system

Abstract

A system is provided.SOLUTION: The system includes a means for monitoring an operation situation in real time from a database of an operation organization or a public API, a means for storing acquired flight cancellation information in the database and collating the flight cancellation information with reservation data of a corresponding user, a means for transmitting a notification of the flight cancellation information to the corresponding user, a means for collecting and analyzing vacancy information and operation information from a plurality of operation organizations in real time, a means for proposing a generated alternative means, and a means for confirming a new reservation selected by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention relates to a system for reducing the hassle and stress experienced by users when a flight is canceled. Conventional methods require users to check the websites of multiple operators and arrange appropriate alternative flights, which requires a great deal of time and effort, especially when the operator's call center or counter is overwhelmed with flight cancellations. Furthermore, the resulting stress can increase user dissatisfaction and reduce the operator's satisfaction with the service. Therefore, there is a need to streamline the arrangement of alternative flights when a flight is canceled, thereby reducing the burden on both users and the operator. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. Specifically, it includes means for monitoring flight status in real time from the operator's database or public API, storing the acquired cancellation information in a database, and comparing it with the reservation data of the relevant user. This system also includes means for sending cancellation information notifications to the relevant user and for collecting and analyzing seat availability information and operation information from multiple operators in real time. This allows the system to quickly and appropriately notify the user of the generated alternative means proposals and provides a means for the user to confirm the new reservation selected by the user. This significantly reduces the effort and stress for users compared to conventional methods, and also alleviates congestion at operator call centers and counters.

[0006] "Operating agencies" are companies or organizations that provide public transportation, such as airlines, railway companies, and bus companies.

[0007] A "database" is a system for efficiently storing and searching information, and is a collection of structured data used to quickly obtain specific information.

[0008] A "public API" is a program interface for accessing specific functions or data, and is a publicly available API that can be used by other systems or applications to collaborate.

[0009] "Real-time" means that information is reflected immediately the moment it is acquired or processed, and refers to processing in real time without delay.

[0010] "Notification" refers to information or messages sent to users by systems or services, and is a means of conveying important information to users.

[0011] "Collection" refers to the process of gathering necessary data and information from multiple sources, and in this invention refers to the collection of information on available seats and operation information of operating agencies.

[0012] "Analysis" is the activity of processing collected data in an appropriate way to derive meaningful information and insights.

[0013] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to derive optimal solutions for specific purposes.

[0014] "Alternative means" refers to other options or methods used when the primary means is unavailable, and in this invention refers to alternative transportation means in the event of a flight cancellation.

[0015] "Reservation" refers to the procedure for securing a specific service or product in advance, and in the present invention means securing a seat on an operating agency or a means of transportation. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention helps users to efficiently find alternative means when a flight is canceled. A specific embodiment of the system will be described below.

[0038] System configuration

[0039] This system mainly consists of three elements: a server, a terminal, and a user. The roles and operations of each are described below.

[0040] server

[0041] 1. Monitoring flight cancellation information

[0042] The server monitors flight status in real time via the operator's database and public API. This monitoring is carried out periodically with the aim of quickly detecting flight cancellations.

[0043] 2. Saving and checking cancellation information

[0044] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[0045] 3. Sending notifications

[0046] The server then sends flight cancellation notifications to affected users via email, SMS, mobile app push notifications, etc. The notifications include details of the cancellation and next steps to take.

[0047] 4. Collection of seat availability and operation information

[0048] The server collects real-time information on seat availability and operation from multiple operators, using APIs and web scraping technology.

[0049] 5. Proposing alternative solutions

[0050] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best fit the user's desired conditions, thereby suggesting the best options to the user.

[0051] 6. Confirmation of reservation

[0052] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[0053] Terminal (user device)

[0054] 1. Receiving notifications

[0055] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[0056] 2. Review and select alternatives

[0057] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[0058] User

[0059] 1. Check notifications

[0060] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[0061] 2. Choosing an alternative

[0062] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[0063] 3. Completing the reservation

[0064] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[0065] Specific examples

[0066] For example, if User A has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation.

[0067] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0068] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User A.

[0069] 3. The server retrieves User A's contact information from the database and sends a push notification to the mobile app.

[0070] 4. The server collects seat availability information from other operators and uses a generative AI model to list the flights that best meet User A's desired conditions.

[0071] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Airline B's flight departing at 15:00."

[0072] 6. The server connects the new reservation to Airline B's reservation system and confirms the reservation. If the reservation is successful, it notifies User A of the corresponding reservation number and details.

[0073] This allows User A to reserve a replacement flight more quickly and without stress than with conventional methods. It also reduces congestion at the operator's call centers and counters.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[0077] Step 2:

[0078] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the cancelled flight (flight number, departure and arrival times, etc.) in the database.

[0079] Step 3:

[0080] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[0081] Step 4:

[0082] The server generates and sends a cancellation notification to the identified user via a communication method (email, SMS, or mobile app push notification), including details of the cancellation and next steps to take.

[0083] Step 5:

[0084] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[0085] Step 6:

[0086] The server analyzes the collected data using a generative AI model to select the alternative flight that best suits the user's desired conditions (destination, time, budget, etc.) and generates a list of alternative flight candidates.

[0087] Step 7:

[0088] The server generates a list of alternative flight options based on the analysis results and sends it to the user in the form of a notification, which includes details of the alternative flights (such as the name of the operating company, departure and arrival times, and fares).

[0089] Step 8:

[0090] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[0091] Step 9:

[0092] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[0093] Step 10:

[0094] After the server confirms the successful booking, it generates and sends a booking confirmation notice to the user, which includes details of the new booking (flight number, departure and arrival times, booking number, etc.).

