessing and acquiring user information, means for analyzing the acquired information to extract activity information, means for registering and listing the extracted activity information in an information base, and means for detecting the user's location and transmitting product information based on interests. Thereby, the user can receive information and products related to the event in a timely manner, and the event experience can be improved.

[0286] <​​​​​​​​​​​ "Users" refers to individuals or groups who utilize this system and who have specific interests or concerns.

[0290] "Means of detecting location" refers to technologies for determining the user's current location, such as GPS and beacon technology, which are used to acquire location information.

[0291] "Interest-based product information" refers to information about products or services related to a user's specific areas of interest, and is provided based on the user's past behavior and preferences.

[0292] In an embodiment of this invention, the server first obtains necessary information from the user's email. This includes a process of collecting email data from the mail server using an API, based on the access rights previously authenticated by the user. At this time, the server uses filtering technology to identify event-related information from the email and extracts that data.

[0293] Next, the server analyzes the extracted data using natural language processing techniques. The software used for this is a natural language processing library such as SpaCy or NLTK, which is used to extract detailed information such as event name, date, location, time, and seat number. The analyzed information is stored in an information base by the server and listed so that users can easily access it.

[0294] Subsequently, the server uses Bluetooth and beacon technology to detect the user's location. For example, by using Bluetooth beacons, it can confirm whether the user has visited a physical store and immediately notify the device of product information based on their interests. This information includes product recommendations based on the user's past purchase history and interests. Specifically, it is possible to query external promotional information APIs via HTTP requests using the Python requests library and provide real-time information.

[0295] For example, consider a situation where a user plans to attend a concert by artist A, and related merchandise has just arrived in a store. In this case, the system can notify the user of this information on their device to pique their interest. Furthermore, if the user is searching for a specific product or service, a relevant prompt might be, "Generate a notification suggesting related products based on the event the user plans to attend."

[0296] In this way, users can quickly receive all relevant information when participating in an event, leading to a more fulfilling experience.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server makes an API call to the user's email server to retrieve emails related to the event. The input is the user's email account information, and the output is the retrieved email data. This data includes information such as the subject, body, sender, and date and time of sending.

[0300] Step 2:

[0301] The server filters the retrieved emails, specifically identifying event-related emails. This step involves detecting specific keywords in the email subject and body, and using them to select event emails. The input is the retrieved email data, and the output is the event-related email data.

[0302] Step 3:

[0303] The server analyzes the filtered event emails using natural language processing techniques. Specifically, SpaCy or NLTK is used to extract the event name, date, location, time, and seat information from the email body. The input is the filtered email data, and the output is the detailed information of the event.

[0304] Step 4:

[0305] The server registers the detailed information of the analyzed event in the information base and lists it. The information base is constructed using a database system, and the information is stored in a table format. The input is the detailed information of the event, and the output is the updated information base.

[0306] Step 5:

[0307] The server detects the location where the user's terminal is installed using a Bluetooth beacon. When the terminal enters the range of the beacon, the location information of the user is collected. The input is the signal data from the beacon, and the output is the current location information of the user.

[0308] Step 6:

[0309] The server generates relevant product information based on the user's current location information and interest information and notifies the terminal. In this process, the requests library in Python is used to obtain promotion data from an external API, and the information is processed according to the user's interests and then notified. The input is the user's current location information and interest information, and the output is the notification content.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0311] This invention provides a more personalized experience by incorporating an emotion engine into an event ticket management system that utilizes users' email addresses. The system operates with three main components: a server, user terminals, and users.

[0312] First, the server accesses the user's email inbox and retrieves new emails. The retrieved emails are analyzed using natural language processing technology to extract detailed event information. This information is stored in a database, and the user can check their event schedule through their device.

[0313] In addition, this invention uses an emotion engine to recognize the user's emotions and adjusts the content provided based on analyzed event information and past history. The emotion engine infers the emotional state from the user's input and past interactions, and plays a role in optimizing the overall system response.

[0314] Specifically, the server evaluates the user's emotions through an emotion engine and sends notifications tailored to that state. The timing and content of the notifications are adjusted to the user's preferences, and event recommendations are also personalized as needed. This process ensures that users receive the most relevant information at the most appropriate time.

[0315] As an example, consider the case where user B purchases a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in the database. At this time, the emotion engine analyzes user B's past behavior and preferences to determine their emotional state. As the event date approaches, the server sends a reminder to user B's terminal that is tailored to their emotional state. If user B prefers to attend the event in a more relaxed state, the reminder content and additional information are adjusted. This allows user B to enjoy the event in a way that best suits their emotional state.

[0316] Thus, the present invention provides a means for optimizing the user's event participation experience by making full use of an emotion engine.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server obtains the user's permission to access their email inbox and download new emails. During this process, the server uses either the IMAP or POP3 protocol to retrieve emails from the specified folder.

[0320] Step 2:

[0321] The server filters downloaded emails. The filtering criteria include extracting emails that contain specific keywords (e.g., "tickets" or "reservations") in the subject or body.

[0322] Step 3:

[0323] The server analyzes filtered emails using natural language processing technology to extract information related to the event. This information includes the event name, date and time, location, and seat number.

[0324] Step 4:

[0325] The server registers the extracted event information in a database. This organizes the event information and saves it in a format that can be accessed later.

[0326] Step 5:

[0327] The user's terminal accesses the database and displays a list of event information related to the user. Through this interface, the user can intuitively check all their appointments.

[0328] Step 6:

[0329] The server uses a sentiment engine to infer the user's emotional state based on past user interaction data and input information. This includes data such as keyboard input, operation speed, and past selection history.

[0330] Step 7:

[0331] Based on the emotional state estimated by the emotion engine, the server adjusts the content and timing of notifications before sending them. For example, if a relaxed emotional state is estimated, a notification with ample time to spare will be sent.

[0332] Step 8:

[0333] When a user selects a ticket they wish to resell, they send a resale request to the server via their device. The server processes this request, lists the ticket on the appropriate resale platform, and provides information to potential buyers.

[0334] This system is designed to provide information that resonates with the user's emotions and improve the user experience.

[0335] (Example 2)

[0336] 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".

[0337] In today's world, users receive a vast amount of electronic communication documents daily, making it difficult to efficiently extract and manage useful event information from them. Furthermore, providing appropriate information tailored to users' emotions and preferences is a challenging task for typical systems. This project aims to improve this situation and realize information delivery optimized for individual users.

[0338] 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.

[0339] In this invention, the server includes means for automatically acquiring the user's electronic communication documents, means for analyzing the content of the acquired electronic communication documents to extract event information, and means for analyzing the user's emotional state using emotion evaluation technology and adjusting the notification content. This enables efficient management of event information useful to the user and the provision of personalized information according to emotions and preferences.

[0340] "Electronic communication documents" refer to digital communication information sent and received via email or messaging applications.

[0341] "Event information" refers to detailed data about a specific event or activity, including information such as the date, time, location, and participation requirements.

[0342] An "information storage device" is a collection of hardware or software that stores digital information and makes it accessible later.

[0343] "Emotional assessment technology" is a technology that analyzes a user's verbal expressions and behavioral data to infer or judge their emotional state.

[0344] A "notification" is a message or alert sent to inform a user of specific information.

[0345] The "optimal choice" refers to suggestions and recommendations that best match the user's preferences and needs.

[0346] This invention achieves personalized information presentation by incorporating sentiment evaluation technology into an event information management system that uses users' electronic communication documents. The system operates with three main components: a server, a user terminal, and the user.

[0347] The server accesses the user's electronic communications documents and retrieves new documents. This includes using the mail server's API. The retrieved documents are parsed using tools such as the Google Cloud Natural Language API, and event information is extracted. This information is stored in data storage devices such as MySQL or PostgreSQL.