[0095] In this way, the system can provide users with quick and appropriate alternative flight arrangements, minimizing disruption in the event of a flight cancellation.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] In the past, when a flight was canceled, passengers had to find an alternative themselves, which often required a great deal of time and effort. Furthermore, the response of the airline's call center or service desk was limited, making it difficult for many passengers to find an alternative quickly and efficiently. This situation could lead to a decline in passenger satisfaction and negatively impact the airline's reputation, so a system that could automatically and efficiently suggest alternatives when a flight was canceled was needed.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for using a generative AI model to propose an alternative means that best suits the user's desired conditions, means for notifying the user of the generated alternative means proposal, and means for confirming the new reservation selected by the user, thereby enabling users to find alternative means quickly and efficiently.

[0101] "Operator" refers to public transportation such as airplanes, trains, buses, and ferries, and includes companies and organizations that provide these means of transportation.

[0102] A "database" is a system for efficiently storing and managing large amounts of information, and refers to a structure that allows information to be searched and data to be saved and updated.

[0103] "Public API" means an application programming interface that is publicly available for use by third parties and provides functionality for retrieving and manipulating data.

[0104] "Real-time" means that processing is done immediately and the results are reflected instantly. This refers to the state in which the latest data is available immediately when collecting information on flight status and seat availability.

[0105] "Cancellation information" refers to information about a scheduled flight or service being canceled for any reason.

[0106] "Matching" refers to the act of comparing multiple pieces of data to see if they match.

[0107] "Notification" means a message or alert that conveys specific information to a User and may be sent by means of email, SMS, push notification, or other means.

[0108] "Availability information" refers to data indicating the number and status of available seats on a public transport flight.

[0109] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate new information or suggestions.

[0110] A "proposal" is the act or content of presenting multiple options or solutions from which a user can choose.

[0111] "Reservation" refers to the process and result of reserving a particular service or seat in advance.

[0112] "Push notifications" are alerts or messages sent from the server to a client application, and are a means of instantly conveying information to users.

[0113] "Web scraping" refers to the techniques and methods of automatically extracting data from websites.

[0114] "User" means a person who uses a system or service, and refers to a customer who receives services from an operating agency.

[0115] System configuration

[0116] The system of this invention is composed of three main elements: a server, a terminal, and a user. How each of these elements functions will be described in detail below.

[0117] server

[0118] The server periodically monitors the operator's database and public API to obtain real-time flight status. For example, the public API of an airline is used for this monitoring. If flight cancellation information is confirmed based on the monitored information, the information is saved in the database and compared with the user's reservation data.

[0119] The server then sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications, using Firebase Cloud Messaging or similar.

[0120] Furthermore, the server collects and analyzes real-time seat availability and operation information from multiple operators, for example, using web scraping technology (BeautifulSoup and Scrapy) and public APIs.

[0121] The server then uses the acquired data to suggest the best alternatives to the user using a generative AI model (such as OpenAI's GPT-3), which generates candidates based on a specific prompt.

[0122] Using the following prompt as an example:

[0123] "My flight from Tokyo to Osaka has been cancelled. Please list the next available flight. My requirements are as follows:

[0124] Departure time: As early as possible

[0125] Amount: Within budget

[0126] Convenience: Direct flights preferred

[0127] By inputting the collected data into a generative AI model, the best alternatives are listed and notified to the user. After the user selects an alternative, the server connects with the operator's reservation system based on the selection and confirms the new reservation.

[0128] Terminal (user device)

[0129] The user's device receives real-time notifications from the server via email, SMS, or mobile app push notifications. The user can review the notifications and view details of the proposed alternatives through the mobile app or web portal.

[0130] Once the user has selected the desired alternative, the terminal can send this information to the server and proceed with confirming the new reservation.

[0131] User

[0132] The user can view the cancellation information through notifications displayed on their device, select their preferred alternatives from the suggested options, complete the booking, and finally receive a confirmation from the server that the new transportation has been secured.

[0133] Specific examples

[0134] For example, if User A is booking a flight from Tokyo to Osaka, the following scenario is possible:

[0135] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0136] 2. The server saves the cancellation information in the database and compares it with User A's reservation data.

[0137] 3. The server sends a flight cancellation notification to User A via a push notification on the mobile app.

[0138] 4. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best suit User A's desired conditions.

[0139] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Next Flight."

[0140] 6. The server confirms the reservation for the "next flight" by connecting with the reservation system and notifies User A of the details.

[0141] This allows passengers to find alternatives quickly and efficiently, and improves the quality of service for operators.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The server monitors flight status in real time from the operator's database or public API. This monitoring process is performed by sending periodic API requests. For example, a request is sent to the API every five minutes to obtain flight cancellation information. The server's input is the response data to the API request, and the output is the result of extracting the cancellation information. If cancellation information is detected, the data is passed on to the next step.

[0145] Step 2:

[0146] The server stores the acquired flight cancellation information in a database and compares it with the reservation data of the corresponding users. First, the cancellation information is inserted into the database, and then the reservation data is searched using an SQL query. The input is the cancellation information and reservation data, and the output is the identification information of the users who have reservations on the canceled flights. This comparison identifies the affected users.

[0147] Step 3:

[0148] The server sends flight cancellation notifications to affected customers via email, SMS, and mobile app push notifications, for example, using Firebase Cloud Messaging. The input is the identity and contact information of the affected customers, and the output is the notification sent.

[0149] Step 4:

[0150] The server collects and analyzes seat availability and operation information from multiple operators in real time. Web scraping technology (BeautifulSoup and Scrapy) and public APIs are used for collection. The input is data obtained from the operators, and the output is a list of seat availability and operation information. The collected information is used in the next step.

[0151] Step 5:

[0152] The server uses a generative AI model to suggest alternatives that best fit the user's desired conditions. A specific prompt is input into the generative AI model (for example, OpenAI's GPT-3), which lists the best alternatives. The input is seat availability information, flight information, and the user's desired conditions, and the output is a suggested alternative. An example of a prompt is, "The flight from Tokyo to Osaka has been canceled, so please list the next available flights. My desired conditions are as follows: Departure time: As early as possible, Price: Within budget, Convenience: Prefer direct flights."

[0153] Step 6:

[0154] The server notifies the user of the generated alternative suggestions via email, SMS, or mobile app push notification. The input is the alternative information, and the output is a notification sent to the user, which includes details of the suggested alternative.

[0155] Step 7:

[0156] The user uses the device to review and select alternatives. The input is the notification and alternative information sent from the server, and the output is the user's selection. The user reviews the list of suggested alternative flights through the mobile app or web portal and selects the desired alternative.

[0157] Step 8:

[0158] The server confirms the new reservation selected by the user. Based on the selected reservation information, it connects with the operator's reservation system and confirms the new reservation. The input is the information on the alternative means selected by the user, and the output is a confirmation of the new reservation. If the reservation is successful, the details are notified to the user.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] With conventional systems, it is difficult for users to efficiently find alternative means of transportation when a service is canceled, which leads to a decrease in user satisfaction. Furthermore, in the food delivery industry, delays due to delivery vehicle breakdowns and traffic congestion frequently occur, significantly affecting customer satisfaction and service quality. There is a need for a system that can solve these problems and quickly and efficiently propose and determine alternative means of transportation.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for saving the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing seat availability information and flight information from multiple operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for monitoring the operation status of the delivery means, acquiring delay information and saving it in a database, means for using a generative AI model to propose an alternative delivery means based on the acquired delay information, and means for generating the alternative means proposal as a prompt message and notifying the delivery person and the customer. This makes it possible to quickly and efficiently propose the optimal alternative means to users affected by cancellations or delays and confirm reservations and re-delivery.

[0164] "Operator" refers to the entire organization that provides transportation, such as airlines, bus companies, and train companies.

[0165] "Database" refers to an information system for efficiently managing, storing, searching, and using data.

[0166] A "public API" is a protocol that makes the functions provided by a specific service available externally.

[0167] "Real-time" means nearly simultaneous, i.e., with little delay.

[0168] A "generative AI model" refers to an artificial intelligence technology that uses advanced algorithms to generate specific patterns and predictions from data.

[0169] A "prompt sentence" refers to a sentence that is input as an instruction or question to an AI model.

[0170] "Push notification" refers to the instantaneous delivery of a message from a server to a client.

[0171] "Available seat information" is data on currently available seats.

[0172] "Delay Information" means data containing details of time delays occurring relative to scheduled trips or deliveries.

[0173] "Alternative means" refers to other means used in place of the original means when it is not available.

[0174] "Reservation Data" refers to data including registration information that a user has made in advance to use a specific service.

[0175] System configuration

[0176] The system of the present invention is mainly composed of three elements: a server, a terminal (user device), and a user.

[0177] server

[0178] The server monitors flight status in real time via the operator's database and public API. Specifically, it periodically executes API calls to obtain flight information. The obtained cancellation information is stored in the database and then matched with the reservation data of the corresponding passenger. This matching process identifies affected passengers. Affected passengers are notified of the cancellation information via email, SMS, and mobile app push notifications.

[0179] Next, the server collects real-time seat availability and operation information from multiple operators using APIs and web scraping technology. Based on the collected data, it uses a generative AI model to suggest the best alternatives for the user. These alternatives are generated as prompts and notified to the user. If the user selects a new reservation, the server connects with the corresponding operator's reservation system to confirm the new reservation.

[0180] Similarly, in a delivery service, the server monitors the operation status of delivery vehicles in real time. For example, if a delivery vehicle is delayed, the information is stored in a database and the affected delivery person and customer are notified. An alternative delivery vehicle is proposed using a generative AI model, and a re-delivery is confirmed.

[0181] Terminal (user device)

[0182] The user's device receives the notification sent from the server. The notification is displayed via email, SMS, or the mobile app's push notification function. The user can check the notification and view a list of alternative means in the event of a cancellation or delay. The user selects an alternative means and proceeds to confirm the new reservation through the app.

[0183] User

[0184] The user checks the notification displayed on the terminal and becomes aware of the flight cancellation or delay. Then, the user selects the desired alternative flight or transportation method from multiple alternative flights and transportation methods proposed by the server. After completing the reservation of the selected alternative flight, the user receives a confirmation notification sent from the server.

[0185] Specific examples

[0186] For example, suppose User A requests food delivery from one city to another, and the delivery vehicle gets stuck in traffic. The server monitors the operation status and obtains this information. The server then stores the delay information in a database and notifies the relevant delivery person and customer. The server uses a generative AI model to examine available alternatives and generate the best alternative as a prompt. This prompt is sent to the delivery person and customer. An example prompt is, "Please display the next available delivery option." The delivery person selects from the proposed alternatives and confirms the re-delivery. This process allows for fast and efficient delivery.

[0187] In this way, the collaboration between the server, terminal, and user components realizes a system that can efficiently deal with cancellations of transportation services and delivery delays. This system allows users to quickly and stress-free secure alternative means of transportation, improving the quality of service. It also contributes to cost reductions by streamlining the operations of transportation services and delivery services.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The server monitors the operation status in real time via the operator's database and public API. It periodically executes API calls to obtain operation information. The input is the API endpoint of each operator, and the output is the obtained real-time operation information.

[0191] Step 2:

[0192] The server stores the flight cancellation information it acquires in a database and compares it with the reservation data of the corresponding users. The input is the acquired flight information and the reservation data in the database, and the output is a list of users affected by the cancellation. Data processing involves comparing the reservation data with the cancellation information to identify matches.

[0193] Step 3:

[0194] The server sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications. The input is a list of users affected by the cancellation and the notification content, and the output is the notification sent. Specifically, it retrieves the contact information of the affected users and sends notifications via each contact method.

[0195] Step 4:

[0196] The server collects and analyzes seat availability and operation information from multiple operators in real time. The input is data obtained from each operator's seat availability API and web scraping, and the output is a list of collected seat availability information. A generative AI model is used to analyze the data and identify the optimal alternative.