[0348] Furthermore, the server uses sentiment assessment technology based on the analyzed event information. Specifically, it utilizes software such as IBM Watson Tone Analyzer to infer the user's emotional state from their past data and document content. Based on this inferred sentiment information, notifications are generated at the optimal time for the user. The notification content is adjusted according to the user's emotional state and sent to the terminal.

[0349] As an example, consider a user purchasing a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in its data storage. After sentiment evaluation technology analyzes the user's past behavior and preferences, as the event approaches, the server sends a reminder to the user's device that is tailored to their emotional state. For users who want to relax, this might include suggestions for relaxing music or activities.

[0350] An example of a prompt might be: "Suggest a reminder tailored to the user's emotional state to optimize their participation in a music event. Assume user B prefers a relaxed state."

[0351] This type of system allows users to receive optimized information tailored to their emotions and preferences, thereby improving their event participation experience.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server uses an API to retrieve the user's electronic communications documents. The input is the user's mailbox, and the output is the retrieved raw message data. The server uses polling and real-time push notifications to detect when an email has arrived.

[0355] Step 2:

[0356] The server converts the acquired message data into text format and sends it to the natural language processing engine. The input is raw message data, and the output is text data. Specifically, this involves extracting text and metadata from the message format.

[0357] Step 3:

[0358] The server utilizes a natural language processing engine to analyze and extract event information from text data. The input is text data, and the output is structured data such as event name, date and time, and location. Here, natural language processing techniques are used to analyze specific keywords and context.

[0359] Step 4:

[0360] The server records the extracted event information in its information storage device. The input is the parsed event information, and the output is a success message for the database entry. Specifically, this involves inserting records into the database using SQL statements.

[0361] Step 5:

[0362] The server retrieves data from an information storage device and evaluates the user's emotional state using sentiment assessment technology. The input consists of past event information and email content, while the output is evaluation information indicating the user's emotional state. The server uses pattern recognition technology to infer emotions.

[0363] Step 6:

[0364] The server generates optimized notifications based on the user's emotional state and event information, and sends them to the device. The input is emotional state evaluation information and event information, and the output is the notification displayed on the user's device. Here, the content and timing of the notification are adjusted, and it is sent via push notification or email.

[0365] Step 7:

[0366] The device displays received notifications to the user. The input is the notification message from the server, and the output is a visual or audible notification displayed in the user interface. Specifically, it adds a new item to the device's notification center and provides an alert to the user.

[0367] (Application Example 2)

[0368] 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."

[0369] In modern times, food delivery services are used by many people, but they lack personalized suggestions that take into account the user's emotional state. Traditional systems have struggled to consider a user's order history and emotional state to provide the optimal menu and delivery options. As a result, users are unable to receive services that match their emotional state and preferences at any given time.

[0370] 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.

[0371] In this invention, the server includes means for automatically acquiring user information, means for analyzing the content of the acquired information and extracting activity information, and means for evaluating the user's emotional state using emotion analysis means and personalizing information appropriate to the user's state. This makes it possible to recommend the most suitable food menu and delivery options according to the user's emotional state.

[0372] "User" refers to an individual or group that uses this system and is the subject of information acquisition, activity information extraction, and sentiment analysis.

[0373] "Information" refers to data related to a user, existing in the form of emails, order history, or other digital communications.

[0374] "Activity information" refers to data related to events and orders extracted by analyzing the content of the acquired information.

[0375] A "data storage device" is a physical or virtual device used to systematically register and manage data such as activity information.

[0376] "Communication" refers to the method of transmitting information from a system to a user, and can take the form of email or push notifications.

[0377] "Emotional analysis methods" refer to technologies that use information obtained from users to evaluate the user's emotional state through natural language processing and other analytical techniques.

[0378] "Options" refer to multiple possible actions or suggestions presented to the user, allowing them to choose the one that is most suitable.

[0379] The system for implementing this invention mainly consists of a server, a user terminal, and the user. The server is responsible for automatically acquiring user information, which includes data such as the user's emails and order history. The server utilizes natural language processing technology to extract activity information using the acquired information. Specifically, the Google Cloud Natural Language API may be used. Through this natural language processing technology, the server can analyze the content of the information and extract relevant activity data.

[0380] Next, the server uses sentiment analysis. Sentiment analysis is a technique that estimates a user's emotional state from their past behavior and current information, and Microsoft Azure's Text Analytics for Sentiment Analysis can be used. This analysis evaluates the user's emotional state and generates data to adjust the content provided based on the results.

[0381] The user's device receives information sent from the server and performs optimized communication with the user. This includes providing information via push notifications and in-app messages.

[0382] As a concrete example, if a user is experiencing a particular emotion at the beginning of the week, the system will analyze the results and offer suggestions for relaxation. For instance, if the emotion engine detects that the user is feeling stressed, the user's device will be notified of an option to receive a free relaxing herbal tea.

[0383] An example of a prompt might be, "If the user's emotional state is one of relaxation, what menu item would you recommend?" This prompt uses a generative AI model to generate suggestions that best suit the user's needs.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server accesses the user's email account and retrieves new emails. In this process, the server takes unprocessed emails received by the user as input. It receives this email data as input and saves its contents.

[0387] Step 2:

[0388] The server analyzes the content of the retrieved emails using natural language processing techniques. This step uses technologies such as the Google Cloud Natural Language API to extract event-related activity information from the email body. The output of this process generates information about the extracted events and orders.

[0389] Step 3:

[0390] The server registers the extracted activity information in the data storage device and organizes it as configuration information. The input for this step is the activity information obtained in the previous step, and the output is the configured information registered in the database. This information is also listed so that the user can check it later.

[0391] Step 4:

[0392] The server evaluates the user's emotional state using sentiment analysis tools. The input for this step is the user's past behavior history and current order information, which is analyzed using Microsoft Azure's Text Analytics. The output generates data representing the user's emotional state.

[0393] Step 5:

[0394] The server generates and sends user-appropriate communications based on the sentiment analysis results to the user's device. The input consists of the sentiment analysis results and registered activity information, while the output is a personalized notification sent to the user's device. This notification is displayed as a push notification or in-app message.

[0395] Step 6:

[0396] The user's device receives the sent notification and presents it to the user. The input for this process is notification data from the server, and the output is recommended information and perks options presented to the user. For example, a user seeking relaxation might be presented with menu options that have a relaxing effect.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention is a system for efficiently managing tickets for entertainment events, in which three elements—a server, a user terminal, and the user—work together. First, the server accesses the user's email inbox. Based on the access rights previously granted by the user, the server retrieves emails and filters them to include ticket-related emails based on specific keywords.

[0414] Next, the server analyzes the filtered emails using natural language processing techniques to extract important information such as the event name, date, location, time, and seat information. The extracted information is then registered in a database by the server and stored in an organized format. This database is kept accessible to users via their terminals. Users can log in to view their ticket list.

[0415] Furthermore, the server has a function to send reminder notifications as the event date approaches. These notifications are delivered to the user's device via email or push notification, prompting the user to take the necessary action.

[0416] In addition, the server analyzes the user's past event participation history and makes suggestions recommending the best seat for the next event reservation. These suggestions are calculated based on interpreted database information and contribute to improving user convenience.

[0417] If a user is unable to attend an event, they can use their device to notify the server of their desire to resell the ticket. The server will then provide the resale ticket information to other potential buyers and support the successful completion of a transaction.

[0418] As a concrete example, suppose user A purchases a musical ticket online. The server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the performance date approaches, the server sends a reminder to user A's device, prompting them to prepare to attend. In the event that user A is unable to attend, they can send a resale request to the server, allowing the ticket to be sold to another user, B. This allows user A to manage and utilize their ticket without unnecessary stress.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server accesses the email inbox based on the user's permission and retrieves new emails. This involves communicating with the email server using the IMAP or POP3 protocol and downloading the emails.

[0422] Step 2:

[0423] The server filters the retrieved emails using specific keywords in the subject line and body to identify emails related to tickets. This ensures that only emails related to tickets are displayed.

[0424] Step 3:

[0425] The server analyzes filtered emails using natural language processing techniques. Information such as event name, date, time, location, and seat number is extracted from the email body.

[0426] Step 4:

[0427] The server registers the extracted event information in a database. The information is organized and stored for each user, and structured in a way that allows for later access.

[0428] Step 5:

[0429] The user's terminal displays a list of tickets based on information received from the server. The user then uses this list to check their event participation schedule.

[0430] Step 6:

[0431] The server monitors the event date and automatically generates and sends a reminder notification a few days before the scheduled date. The notification is received on the user's device, prompting the user to prepare for the event.

[0432] Step 7:

[0433] The server analyzes the user's past event participation history to calculate the optimal seat for new reservations. The analysis results are notified to the user's device and provided as reference information when making reservations.

[0434] Step 8:

[0435] The user selects the ticket they wish to resell and sends a resale request to the server. The server processes this information and lists it on the appropriate resale platform to provide it to potential buyers.

[0436] (Example 1)

[0437] 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."

[0438] To improve user convenience in managing event tickets, automated organization of digitized information, optimal seat recommendations, and resale support for those unable to attend are necessary. However, traditional methods often involve manual processes, requiring users to proactively manage their own communication records, which is burdensome. This has resulted in a lower quality user experience and made event participation cumbersome.

[0439] 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.

[0440] In this invention, the server includes means for automatically accessing and acquiring user communication records, means for analyzing the acquired communication records to extract event information, means for registering and listing the extracted event information in an information aggregation device, means for sending notifications to the user based on the event information, means for providing information on tickets that the user wishes to resell to other prospective buyers, and means for analyzing the user's past event participation history to recommend the best seat for the next event. This enables the user to efficiently and comfortably participate in events and manage their tickets.

[0441] "Communication records" refer to a collection of digital information, including a user's email and message history.

[0442] "Event information" refers to details such as the event name, date, location, time, and seating arrangements.

[0443] An "information storage device" refers to a system or database for storing and managing data.

[0444] "Notifications" refer to messages or alerts used to inform users of information.

[0445] "Intending to resell" refers to a situation where a user intends to sell their admission ticket to someone else.

[0446] "Ticket information" refers to information about digital or physical identification that grants permission to participate in a specific event.

[0447] "Optimal seating" refers to the ideal seating location at an event, recommended based on the user's past history and preferences.

[0448] This invention is an information processing system for efficiently managing user event admission tickets. The server accesses the user's communication records and automatically retrieves relevant information. This process involves retrieving data from a mail server using the IMAP protocol. The server also uses natural language processing (NLP) techniques to analyze the retrieved communication records and extract event information. This analysis utilizes a script written in Python and an NLP library (e.g., SpaCy).

[0449] The extracted event information is registered in an SQL database by the server and stored in an organized format. MySQL is used as the specific database system. Users can access this database via their terminals to check their admission ticket information. The user interface is provided as a web portal using PHP and JavaScript.

[0450] As an event approaches, the server sends a reminder notification to the user. This notification is delivered via email or push notification. Firebase Cloud Messaging is used for push notifications. Furthermore, the server analyzes past event attendance history to recommend the best seat for future event reservations. This analysis utilizes machine learning techniques based on historical data. For example, when user A purchases a musical ticket online, the server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the day of the event approaches, the server sends a reminder to the user's device to encourage preparation.

[0451] If a user is unable to attend an event, they can notify the server via their device of their desire to resell their ticket. The server then provides this resale information to other potential buyers, and the ticket is resold through an e-commerce platform. This functionality allows users to manage and use their tickets without any hassle.

[0452] An example of a prompt to a generative AI model would be: "Analyze the musical ticket confirmation email and extract the event name, date, location, time, and seat information." This would initiate the server's process of retrieving the appropriate information and saving it in a well-organized format.

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

[0454] Step 1:

[0455] The server accesses and retrieves the user's communication records. The input is the user's mail server account information, and the output is the data of the received emails. The server uses the IMAP protocol to log in to the specified email account and retrieve email information from the inbox. As a result, the complete text of the emails is collected on the server.

[0456] Step 2:

[0457] The server filters the acquired email data to identify emails containing event information. The input is all email data acquired in step 1, and the output is event-related email data. The server filters the emails using specific keywords (e.g., "ticket purchase confirmation," "event," etc.) to extract only relevant emails. This narrows down the target of subsequent analysis.

[0458] Step 3:

[0459] The server analyzes filtered emails using natural language processing (NLP) techniques to extract event information. The input is the filtered email data obtained in step 2, and the output is structured event information such as event name, date, location, time, and seating information. The server analyzes the email body using an NLP library (e.g., SpaCy) to extract this data. Specifically, it uses text parsing and regular expressions to identify the necessary information.

[0460] Step 4:

[0461] The server extracts event information and registers it in an information aggregation device, i.e., a database. The input is the structured event information obtained in step 3, and the output is the event information stored in the database. The server uses SQL statements to insert the data into the MySQL database. This ensures that the event information is stored in an organized format, allowing users to review it later.

[0462] Step 5:

[0463] Users access and view event information via their devices. Input is login credentials, and output is a list of their own event information. Users log in to the web portal and view the event information displayed on the screen. This interface is built with PHP and JavaScript, enabling smooth information searching and display.

[0464] Step 6:

[0465] The server sends a reminder notification as the event date approaches. The input is the event information and current date registered in the database, and the output is the reminder notification sent to the user. The server periodically checks the database and creates and sends a reminder a few days before the event. The notification is delivered as an email or push notification, using Firebase Cloud Messaging for push notifications.

[0466] Step 7:

[0467] The server analyzes the user's past event attendance history and recommends the best seat for the next event. The input is the past attendance history stored in the database, and the output is recommended seating information. The server uses machine learning algorithms to analyze past historical data and calculate the best seat for the next event based on trends. This process allows users to have a better event experience.

[0468] Step 8:

[0469] If a user is unable to participate, they notify the server of their resale request via their device. The input is the user's resale request information, and the output is the resale information to other potential buyers. The server accepts the resale request, registers the relevant information on the resale platform, and provides it to other potential buyers, thereby facilitating a quick resale.

[0470] (Application Example 1)

[0471] 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."

[0472] While systems existed to manage event tickets held by users, they were limited to managing ticket information and lacked the functionality to provide relevant product information based on the user's location and interests. Furthermore, features such as resale and optimal seating recommendations were limited, failing to adequately enhance user convenience. As a result, users were unable to obtain relevant product information until just before the event, making it difficult for them to take appropriate action.

[0473] 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.

[0474] In this invention, the server includes means for automatically accessing and acquiring user information, means for analyzing the acquired information and extracting activity information, means for registering and listing the extracted activity information in an information base, and means for detecting the user's location and transmitting product information based on their interests. This enables users to receive event-related information and products in a timely manner, thereby improving the event experience.

[0475] "User information" refers to electronic data related to a user, including, in particular, their email address and location information.

[0476] "Activity information" refers to events and related information that users are interested in, and specifically includes the event name, date, location, time, and seating information.

[0477] An "information base" is a database for storing extracted activity information, organized in a format accessible to users.

[0478] "Users" refers to individuals or groups who utilize this system and who have specific interests or concerns.

[0479] "Means of detecting location" refers to technologies for determining the user's current location, such as GPS and beacon technology, which are used to acquire location information.

[0480] "Interest-based product information" refers to information about products or services related to a user's specific areas of interest, and is provided based on the user's past behavior and preferences.

[0481] In an embodiment of this invention, the server first obtains necessary information from the user's email. This includes a process of collecting email data from the mail server using an API, based on the access rights previously authenticated by the user. At this time, the server uses filtering technology to identify event-related information from the email and extracts that data.