[0197] Step 5:

[0198] The server notifies the user of the alternative suggestions it has generated. The notification is generated as a prompt and sent to the user. The input is the data on alternatives analyzed by the generative AI model, and the output is the sent notification of the suggestions. Specifically, the details of each alternative are generated as a prompt and a notification is sent.

[0199] Step 6:

[0200] The server confirms the new reservation selected by the user. The input is the alternative means selected by the user and its reservation information, and the output is the confirmed new reservation information. The server communicates with the corresponding operator's reservation system to confirm the new reservation and notify the user of the details.

[0201] Step 7:

[0202] The server monitors the operation status of delivery vehicles, acquires delay information, and stores it in a database. The input is the operation information API for delivery vehicles, and the output is the acquired delay information. Data processing involves analyzing the location information of delivery vehicles and generating delay information.

[0203] Step 8:

[0204] Based on the delay information acquired by the server, a generative AI model is used to propose alternative delivery methods. The input is the delay information and data on alternative delivery methods, and the output is the proposed alternative method. Specifically, the delay information is analyzed, and the generative AI model identifies available alternative methods.

[0205] Step 9:

[0206] The server generates a prompt to suggest alternative means and notifies the delivery person and the customer. The input is the generated alternative means data, and the output is the sent suggestion notification. An example of a prompt is "Please display the next available additional delivery means."

[0207] Step 10:

[0208] The terminal receives the notification sent from the server and confirms the notification. The input is the notification content from the server, and the output is the displayed notification. The user confirms the notification and obtains information to select alternatives to cancellations and delays.

[0209] Step 11:

[0210] The terminal displays a list of alternatives proposed by the server and the user selects the desired option. The input is the list of proposed alternatives and the output is the alternative selected by the user. The user makes the selection through the terminal and confirms the new reservation or re-delivery.

[0211] Step 12:

[0212] The server saves the details of the new reservation or redelivery in a database and sends a confirmation to the user. The input is the confirmed new reservation or redelivery information, and the output is the sent confirmation. Specifically, it works by coordinating with the corresponding delivery or transportation system to confirm the reservation or redelivery.

[0213] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0214] The system of the present invention helps users to efficiently find alternative means when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. Specific embodiments of the system are described below.

[0215] System configuration

[0216] This system mainly consists of four elements: the server, the terminal, the user, and the emotion engine. The roles and operations of each are described below.

[0217] server

[0218] 1. Monitoring flight cancellation information

[0219] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[0220] 2. Saving and checking cancellation information

[0221] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[0222] 3. Sending notifications

[0223] The server generates flight cancellation notifications for the relevant users, customizes the content of the notifications based on the user's emotional state, and sends them via communication methods (email, SMS, mobile app push notifications).

[0224] 4. Collection of seat availability and operation information

[0225] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[0226] 5. Proposing alternative solutions

[0227] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best suit the user's desired conditions (destination, time, budget) and emotional state. A list of alternative flight options is generated and notified to the user.

[0228] 6. Confirmation of reservation

[0229] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[0230] Terminal (user device)

[0231] 1. Receiving notifications

[0232] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[0233] 2. Review and select alternatives

[0234] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[0235] User

[0236] 1. Check notifications

[0237] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[0238] 2. Choosing an alternative

[0239] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[0240] 3. Completing the reservation

[0241] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[0242] Emotion Engine

[0243] 1. Emotional awareness

[0244] The emotion engine analyzes the user's emotions based on their voice, text input, and device sensor information. The emotion engine evaluates their stress level and mood and sends the results back to the server.

[0245] 2. Customized Notifications

[0246] The server customizes the notification content based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification content may be changed to a more gentle tone and emphasize prompt action.

[0247] 3. Adjusting priorities

[0248] The server adjusts the content and priorities of alternative suggestions based on the stress level information obtained from the emotion engine. For users with high stress levels, it prioritizes suggestions for the nearest available flight and more comfortable transportation options.

[0249] Specific examples

[0250] For example, if User B has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation:

[0251] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0252] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User B.

[0253] 3. The server retrieves User B's contact information from the database and sends a push notification to the mobile app. Because the emotion engine recognizes User B's high stress state, the notification content is expressed in a particularly gentle manner.

[0254] 4. The server collects information on available seats on other operators and uses the generative AI model to list the flights that best match User B's desired conditions and stress level. In this case, priority is given to the nearest flight and the most comfortable means of transportation.

[0255] 5. User B checks the list of suggested alternative flights through the mobile app and selects "Airline C's flight departing at 14:00."

[0256] 6. The server connects the new reservation to Airline C's reservation system and confirms the reservation. If the reservation is successful, it notifies User B of the corresponding reservation number and details.

[0257] This allows User B to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's call centers and counters.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is managed by a configured timer, and the server calls the API at regular intervals to obtain flight status information.

[0261] Step 2:

[0262] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the canceled flight (flight number, departure and arrival times, etc.) in the database.

[0263] Step 3:

[0264] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[0265] Step 4:

[0266] The emotion engine evaluates the user's emotional state based on their past voice and text inputs and sensor information, and provides feedback on their stress level and mood to the server.

[0267] Step 5:

[0268] The server customizes the content of the cancellation notification based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification may include more gentle language and an emphasis on immediate action. The server then sends the notification as an email, SMS, or mobile app push notification.

[0269] Step 6:

[0270] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology. The collected data is stored in a database and updated.

[0271] Step 7:

[0272] The server analyzes the collected data using a generative AI model, which considers the user's destination, time, budget, and emotional state to select the most suitable alternative flight or other transportation option, and generates a list of selected alternative flights.

[0273] Step 8:

[0274] The server then notifies the user of the list of alternative flights. The notification content is customized according to the user's emotional state. The notification includes details of the alternative flights (operating company name, departure and arrival times, fares, etc.).