[0482] Next, the server analyzes the extracted data using natural language processing techniques. The software used for this is a natural language processing library such as SpaCy or NLTK, which is used to extract detailed information such as event name, date, location, time, and seat number. The analyzed information is stored in an information base by the server and listed so that users can easily access it.

[0483] Subsequently, the server uses Bluetooth and beacon technology to detect the user's location. For example, by using Bluetooth beacons, it can confirm whether the user has visited a physical store and immediately notify the device of product information based on their interests. This information includes product recommendations based on the user's past purchase history and interests. Specifically, it is possible to query external promotional information APIs via HTTP requests using the Python requests library and provide real-time information.

[0484] For example, consider a situation where a user plans to attend a concert by artist A, and related merchandise has just arrived in a store. In this case, the system can notify the user of this information on their device to pique their interest. Furthermore, if the user is searching for a specific product or service, a relevant prompt might be, "Generate a notification suggesting related products based on the event the user plans to attend."

[0485] In this way, users can quickly receive all relevant information when participating in an event, leading to a more fulfilling experience.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server makes an API call to the user's email server to retrieve emails related to the event. The input is the user's email account information, and the output is the retrieved email data. This data includes information such as the subject, body, sender, and date and time of sending.

[0489] Step 2:

[0490] The server filters the retrieved emails, specifically identifying event-related emails. This step involves detecting specific keywords in the email subject and body, and using them to select event emails. The input is the retrieved email data, and the output is the event-related email data.

[0491] Step 3:

[0492] The server analyzes filtered event emails using natural language processing techniques. Specifically, it uses SpaCy and NLTK to extract event names, dates, locations, times, and seat information from the email body. The input is filtered email data, and the output is detailed event information.

[0493] Step 4:

[0494] The server registers and lists detailed information about the analyzed events in an information base. The information base is built using a database system, and the information is stored in a table format. The input is the detailed event information, and the output is the updated information base.

[0495] Step 5:

[0496] The server detects the location of the user's device using a Bluetooth beacon. When the device enters the beacon's range, it collects the user's location information. The input is signal data from the beacon, and the output is the user's current location information.

[0497] Step 6:

[0498] The server generates relevant product information based on the user's current location and interests, and notifies the device. This process uses the Python requests library to retrieve promotional data from an external API, processing it to match the user's interests before sending the notification. The input is the user's current location and interests, and the output is the notification content.

[0499] 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.

[0500] This invention provides a more personalized experience by incorporating an emotion engine into an event ticket management system that utilizes users' email addresses. The system operates with three main components: a server, user terminals, and users.

[0501] First, the server accesses the user's email inbox and retrieves new emails. The retrieved emails are analyzed using natural language processing technology to extract detailed event information. This information is stored in a database, and the user can check their event schedule through their device.

[0502] In addition, this invention uses an emotion engine to recognize the user's emotions and adjusts the content provided based on analyzed event information and past history. The emotion engine infers the emotional state from the user's input and past interactions, and plays a role in optimizing the overall system response.

[0503] Specifically, the server evaluates the user's emotions through an emotion engine and sends notifications tailored to that state. The timing and content of the notifications are adjusted to the user's preferences, and event recommendations are also personalized as needed. This process ensures that users receive the most relevant information at the most appropriate time.

[0504] As an example, consider the case where user B purchases a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in the database. At this time, the emotion engine analyzes user B's past behavior and preferences to determine their emotional state. As the event date approaches, the server sends a reminder to user B's terminal that is tailored to their emotional state. If user B prefers to attend the event in a more relaxed state, the reminder content and additional information are adjusted. This allows user B to enjoy the event in a way that best suits their emotional state.

[0505] Thus, the present invention provides a means for optimizing the user's event participation experience by making full use of an emotion engine.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server obtains the user's permission to access their email inbox and download new emails. During this process, the server uses either the IMAP or POP3 protocol to retrieve emails from the specified folder.

[0509] Step 2:

[0510] The server filters downloaded emails. The filtering criteria include extracting emails that contain specific keywords (e.g., "tickets" or "reservations") in the subject or body.

[0511] Step 3:

[0512] The server analyzes filtered emails using natural language processing technology to extract information related to the event. This information includes the event name, date and time, location, and seat number.

[0513] Step 4:

[0514] The server registers the extracted event information in a database. This organizes the event information and saves it in a format that can be accessed later.

[0515] Step 5:

[0516] The user's terminal accesses the database and displays a list of event information related to the user. Through this interface, the user can intuitively check all their appointments.

[0517] Step 6:

[0518] The server uses a sentiment engine to infer the user's emotional state based on past user interaction data and input information. This includes data such as keyboard input, operation speed, and past selection history.

[0519] Step 7:

[0520] Based on the emotional state estimated by the emotion engine, the server adjusts the content and timing of notifications before sending them. For example, if a relaxed emotional state is estimated, a notification with ample time to spare will be sent.

[0521] Step 8:

[0522] When a user selects a ticket they wish to resell, they send a resale request to the server via their device. The server processes this request, lists the ticket on the appropriate resale platform, and provides information to potential buyers.

[0523] This system is designed to provide information that resonates with the user's emotions and improve the user experience.

[0524] (Example 2)

[0525] 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."

[0526] In today's world, users receive a vast amount of electronic communication documents daily, making it difficult to efficiently extract and manage useful event information from them. Furthermore, providing appropriate information tailored to users' emotions and preferences is a challenging task for typical systems. This project aims to improve this situation and realize information delivery optimized for individual users.

[0527] 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.

[0528] In this invention, the server includes means for automatically acquiring the user's electronic communication documents, means for analyzing the content of the acquired electronic communication documents to extract event information, and means for analyzing the user's emotional state using emotion evaluation technology and adjusting the notification content. This enables efficient management of event information useful to the user and the provision of personalized information according to emotions and preferences.

[0529] "Electronic communication documents" refer to digital communication information sent and received via email or messaging applications.

[0530] "Event information" refers to detailed data about a specific event or activity, including information such as the date, time, location, and participation requirements.

[0531] An "information storage device" is a collection of hardware or software that stores digital information and makes it accessible later.

[0532] "Emotional assessment technology" is a technology that analyzes a user's verbal expressions and behavioral data to infer or judge their emotional state.

[0533] A "notification" is a message or alert sent to inform a user of specific information.

[0534] The "optimal choice" refers to suggestions and recommendations that best match the user's preferences and needs.

[0535] This invention achieves personalized information presentation by incorporating sentiment evaluation technology into an event information management system that uses users' electronic communication documents. The system operates with three main components: a server, a user terminal, and the user.

[0536] The server accesses the user's electronic communications documents and retrieves new documents. This includes using the mail server's API. The retrieved documents are parsed using tools such as the Google Cloud Natural Language API, and event information is extracted. This information is stored in data storage devices such as MySQL or PostgreSQL.

[0537] Furthermore, the server uses sentiment assessment technology based on the analyzed event information. Specifically, it utilizes software such as IBM Watson Tone Analyzer to infer the user's emotional state from their past data and document content. Based on this inferred sentiment information, notifications are generated at the optimal time for the user. The notification content is adjusted according to the user's emotional state and sent to the terminal.

[0538] As an example, consider a user purchasing a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in its data storage. After sentiment evaluation technology analyzes the user's past behavior and preferences, as the event approaches, the server sends a reminder to the user's device that is tailored to their emotional state. For users who want to relax, this might include suggestions for relaxing music or activities.

[0539] An example of a prompt might be: "Suggest a reminder tailored to the user's emotional state to optimize their participation in a music event. Assume user B prefers a relaxed state."

[0540] This type of system allows users to receive optimized information tailored to their emotions and preferences, thereby improving their event participation experience.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server uses an API to retrieve the user's electronic communications documents. The input is the user's mailbox, and the output is the retrieved raw message data. The server uses polling and real-time push notifications to detect when an email has arrived.

[0544] Step 2:

[0545] The server converts the acquired message data into text format and sends it to the natural language processing engine. The input is raw message data, and the output is text data. Specifically, this involves extracting text and metadata from the message format.

[0546] Step 3:

[0547] The server utilizes a natural language processing engine to analyze and extract event information from text data. The input is text data, and the output is structured data such as event name, date and time, and location. Here, natural language processing techniques are used to analyze specific keywords and context.

[0548] Step 4:

[0549] The server records the extracted event information in its information storage device. The input is the parsed event information, and the output is a success message for the database entry. Specifically, this involves inserting records into the database using SQL statements.

[0550] Step 5:

[0551] The server retrieves data from an information storage device and evaluates the user's emotional state using sentiment assessment technology. The input consists of past event information and email content, while the output is evaluation information indicating the user's emotional state. The server uses pattern recognition technology to infer emotions.

[0552] Step 6:

[0553] The server generates optimized notifications based on the user's emotional state and event information, and sends them to the device. The input is emotional state evaluation information and event information, and the output is the notification displayed on the user's device. Here, the content and timing of the notification are adjusted, and it is sent via push notification or email.

[0554] Step 7:

[0555] The device displays received notifications to the user. The input is the notification message from the server, and the output is a visual or audible notification displayed in the user interface. Specifically, it adds a new item to the device's notification center and provides an alert to the user.

[0556] (Application Example 2)

[0557] 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."

[0558] In modern times, food delivery services are used by many people, but they lack personalized suggestions that take into account the user's emotional state. Traditional systems have struggled to consider a user's order history and emotional state to provide the optimal menu and delivery options. As a result, users are unable to receive services that match their emotional state and preferences at any given time.

[0559] 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.

[0560] In this invention, the server includes means for automatically acquiring user information, means for analyzing the content of the acquired information and extracting activity information, and means for evaluating the user's emotional state using emotion analysis means and personalizing information appropriate to the user's state. This makes it possible to recommend the most suitable food menu and delivery options according to the user's emotional state.

[0561] "User" refers to an individual or group that uses this system and is the subject of information acquisition, activity information extraction, and sentiment analysis.

[0562] "Information" refers to data related to a user, existing in the form of emails, order history, or other digital communications.

[0563] "Activity information" refers to data related to events and orders extracted by analyzing the content of the acquired information.

[0564] A "data storage device" is a physical or virtual device used to systematically register and manage data such as activity information.

[0565] "Communication" refers to the method of transmitting information from a system to a user, and can take the form of email or push notifications.

[0566] "Emotional analysis methods" refer to technologies that use information obtained from users to evaluate the user's emotional state through natural language processing and other analytical techniques.

[0567] "Options" refer to multiple possible actions or suggestions presented to the user, allowing them to choose the one that is most suitable.

[0568] The system for implementing this invention mainly consists of a server, a user terminal, and the user. The server is responsible for automatically acquiring user information, which includes data such as the user's emails and order history. The server utilizes natural language processing technology to extract activity information using the acquired information. Specifically, the Google Cloud Natural Language API may be used. Through this natural language processing technology, the server can analyze the content of the information and extract relevant activity data.

[0569] Next, the server uses sentiment analysis. Sentiment analysis is a technique that estimates a user's emotional state from their past behavior and current information, and Microsoft Azure's Text Analytics for Sentiment Analysis can be used. This analysis evaluates the user's emotional state and generates data to adjust the content provided based on the results.

[0570] The user's device receives information sent from the server and performs optimized communication with the user. This includes providing information via push notifications and in-app messages.

[0571] As a concrete example, if a user is experiencing a particular emotion at the beginning of the week, the system will analyze the results and offer suggestions for relaxation. For instance, if the emotion engine detects that the user is feeling stressed, the user's device will be notified of an option to receive a free relaxing herbal tea.

[0572] An example of a prompt might be, "If the user's emotional state is one of relaxation, what menu item would you recommend?" This prompt uses a generative AI model to generate suggestions that best suit the user's needs.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The server accesses the user's email account and retrieves new emails. In this process, the server takes unprocessed emails received by the user as input. It receives this email data as input and saves its contents.

[0576] Step 2:

[0577] The server analyzes the content of the retrieved emails using natural language processing techniques. This step uses technologies such as the Google Cloud Natural Language API to extract event-related activity information from the email body. The output of this process generates information about the extracted events and orders.

[0578] Step 3:

[0579] The server registers the extracted activity information in the data storage device and organizes it as configuration information. The input for this step is the activity information obtained in the previous step, and the output is the configured information registered in the database. This information is also listed so that the user can check it later.

[0580] Step 4:

[0581] The server evaluates the user's emotional state using sentiment analysis tools. The input for this step is the user's past behavior history and current order information, which is analyzed using Microsoft Azure's Text Analytics. The output generates data representing the user's emotional state.

[0582] Step 5:

[0583] The server generates and sends user-appropriate communications based on the sentiment analysis results to the user's device. The input consists of the sentiment analysis results and registered activity information, while the output is a personalized notification sent to the user's device. This notification is displayed as a push notification or in-app message.

[0584] Step 6:

[0585] The user's device receives the sent notification and presents it to the user. The input for this process is notification data from the server, and the output is recommended information and perks options presented to the user. For example, a user seeking relaxation might be presented with menu options that have a relaxing effect.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention is a system for efficiently managing tickets for entertainment events, in which three elements—a server, a user terminal, and the user—work together. First, the server accesses the user's email inbox. Based on the access rights previously granted by the user, the server retrieves emails and filters them to include ticket-related emails based on specific keywords.

[0604] Next, the server analyzes the filtered emails using natural language processing techniques to extract important information such as the event name, date, location, time, and seat information. The extracted information is then registered in a database by the server and stored in an organized format. This database is kept accessible to users via their terminals. Users can log in to view their ticket list.

[0605] Furthermore, the server has a function to send reminder notifications as the event date approaches. These notifications are delivered to the user's device via email or push notification, prompting the user to take the necessary action.

[0606] In addition, the server analyzes the user's past event participation history and makes suggestions recommending the best seat for the next event reservation. These suggestions are calculated based on interpreted database information and contribute to improving user convenience.

[0607] If a user is unable to attend an event, they can use their device to notify the server of their desire to resell the ticket. The server will then provide the resale ticket information to other potential buyers and support the successful completion of a transaction.

[0608] As a concrete example, suppose user A purchases a musical ticket online. The server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the performance date approaches, the server sends a reminder to user A's device, prompting them to prepare to attend. In the event that user A is unable to attend, they can send a resale request to the server, allowing the ticket to be sold to another user, B. This allows user A to manage and utilize their ticket without unnecessary stress.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server accesses the email inbox based on the user's permission and retrieves new emails. This involves communicating with the email server using the IMAP or POP3 protocol and downloading the emails.

[0612] Step 2:

[0613] The server filters the retrieved emails using specific keywords in the subject line and body to identify emails related to tickets. This ensures that only emails related to tickets are displayed.

[0614] Step 3:

[0615] The server analyzes filtered emails using natural language processing techniques. Information such as event name, date, time, location, and seat number is extracted from the email body.

[0616] Step 4:

[0617] The server registers the extracted event information in a database. The information is organized and stored for each user, and structured in a way that allows for later access.

[0618] Step 5:

[0619] The user's terminal displays a list of tickets based on information received from the server. The user then uses this list to check their event participation schedule.

[0620] Step 6:

[0621] The server monitors the event date and automatically generates and sends a reminder notification a few days before the scheduled date. The notification is received on the user's device, prompting the user to prepare for the event.

[0622] Step 7:

[0623] The server analyzes the user's past event participation history to calculate the optimal seat for new reservations. The analysis results are notified to the user's device and provided as reference information when making reservations.

[0624] Step 8:

[0625] The user selects the ticket they wish to resell and sends a resale request to the server. The server processes this information and lists it on the appropriate resale platform to provide it to potential buyers.

[0626] (Example 1)

[0627] 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".