[0275] Step 9:

[0276] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[0277] Step 10:

[0278] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[0279] Step 11:

[0280] After the server confirms the successful reservation, it generates and sends a reservation confirmation notice to the user. The notice contains details of the new reservation (flight number, departure and arrival times, reservation number, etc.). For high-stress users, the notice includes a message emphasizing that the reservation has been completed.

[0281] Examples:

[0282] For example, if User C has booked a flight from Tokyo to Fukuoka, the following scenario will unfold when the server receives information about a flight cancellation.

[0283] 1. The server obtains information about the cancellation of a flight from Tokyo to Fukuoka through the airline's API.

[0284] 2. The server saves the cancellation information in the database and compares it with the reservation data of User C, the relevant user.

[0285] 3. The server retrieves user C's contact information from the database, and the emotion engine analyzes past communication and usage history.

[0286] 4. The emotion engine recognizes User C's high stress state and provides feedback to the server.

[0287] 5. Based on the feedback from the emotion engine, the server generates a notification that emphasizes calm expressions and prompt responses, and sends it as a push notification to the mobile app.

[0288] 6. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best fit your desired conditions and stress level, prioritizing the earliest flights and most comfortable travel options.

[0289] 7. User C checks the list of suggested alternative flights through the mobile app and selects "Operator D's flight departing at 16:00."

[0290] 8. The server connects the new reservation to Operator D's reservation system and confirms the reservation. If the reservation is successful, it retrieves the corresponding reservation details and stores them in the database.

[0291] 9. After the server confirms the success of the reservation, it generates and sends a reservation confirmation notification to User C. For User C who is under high stress, the notification emphasizes that the reservation has been completed.

[0292] This allows User C to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's counters and call centers.

[0293] Example 2

[0294] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] When a service is canceled, it is difficult for passengers to find alternative means quickly and efficiently, which tends to increase the stress they feel. Furthermore, conventional systems are unable to provide services that take into account the emotional state of the passenger, so there is a need to reduce the psychological burden on passengers.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0297] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for analyzing using a generative AI model and proposing an alternative means that best suits the user's desired conditions and emotional state, and means for confirming the new reservation selected by the user. This allows users to quickly and efficiently find alternative means and further reduces psychological burden by receiving appropriate responses according to their emotional state.

[0298] "Transportation agency" refers to an organization that provides passenger or freight transportation services, such as an airline, railroad, or bus company.

[0299] A "database" is a system that organizes and stores information in a particular way, allowing the information to be efficiently searched, managed, and updated.

[0300] "Public API" refers to an application programming interface that is publicly available for developers to use and is used to connect with other systems and applications.

[0301] "Cancellation information" refers to information indicating that a scheduled flight will not operate for some reason.

[0302] "User" refers to an individual or legal entity that uses the services of an operating company.

[0303] "Notification" refers to the act of transmitting specific information to a specific target, and includes email, SMS, app push notifications, etc.

[0304] "Available seat information" refers to information about available seats on an operating company, and indicates that they are available for reservation.

[0305] "Operation information" refers to general information regarding operations, such as flight schedules, operation status, delays, and cancellations.

[0306] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and generate output according to a purpose.

[0307] An "emotion engine" refers to a system that analyzes a user's emotional state in real time and uses the results to provide an appropriate response.

[0308] "New Booking" means a booking for a new means of transportation selected by a User for a flight or means of transportation that has become necessary due to a cancellation.

[0309] "Real-time" refers to data acquisition and processing occurring almost instantaneously.

[0310] "Matching" refers to the act of comparing different pieces of information to see if they match.

[0311] This invention is a system that helps users efficiently find alternative means of transportation when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The roles and operations of each are described in detail below.

[0312] server

[0313] The server is the core of the system. First, it monitors flight status in real time using the public API of the operator. For example, it uses the Python requests library to send HTTP requests and parses the responses in JSON format. This cancellation information is stored in a PostgreSQL database and compared with user reservation data using SQL queries. Based on the results of the comparison, Twilio API and Firebase Cloud Messaging are used to send cancellation information notifications to affected users.

[0314] The server then polls the APIs of multiple operators to collect seat availability and flight information. The collected information is stored in a database, always kept up to date. This information is then analyzed using a generative AI model (e.g., OpenAI's GPT-3) to suggest optimal alternatives based on the user's desired conditions and emotional state. As a result, a list of alternative flight options is generated and notified to the user again.

[0315] Terminal (user device)

[0316] The device receives a notification sent from the server. Specifically, the notification is displayed via the email app, SMS app, or the mobile app's push notification function (e.g., APNs on iOS or FCM on Android). After receiving the notification, the user can view the proposed alternatives through the mobile app or web portal. Through a user interface built with React Native or Flutter, the user selects the desired option and completes the process to confirm the new reservation.

[0317] User

[0318] The user confirms the notification displayed on the terminal and becomes aware that a flight cancellation has occurred. After receiving the notification, the user selects the desired alternative flight or transportation method from the multiple alternative flights and transportation methods proposed by the server. The server then confirms the new reservation and receives details (such as a reservation number) if successful.

[0319] Emotion Engine

[0320] The emotion engine analyzes the user's emotions in real time based on the user's voice, text input, and device sensor information. This analysis uses natural language processing technology. The emotion engine feeds back the analysis results to the server, which then customizes the notification content based on the results. For example, if the server recognizes that the user is in a state of high stress, it will change the notification content to be particularly gentle. In addition, when the server suggests alternative solutions, it also adjusts the priority taking the user's emotional state into account.

[0321] Specific examples

[0322] For example, if a user has booked a flight from Tokyo to Osaka, and the server receives information from an airline's API that the flight from Tokyo to Osaka has been cancelled, the following scenario will unfold:

[0323] 1. The server stores the acquired cancellation information in a database and compares it with the reservation data of the corresponding user.