[0628] To improve user convenience in managing event tickets, automated organization of digitized information, optimal seat recommendations, and resale support for those unable to attend are necessary. However, traditional methods often involve manual processes, requiring users to proactively manage their own communication records, which is burdensome. This has resulted in a lower quality user experience and made event participation cumbersome.

[0629] 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.

[0630] In this invention, the server includes means for automatically accessing and acquiring user communication records, means for analyzing the acquired communication records to extract event information, means for registering and listing the extracted event information in an information aggregation device, means for sending notifications to the user based on the event information, means for providing information on tickets that the user wishes to resell to other prospective buyers, and means for analyzing the user's past event participation history to recommend the best seat for the next event. This enables the user to efficiently and comfortably participate in events and manage their tickets.

[0631] "Communication records" refer to a collection of digital information, including a user's email and message history.

[0632] "Event information" refers to details such as the event name, date, location, time, and seating arrangements.

[0633] An "information storage device" refers to a system or database for storing and managing data.

[0634] "Notifications" refer to messages or alerts used to inform users of information.

[0635] "Intending to resell" refers to a situation where a user intends to sell their admission ticket to someone else.

[0636] "Ticket information" refers to information about digital or physical identification that grants permission to participate in a specific event.

[0637] "Optimal seating" refers to the ideal seating location at an event, recommended based on the user's past history and preferences.

[0638] This invention is an information processing system for efficiently managing user event admission tickets. The server accesses the user's communication records and automatically retrieves relevant information. This process involves retrieving data from a mail server using the IMAP protocol. The server also uses natural language processing (NLP) techniques to analyze the retrieved communication records and extract event information. This analysis utilizes a script written in Python and an NLP library (e.g., SpaCy).

[0639] The extracted event information is registered in an SQL database by the server and stored in an organized format. MySQL is used as the specific database system. Users can access this database via their terminals to check their admission ticket information. The user interface is provided as a web portal using PHP and JavaScript.

[0640] As an event approaches, the server sends a reminder notification to the user. This notification is delivered via email or push notification. Firebase Cloud Messaging is used for push notifications. Furthermore, the server analyzes past event attendance history to recommend the best seat for future event reservations. This analysis utilizes machine learning techniques based on historical data. For example, when user A purchases a musical ticket online, the server detects the purchase confirmation email, analyzes the event information, and adds it to a database. As the day of the event approaches, the server sends a reminder to the user's device to encourage preparation.

[0641] If a user is unable to attend an event, they can notify the server via their device of their desire to resell their ticket. The server then provides this resale information to other potential buyers, and the ticket is resold through an e-commerce platform. This functionality allows users to manage and use their tickets without any hassle.

[0642] An example of a prompt to a generative AI model would be: "Analyze the musical ticket confirmation email and extract the event name, date, location, time, and seat information." This would initiate the server's process of retrieving the appropriate information and saving it in a well-organized format.

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

[0644] Step 1:

[0645] The server accesses and retrieves the user's communication records. The input is the user's mail server account information, and the output is the data of the received emails. The server uses the IMAP protocol to log in to the specified email account and retrieve email information from the inbox. As a result, the complete text of the emails is collected on the server.

[0646] Step 2:

[0647] The server filters the acquired email data to identify emails containing event information. The input is all email data acquired in step 1, and the output is event-related email data. The server filters the emails using specific keywords (e.g., "ticket purchase confirmation," "event," etc.) to extract only relevant emails. This narrows down the target of subsequent analysis.

[0648] Step 3:

[0649] The server analyzes filtered emails using natural language processing (NLP) techniques to extract event information. The input is the filtered email data obtained in step 2, and the output is structured event information such as event name, date, location, time, and seating information. The server analyzes the email body using an NLP library (e.g., SpaCy) to extract this data. Specifically, it uses text parsing and regular expressions to identify the necessary information.

[0650] Step 4:

[0651] The server extracts event information and registers it in an information aggregation device, i.e., a database. The input is the structured event information obtained in step 3, and the output is the event information stored in the database. The server uses SQL statements to insert the data into the MySQL database. This ensures that the event information is stored in an organized format, allowing users to review it later.

[0652] Step 5:

[0653] Users access and view event information via their devices. Input is login credentials, and output is a list of their own event information. Users log in to the web portal and view the event information displayed on the screen. This interface is built with PHP and JavaScript, enabling smooth information searching and display.

[0654] Step 6:

[0655] The server sends a reminder notification as the event date approaches. The input is the event information and current date registered in the database, and the output is the reminder notification sent to the user. The server periodically checks the database and creates and sends a reminder a few days before the event. The notification is delivered as an email or push notification, using Firebase Cloud Messaging for push notifications.

[0656] Step 7:

[0657] The server analyzes the user's past event attendance history and recommends the best seat for the next event. The input is the past attendance history stored in the database, and the output is recommended seating information. The server uses machine learning algorithms to analyze past historical data and calculate the best seat for the next event based on trends. This process allows users to have a better event experience.

[0658] Step 8:

[0659] If a user is unable to participate, they notify the server of their resale request via their device. The input is the user's resale request information, and the output is the resale information to other potential buyers. The server accepts the resale request, registers the relevant information on the resale platform, and provides it to other potential buyers, thereby facilitating a quick resale.

[0660] (Application Example 1)

[0661] 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".

[0662] While systems existed to manage event tickets held by users, they were limited to managing ticket information and lacked the functionality to provide relevant product information based on the user's location and interests. Furthermore, features such as resale and optimal seating recommendations were limited, failing to adequately enhance user convenience. As a result, users were unable to obtain relevant product information until just before the event, making it difficult for them to take appropriate action.

[0663] 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.

[0664] In this invention, the server includes means for automatically accessing and acquiring user information, means for analyzing the acquired information and extracting activity information, means for registering and listing the extracted activity information in an information base, and means for detecting the user's location and transmitting product information based on their interests. This enables users to receive event-related information and products in a timely manner, thereby improving the event experience.

[0665] "User information" refers to electronic data related to a user, including, in particular, their email address and location information.

[0666] "Activity information" refers to events and related information that users are interested in, and specifically includes the event name, date, location, time, and seating information.

[0667] An "information base" is a database for storing extracted activity information, organized in a format accessible to users.

[0668] "Users" refers to individuals or groups who utilize this system and who have specific interests or concerns.

[0669] "Means of detecting location" refers to technologies for determining the user's current location, such as GPS and beacon technology, which are used to acquire location information.

[0670] "Interest-based product information" refers to information about products or services related to a user's specific areas of interest, and is provided based on the user's past behavior and preferences.

[0671] In an embodiment of this invention, the server first obtains necessary information from the user's email. This includes a process of collecting email data from the mail server using an API, based on the access rights previously authenticated by the user. At this time, the server uses filtering technology to identify event-related information from the email and extracts that data.

[0672] Next, the server analyzes the extracted data using natural language processing techniques. The software used for this is a natural language processing library such as SpaCy or NLTK, which is used to extract detailed information such as event name, date, location, time, and seat number. The analyzed information is stored in an information base by the server and listed so that users can easily access it.

[0673] Subsequently, the server uses Bluetooth and beacon technology to detect the user's location. For example, by using Bluetooth beacons, it can confirm whether the user has visited a physical store and immediately notify the device of product information based on their interests. This information includes product recommendations based on the user's past purchase history and interests. Specifically, it is possible to query external promotional information APIs via HTTP requests using the Python requests library and provide real-time information.

[0674] For example, consider a situation where a user plans to attend a concert by artist A, and related merchandise has just arrived in a store. In this case, the system can notify the user of this information on their device to pique their interest. Furthermore, if the user is searching for a specific product or service, a relevant prompt might be, "Generate a notification suggesting related products based on the event the user plans to attend."

[0675] In this way, users can quickly receive all relevant information when participating in an event, leading to a more fulfilling experience.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server makes an API call to the user's email server to retrieve emails related to the event. The input is the user's email account information, and the output is the retrieved email data. This data includes information such as the subject, body, sender, and date and time of sending.