[0324] 2. The server retrieves the user's contact information from the database and sends a push notification to the mobile app. The notification is delivered in a particularly gentle manner because the emotion engine recognizes the user's high stress state.

[0325] 3. The server collects information on available seats from other operators and uses a generative AI model to list the flights that best match the user's desired conditions and stress level. For example, the "earliest flight" or "comfortable transportation" is prioritized.

[0326] 4. The user reviews the list of suggested alternative flights through the mobile app and selects one.

[0327] 5. The server confirms the new reservation and notifies the user of the details.

[0328] Such a system allows users to find alternatives quickly and stress-free.

[0329] Additionally, the following example prompt sentence is used as input to the generative AI model: "Please suggest alternative flights that meet the following conditions: destination: Osaka, desired departure time: 14:00, budget: within 30,000 yen, user's emotional state: high stress."

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1:

[0332] The server monitors the operation status in real time through the operator's public API. Specifically, it periodically sends HTTP requests using the Python requests library and receives responses in JSON format. The input data is operation status information obtained from the operator's API endpoint, and the output is cancellation information in JSON format. Once the cancellation information is obtained, the server proceeds to the next step.

[0333] Step 2:

[0334] The server stores the cancellation information in a PostgreSQL database. This operation uses the psycopg2 library. The input data is the cancellation information, and the output is the cancellation entry stored in the database. It then matches this with the user's reservation data using an SQL query to identify the corresponding user.

[0335] Step 3:

[0336] The server generates a notification of the flight cancellation information for the identified user. First, it retrieves the user's contact information from the database. Next, it generates a notification message and queries the emotion engine for the user's emotional state. The input data is the contact information and the flight cancellation information, and the output is a customized notification message. Specifically, it sends the notification using the Twilio API or Firebase Cloud Messaging.

[0337] Step 4:

[0338] The emotion engine analyzes the user's emotional state in real time. The input data is the user's voice, text input, and device sensor information, and the output is an evaluation of the user's emotional state. Based on this evaluation, the server customizes the notification content as needed.

[0339] Step 5:

[0340] The server uses the APIs of multiple operators to collect seat availability information and operation information. Web scraping technology may also be used. The input data is the response of the operator's various APIs, and the output is seat availability information and operation information stored in the database. This ensures that the latest information is always maintained.

[0341] Step 6:

[0342] The server uses a generative AI model (e.g., OpenAI's GPT-3) to analyze the collected seat availability information and flight information. The input data are seat availability information, flight information, the user's desired conditions, and a prompt sentence about the user's emotional state, and the output is a list of suggested alternatives. An example of a prompt sentence is, "Please suggest alternative flights that meet the following conditions: destination: destination, desired departure time: desired time, budget: budget, user's emotional state: emotional state."

[0343] Step 7:

[0344] The server notifies the user of the generated list of suggested alternatives. The input data is the list of suggested alternatives, and the output is a notification sent to the user's device. Specifically, the server sends the notification using the Twilio API or Firebase Cloud Messaging again.

[0345] Step 8:

[0346] The user checks the list of suggested alternatives on their device and selects the option they want. The input data is the notification from the server, and the output is the information about the alternative selected by the user.

[0347] Step 9:

[0348] The server communicates with the corresponding operator's reservation system to confirm a new reservation based on the alternative selected by the user. The input data is the alternative selected by the user, and the output is a confirmation of the reservation. If the reservation is successful, the server notifies the user of the details (such as the reservation number).

[0349] Step 10:

[0350] The user receives a notification confirming that the new reservation has been confirmed and receives further details (such as the reservation number). The input data are the reservation details from the server, and the output is the user's confirmation of the new reservation.

[0351] (Application example 2)

[0352] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0353] In modern society, delays in transportation and delivery services cause significant stress and difficulty for users. Furthermore, due to a lack of systems that can quickly obtain information about cancellations and delays and suggest appropriate alternatives, users are often forced to resolve issues on their own. Furthermore, the lack of response that takes into account the user's emotional state can lead to increased stress and dissatisfaction. The objective of this invention is to solve these problems and provide a system that provides a fast, user-friendly response when transportation or delivery services are delayed.

[0354] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring operation status in real time from the database and public API of transportation operators, means for saving the acquired cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and operation information from multiple transportation operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for collecting delivery delay information and recognizing the user's emotional state to customize the notification content, and means for suggesting alternative means when delivery is delayed and confirming a new order. This enables a quick and user-friendly response when transportation or delivery service delays occur.

[0355] "Transportation" refers to all modes of transportation, including public transportation and private transportation services.

[0356] "Cancellation Information" means information indicating that a scheduled service of an operator has been cancelled.

[0357] "Emotion engine" refers to a software or hardware component for recognizing and analyzing a user's emotional state.

[0358] "Alternative Means" means the alternative means of travel or service offered to a Passenger in the event of a cancellation or delay.

[0359] "Real-time" means that data is processed as soon as it is acquired, with results available almost immediately.

[0360] "API" stands for Application Programming Interface, and refers to an interface that enables communication between different software applications.

[0361] "Web scraping" refers to the automated process of extracting information from websites.

[0362] A "generative AI model" refers to a model that includes algorithms that use artificial intelligence techniques to analyze data and generate new data or suggestions.

[0363] "Prompt" means an instruction or question input to a generative AI model, and is text used to guide the model to an appropriate output.

[0364] "Notification" means an informational message sent by the System to a User, which may take the form of email, SMS, mobile app push notification, etc.

[0365] "New Order" means a replacement order selected by a User when an existing order is cancelled or delayed.

[0366] "Database" means an electronic system for efficiently storing, retrieving, and managing structured data.

[0367] "User" refers to any individual or organization that uses this system or service.

[0368] "Stress state" indicates the user's mental or emotional state of tension and is analyzed by the emotion engine.

[0369] System configuration

[0370] The embodiment of the present invention consists of three elements: a server, a terminal, and a user. The system provides a fast and user-friendly response when there is a delay in the food delivery service through the cooperative operation of these elements.