[0679] Step 2:

[0680] The server filters the retrieved emails, specifically identifying event-related emails. This step involves detecting specific keywords in the email subject and body, and using them to select event emails. The input is the retrieved email data, and the output is the event-related email data.

[0681] Step 3:

[0682] The server analyzes filtered event emails using natural language processing techniques. Specifically, it uses SpaCy and NLTK to extract event names, dates, locations, times, and seat information from the email body. The input is filtered email data, and the output is detailed event information.

[0683] Step 4:

[0684] The server registers and lists detailed information about the analyzed events in an information base. The information base is built using a database system, and the information is stored in a table format. The input is the detailed event information, and the output is the updated information base.

[0685] Step 5:

[0686] The server detects the location of the user's device using a Bluetooth beacon. When the device enters the beacon's range, it collects the user's location information. The input is signal data from the beacon, and the output is the user's current location information.

[0687] Step 6:

[0688] The server generates relevant product information based on the user's current location and interests, and notifies the device. This process uses the Python requests library to retrieve promotional data from an external API, processing it to match the user's interests before sending the notification. The input is the user's current location and interests, and the output is the notification content.

[0689] 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.

[0690] This invention provides a more personalized experience by incorporating an emotion engine into an event ticket management system that utilizes users' email addresses. The system operates with three main components: a server, user terminals, and users.

[0691] First, the server accesses the user's email inbox and retrieves new emails. The retrieved emails are analyzed using natural language processing technology to extract detailed event information. This information is stored in a database, and the user can check their event schedule through their device.

[0692] In addition, this invention uses an emotion engine to recognize the user's emotions and adjusts the content provided based on analyzed event information and past history. The emotion engine infers the emotional state from the user's input and past interactions, and plays a role in optimizing the overall system response.

[0693] Specifically, the server evaluates the user's emotions through an emotion engine and sends notifications tailored to that state. The timing and content of the notifications are adjusted to the user's preferences, and event recommendations are also personalized as needed. This process ensures that users receive the most relevant information at the most appropriate time.

[0694] As an example, consider the case where user B purchases a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in the database. At this time, the emotion engine analyzes user B's past behavior and preferences to determine their emotional state. As the event date approaches, the server sends a reminder to user B's terminal that is tailored to their emotional state. If user B prefers to attend the event in a more relaxed state, the reminder content and additional information are adjusted. This allows user B to enjoy the event in a way that best suits their emotional state.

[0695] Thus, the present invention provides a means for optimizing the user's event participation experience by making full use of an emotion engine.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server obtains the user's permission to access their email inbox and download new emails. During this process, the server uses either the IMAP or POP3 protocol to retrieve emails from the specified folder.

[0699] Step 2:

[0700] The server filters downloaded emails. The filtering criteria include extracting emails that contain specific keywords (e.g., "tickets" or "reservations") in the subject or body.

[0701] Step 3:

[0702] The server analyzes filtered emails using natural language processing technology to extract information related to the event. This information includes the event name, date and time, location, and seat number.

[0703] Step 4:

[0704] The server registers the extracted event information in a database. This organizes the event information and saves it in a format that can be accessed later.

[0705] Step 5:

[0706] The user's terminal accesses the database and displays a list of event information related to the user. Through this interface, the user can intuitively check all their appointments.

[0707] Step 6:

[0708] The server uses a sentiment engine to infer the user's emotional state based on past user interaction data and input information. This includes data such as keyboard input, operation speed, and past selection history.

[0709] Step 7:

[0710] Based on the emotional state estimated by the emotion engine, the server adjusts the content and timing of notifications before sending them. For example, if a relaxed emotional state is estimated, a notification with ample time to spare will be sent.

[0711] Step 8:

[0712] When a user selects a ticket they wish to resell, they send a resale request to the server via their device. The server processes this request, lists the ticket on the appropriate resale platform, and provides information to potential buyers.

[0713] This system is designed to provide information that resonates with the user's emotions and improve the user experience.

[0714] (Example 2)

[0715] 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".

[0716] In today's world, users receive a vast amount of electronic communication documents daily, making it difficult to efficiently extract and manage useful event information from them. Furthermore, providing appropriate information tailored to users' emotions and preferences is a challenging task for typical systems. This project aims to improve this situation and realize information delivery optimized for individual users.

[0717] 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.

[0718] In this invention, the server includes means for automatically acquiring the user's electronic communication documents, means for analyzing the content of the acquired electronic communication documents to extract event information, and means for analyzing the user's emotional state using emotion evaluation technology and adjusting the notification content. This enables efficient management of event information useful to the user and the provision of personalized information according to emotions and preferences.

[0719] "Electronic communication documents" refer to digital communication information sent and received via email or messaging applications.

[0720] "Event information" refers to detailed data about a specific event or activity, including information such as the date, time, location, and participation requirements.

[0721] An "information storage device" is a collection of hardware or software that stores digital information and makes it accessible later.

[0722] "Emotional assessment technology" is a technology that analyzes a user's verbal expressions and behavioral data to infer or judge their emotional state.

[0723] A "notification" is a message or alert sent to inform a user of specific information.

[0724] The "optimal choice" refers to suggestions and recommendations that best match the user's preferences and needs.

[0725] This invention achieves personalized information presentation by incorporating sentiment evaluation technology into an event information management system that uses users' electronic communication documents. The system operates with three main components: a server, a user terminal, and the user.

[0726] The server accesses the user's electronic communications documents and retrieves new documents. This includes using the mail server's API. The retrieved documents are parsed using tools such as the Google Cloud Natural Language API, and event information is extracted. This information is stored in data storage devices such as MySQL or PostgreSQL.

[0727] Furthermore, the server uses sentiment assessment technology based on the analyzed event information. Specifically, it utilizes software such as IBM Watson Tone Analyzer to infer the user's emotional state from their past data and document content. Based on this inferred sentiment information, notifications are generated at the optimal time for the user. The notification content is adjusted according to the user's emotional state and sent to the terminal.

[0728] As an example, consider a user purchasing a ticket to a music event. The server analyzes the purchase confirmation email and registers the event information in its data storage. After sentiment evaluation technology analyzes the user's past behavior and preferences, as the event approaches, the server sends a reminder to the user's device that is tailored to their emotional state. For users who want to relax, this might include suggestions for relaxing music or activities.

[0729] An example of a prompt might be: "Suggest a reminder tailored to the user's emotional state to optimize their participation in a music event. Assume user B prefers a relaxed state."

[0730] This type of system allows users to receive optimized information tailored to their emotions and preferences, thereby improving their event participation experience.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The server uses an API to retrieve the user's electronic communications documents. The input is the user's mailbox, and the output is the retrieved raw message data. The server uses polling and real-time push notifications to detect when an email has arrived.

[0734] Step 2:

[0735] The server converts the acquired message data into text format and sends it to the natural language processing engine. The input is raw message data, and the output is text data. Specifically, this involves extracting text and metadata from the message format.

[0736] Step 3:

[0737] The server utilizes a natural language processing engine to analyze and extract event information from text data. The input is text data, and the output is structured data such as event name, date and time, and location. Here, natural language processing techniques are used to analyze specific keywords and context.

[0738] Step 4:

[0739] The server records the extracted event information in its information storage device. The input is the parsed event information, and the output is a success message for the database entry. Specifically, this involves inserting records into the database using SQL statements.

[0740] Step 5:

[0741] The server retrieves data from an information storage device and evaluates the user's emotional state using sentiment assessment technology. The input consists of past event information and email content, while the output is evaluation information indicating the user's emotional state. The server uses pattern recognition technology to infer emotions.

[0742] Step 6:

[0743] The server generates optimized notifications based on the user's emotional state and event information, and sends them to the device. The input is emotional state evaluation information and event information, and the output is the notification displayed on the user's device. Here, the content and timing of the notification are adjusted, and it is sent via push notification or email.