[0371] server

[0372] The server has the following main functions:

[0373] 1. Monitoring delay information:

[0374] The server periodically calls the delivery company's API endpoint to monitor the delivery status in real time. The information obtained from this API endpoint is saved in the following format, for example.

[0375] 2. Storage and collation of delayed information:

[0376] The server stores the acquired delay information in a database and compares it with the order data of the corresponding user. At this time, the server uses a database management system (e.g., MySQL or PostgreSQL).

[0377] 3. Sending notifications:

[0378] If a delay is confirmed, the server uses an emotion engine to customize the notification content, taking into account the user's emotional state. For example, if the user is in a high-stress state, the notification content may be changed to a more calming tone.

[0379] 4. Proposing alternatives using generative AI models:

[0380] The server uses the generative AI model to generate the best alternative based on the user's desired conditions and emotional state. As input to the generative AI model, the prompt sentence is set as follows:

[0381] The user's requirements are as follows:

[0382] Chicken dishes

[0383] Delivery time within 30 minutes

[0384] The price is less than 1,500 yen

[0385] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0386] Based on this criteria, suggest the best alternative restaurant and menu.

[0387] 5. Confirming a new order:

[0388] After the user selects an alternative, the server will contact the corresponding delivery system to place a new order, and once the order is placed, the server will notify the user of the order details.

[0389] Terminal (user device)

[0390] The user's device has the following capabilities:

[0391] 1. Receiving notifications:

[0392] Receive delay notifications sent by the server, which can appear as email, SMS, or mobile app push notifications.

[0393] 2. Review and select alternatives:

[0394] Through a mobile app or web portal, users can review the alternatives suggested by the server and choose the option that best suits their needs.

[0395] User

[0396] The user performs the following actions:

[0397] 1. Check notifications:

[0398] The user checks the delay notification displayed on the terminal.

[0399] 2. Alternative Selection:

[0400] From the multiple alternatives proposed by the server, you can choose the one that best suits your requirements.

[0401] 3. Completing a new order:

[0402] Confirm the selected alternative and receive the new order details from the server.

[0403] Specific examples

[0404] For example, if a user orders a chicken dish from a particular food delivery service, the following scenario unfolds when the server detects that the delivery will be delayed:

[0405] 1. The server learns through the delivery company's API that the user's order is 30 minutes late.

[0406] 2. Store the delay information in a database and match it with the order data of the corresponding user.

[0407] 3. Analyze the stress state using an emotion engine based on the user's past feedback.

[0408] 4. If the user is under high stress, notify them of delays in a gentler manner.

[0409] 5. The server uses a generative AI model to suggest alternatives that fit the user's preferences:

[0410] The user's requirements are as follows:

[0411] Chicken dishes

[0412] Delivery time within 30 minutes

[0413] The price is less than 1,500 yen

[0414] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0415] Based on this criteria, suggest the best alternative restaurant and menu.

[0416] 6. The user reviews the proposed alternatives through the mobile app and selects the preferred option.

[0417] 7. The server confirms the new order and notifies the user of the details.

[0418] This allows users to secure alternative means quickly and stress-free, minimizing delivery disruptions.

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1: Monitor latency information

[0421] The server periodically calls the delivery company's API endpoint to monitor delivery status in real time. The input is the API endpoint URL, and the output is the retrieved delivery status information. The server stores this information in a database.

[0422] Step 2: Storing and verifying delay information

[0423] The server saves the acquired delivery status information in a database and compares it with the corresponding user's order data. The input is the delivery status information and the user's order data, and the output is a list of delayed orders. The server identifies delayed orders based on the comparison results.

[0424] Step 3: Sending notifications

[0425] The server sends notifications of delays to the appropriate users. The input is a list of delayed orders and user contact information, and the output is a notification message to the user. Notifications can be sent via email, SMS, or mobile app push notifications.

[0426] Step 4: Analyze emotional state

[0427] The server uses an emotion engine to analyze the user's emotional state. The input is the user's past feedback and sensor information, and the output is the analyzed emotional state (e.g., high stress). The notification content is customized based on this information.

[0428] Step 5: Propose alternatives

[0429] The server uses a generative AI model to suggest optimal alternatives based on the user's desired conditions and emotional state. The input is the user's desired conditions, emotional state, and prompt, and the output is a generated list of alternatives. An example prompt is as follows:

[0430] The user's requirements are as follows:

[0431] Chicken dishes

[0432] Delivery time within 30 minutes

[0433] The price is less than 1,500 yen

[0434] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0435] Based on this criteria, suggest the best alternative restaurant and menu.

[0436] Step 6: Review and select alternatives

[0437] The device receives the notification and the user checks the list of alternatives sent by the server. The input is the list of alternatives sent by the server and the output is the selected alternative. The user selects the desired option through the mobile app or web portal.

[0438] Step 7: Confirm your new order

[0439] The server places a new order based on the selected alternative. The inputs are the selected alternative and the delivery system's API, and the output is a new order confirmation message. The server notifies the user with the details of the new order.

[0440] These steps enable the system to provide a fast, user-friendly response when food delivery services are delayed, allowing users to secure alternatives without stress.

[0441] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0442] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0452] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0453] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0454] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0456] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0457] The system of the present invention helps users to efficiently find alternative means when a flight is canceled. A specific embodiment of the system will be described below.

[0458] System configuration

[0459] This system mainly consists of three elements: a server, a terminal, and a user. The roles and operations of each are described below.

[0460] server

[0461] 1. Monitoring flight cancellation information

[0462] The server monitors flight status in real time via the operator's database and public API. This monitoring is carried out periodically with the aim of quickly detecting flight cancellations.

[0463] 2. Saving and checking cancellation information

[0464] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[0465] 3. Sending notifications

[0466] The server then sends flight cancellation notifications to affected users via email, SMS, mobile app push notifications, etc. The notifications include details of the cancellation and next steps to take.