[0744] Step 7:

[0745] The device displays received notifications to the user. The input is the notification message from the server, and the output is a visual or audible notification displayed in the user interface. Specifically, it adds a new item to the device's notification center and provides an alert to the user.

[0746] (Application Example 2)

[0747] 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".

[0748] In modern times, food delivery services are used by many people, but they lack personalized suggestions that take into account the user's emotional state. Traditional systems have struggled to consider a user's order history and emotional state to provide the optimal menu and delivery options. As a result, users are unable to receive services that match their emotional state and preferences at any given time.

[0749] 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.

[0750] In this invention, the server includes means for automatically acquiring user information, means for analyzing the content of the acquired information and extracting activity information, and means for evaluating the user's emotional state using emotion analysis means and personalizing information appropriate to the user's state. This makes it possible to recommend the most suitable food menu and delivery options according to the user's emotional state.

[0751] "User" refers to an individual or group that uses this system and is the subject of information acquisition, activity information extraction, and sentiment analysis.

[0752] "Information" refers to data related to a user, existing in the form of emails, order history, or other digital communications.

[0753] "Activity information" refers to data related to events and orders extracted by analyzing the content of the acquired information.

[0754] A "data storage device" is a physical or virtual device used to systematically register and manage data such as activity information.

[0755] "Communication" refers to the method of transmitting information from a system to a user, and can take the form of email or push notifications.

[0756] "Emotional analysis methods" refer to technologies that use information obtained from users to evaluate the user's emotional state through natural language processing and other analytical techniques.

[0757] "Options" refer to multiple possible actions or suggestions presented to the user, allowing them to choose the one that is most suitable.

[0758] The system for implementing this invention mainly consists of a server, a user terminal, and the user. The server is responsible for automatically acquiring user information, which includes data such as the user's emails and order history. The server utilizes natural language processing technology to extract activity information using the acquired information. Specifically, the Google Cloud Natural Language API may be used. Through this natural language processing technology, the server can analyze the content of the information and extract relevant activity data.

[0759] Next, the server uses sentiment analysis. Sentiment analysis is a technique that estimates a user's emotional state from their past behavior and current information, and Microsoft Azure's Text Analytics for Sentiment Analysis can be used. This analysis evaluates the user's emotional state and generates data to adjust the content provided based on the results.

[0760] The user's device receives information sent from the server and performs optimized communication with the user. This includes providing information via push notifications and in-app messages.

[0761] As a concrete example, if a user is experiencing a particular emotion at the beginning of the week, the system will analyze the results and offer suggestions for relaxation. For instance, if the emotion engine detects that the user is feeling stressed, the user's device will be notified of an option to receive a free relaxing herbal tea.

[0762] An example of a prompt might be, "If the user's emotional state is one of relaxation, what menu item would you recommend?" This prompt uses a generative AI model to generate suggestions that best suit the user's needs.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server accesses the user's email account and retrieves new emails. In this process, the server takes unprocessed emails received by the user as input. It receives this email data as input and saves its contents.

[0766] Step 2:

[0767] The server analyzes the content of the retrieved emails using natural language processing techniques. This step uses technologies such as the Google Cloud Natural Language API to extract event-related activity information from the email body. The output of this process generates information about the extracted events and orders.

[0768] Step 3:

[0769] The server registers the extracted activity information in the data storage device and organizes it as configuration information. The input for this step is the activity information obtained in the previous step, and the output is the configured information registered in the database. This information is also listed so that the user can check it later.

[0770] Step 4:

[0771] The server evaluates the user's emotional state using sentiment analysis tools. The input for this step is the user's past behavior history and current order information, which is analyzed using Microsoft Azure's Text Analytics. The output generates data representing the user's emotional state.

[0772] Step 5:

[0773] The server generates and sends user-appropriate communications based on the sentiment analysis results to the user's device. The input consists of the sentiment analysis results and registered activity information, while the output is a personalized notification sent to the user's device. This notification is displayed as a push notification or in-app message.

[0774] Step 6:

[0775] The user's device receives the sent notification and presents it to the user. The input for this process is notification data from the server, and the output is recommended information and perks options presented to the user. For example, a user seeking relaxation might be presented with menu options that have a relaxing effect.

[0776] 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.

[0777] 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.

[0778] 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 robot 414.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] Means for automatically accessing and obtaining a user's email address,

[0800] A means of analyzing the content of acquired emails and extracting event information,

[0801] A means of registering the extracted event information in a database and listing it,

[0802] A means for sending a notification to the user based on the aforementioned event information,

[0803] A means of providing information about tickets intended for resale to other potential buyers,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which utilizes natural language processing technology to improve the accuracy of the analysis.

[0807] (Claim 3)

[0808] The system according to claim 1, which has a function to recommend the optimal seat based on analyzed event information.

[0809] "Example 1"

[0810] (Claim 1)

[0811] A means of automatically accessing and obtaining user communication records,

[0812] A means of analyzing the contents of acquired communication records to extract event information,

[0813] A means for registering extracted event information in an information aggregation device and listing it,

[0814] A means of sending a notification to the user based on the aforementioned event information,

[0815] A means of providing information on tickets intended for resale to other potential buyers,

[0816] A means of analyzing the user's past event participation history and recommending the most suitable seat for the next event,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, which utilizes natural language processing technology to improve the accuracy of the analysis.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the recommendation of the optimal seat is performed using a generation technology.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] Means for automatically accessing and obtaining user information,

[0825] A means of analyzing acquired information and extracting activity information,

[0826] A means of registering extracted activity information in an information base and listing it,

[0827] A means for transmitting information to the user based on the aforementioned activity information,

[0828] A means of providing information on tickets that you wish to resell to other potential buyers,

[0829] A means for detecting the user's location and transmitting product information based on their interests,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which improves analysis accuracy by utilizing natural language processing technology.

[0833] (Claim 3)

[0834] The system according to claim 1, which has a function to recommend the optimal seat based on analyzed activity information and recommends products based on the user's interests.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] A means of automatically acquiring the user's electronic communication documents,

[0838] A means of analyzing the contents of acquired electronic communication documents to extract information about events,

[0839] A means of recording and listing the extracted event information in an information storage device,

[0840] A means of sending a notification to the user based on the information of the aforementioned event,

[0841] A means of analyzing the user's emotional state using emotion evaluation technology and adjusting the content of notifications,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, which utilizes natural language processing technology to improve the accuracy of the analysis.

[0845] (Claim 3)

[0846] The system according to claim 1, which has the function of presenting the optimal choice based on the information of the analyzed events.

[0847] "Application example 2 when combining with an emotional engine"

[0848] (Claim 1)

[0849] A means of automatically acquiring user information,

[0850] A means of analyzing the content of the acquired information and extracting activity information,

[0851] A means for registering extracted activity information in a data storage device and listing it,

[0852] A means for transmitting a communication to the user based on the aforementioned activity information,

[0853] A means of evaluating the user's emotional state using emotion analysis methods and personalizing information appropriate to the user's state,

[0854] A means of providing other potential buyers with a request to alter activity information,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which utilizes natural language processing technology to improve the accuracy of the analysis and further incorporates sentiment analysis techniques.

[0858] (Claim 3)

[0859] The system according to claim 1, which has a function to recommend the optimal choice based on analyzed activity information and the user's emotional state. [Explanation of symbols]

[0860] 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. Means for automatically accessing and obtaining a user's email address, A means of analyzing the content of acquired emails and extracting event information, A means of registering the extracted event information in a database and listing it, A means for sending a notification to the user based on the aforementioned event information, A means of providing information about tickets intended for resale to other potential buyers, A system that includes this.

2. The system according to claim 1, which utilizes natural language processing technology to improve the accuracy of the analysis.

3. The system according to claim 1, which has a function to recommend the optimal seat based on analyzed event information.

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