[0467] 4. Collection of seat availability and operation information

[0468] The server collects real-time information on seat availability and operation from multiple operators, using APIs and web scraping technology.

[0469] 5. Proposing alternative solutions

[0470] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best fit the user's desired conditions, thereby suggesting the best options to the user.

[0471] 6. Confirmation of reservation

[0472] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[0473] Terminal (user device)

[0474] 1. Receiving notifications

[0475] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[0476] 2. Review and select alternatives

[0477] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[0478] User

[0479] 1. Check notifications

[0480] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[0481] 2. Choosing an alternative

[0482] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[0483] 3. Completing the reservation

[0484] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[0485] Specific examples

[0486] For example, if User A has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation.

[0487] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0488] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User A.

[0489] 3. The server retrieves User A's contact information from the database and sends a push notification to the mobile app.

[0490] 4. The server collects seat availability information from other operators and uses a generative AI model to list the flights that best meet User A's desired conditions.

[0491] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Airline B's flight departing at 15:00."

[0492] 6. The server connects the new reservation to Airline B's reservation system and confirms the reservation. If the reservation is successful, it notifies User A of the corresponding reservation number and details.

[0493] This allows User A to reserve a replacement flight more quickly and without stress than with conventional methods. It also reduces congestion at the operator's call centers and counters.

[0494] The processing flow will be explained below.

[0495] Step 1:

[0496] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[0497] Step 2:

[0498] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the cancelled flight (flight number, departure and arrival times, etc.) in the database.

[0499] Step 3:

[0500] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[0501] Step 4:

[0502] The server generates and sends a cancellation notification to the identified user via a communication method (email, SMS, or mobile app push notification), including details of the cancellation and next steps to take.

[0503] Step 5:

[0504] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[0505] Step 6:

[0506] The server analyzes the collected data using a generative AI model to select the alternative flight that best suits the user's desired conditions (destination, time, budget, etc.) and generates a list of alternative flight candidates.

[0507] Step 7:

[0508] The server generates a list of alternative flight options based on the analysis results and sends it to the user in the form of a notification, which includes details of the alternative flights (such as the name of the operating company, departure and arrival times, and fares).

[0509] Step 8:

[0510] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[0511] Step 9:

[0512] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[0513] Step 10:

[0514] After the server confirms the successful booking, it generates and sends a booking confirmation notice to the user, which includes details of the new booking (flight number, departure and arrival times, booking number, etc.).

[0515] In this way, the system can provide users with quick and appropriate alternative flight arrangements, minimizing disruption in the event of a flight cancellation.

[0516] Example 1

[0517] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] In the past, when a flight was canceled, passengers had to find an alternative themselves, which often required a great deal of time and effort. Furthermore, the response of the airline's call center or service desk was limited, making it difficult for many passengers to find an alternative quickly and efficiently. This situation could lead to a decline in passenger satisfaction and negatively impact the airline's reputation, so a system that could automatically and efficiently suggest alternatives when a flight was canceled was needed.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0520] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for using a generative AI model to propose an alternative means that best suits the user's desired conditions, means for notifying the user of the generated alternative means proposal, and means for confirming the new reservation selected by the user, thereby enabling users to find alternative means quickly and efficiently.

[0521] "Operator" refers to public transportation such as airplanes, trains, buses, and ferries, and includes companies and organizations that provide these means of transportation.

[0522] A "database" is a system for efficiently storing and managing large amounts of information, and refers to a structure that allows information to be searched and data to be saved and updated.

[0523] "Public API" means an application programming interface that is publicly available for use by third parties and provides functionality for retrieving and manipulating data.

[0524] "Real-time" means that processing is done immediately and the results are reflected instantly. This refers to the state in which the latest data is available immediately when collecting information on flight status and seat availability.

[0525] "Cancellation information" refers to information about a scheduled flight or service being canceled for any reason.

[0526] "Matching" refers to the act of comparing multiple pieces of data to see if they match.

[0527] "Notification" means a message or alert that conveys specific information to a User and may be sent by means of email, SMS, push notification, or other means.

[0528] "Availability information" refers to data indicating the number and status of available seats on a public transport flight.

[0529] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate new information or suggestions.

[0530] A "proposal" is the act or content of presenting multiple options or solutions from which a user can choose.

[0531] "Reservation" refers to the process and result of reserving a particular service or seat in advance.

[0532] "Push notifications" are alerts or messages sent from the server to a client application, and are a means of instantly conveying information to users.

[0533] "Web scraping" refers to the techniques and methods of automatically extracting data from websites.

[0534] "User" means a person who uses a system or service, and refers to a customer who receives services from an operating agency.

[0535] System configuration

[0536] The system of this invention is composed of three main elements: a server, a terminal, and a user. How each of these elements functions will be described in detail below.

[0537] server

[0538] The server periodically monitors the operator's database and public API to obtain real-time flight status. For example, the public API of an airline is used for this monitoring. If flight cancellation information is confirmed based on the monitored information, the information is saved in the database and compared with the user's reservation data.

[0539] The server then sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications, using Firebase Cloud Messaging or similar.

[0540] Furthermore, the server collects and analyzes real-time seat availability and operation information from multiple operators, for example, using web scraping technology (BeautifulSoup and Scrapy) and public APIs.

[0541] The server then uses the acquired data to suggest the best alternatives to the user using a generative AI model (such as OpenAI's GPT-3), which generates candidates based on a specific prompt.

[0542] Using the following prompt as an example:

[0543] "My flight from Tokyo to Osaka has been cancelled. Please list the next available flight. My requirements are as follows:

[0544] Departure time: As early as possible

[0545] Amount: Within budget

[0546] Convenience: Direct flights preferred

[0547] By inputting the collected data into a generative AI model, the best alternatives are listed and notified to the user. After the user selects an alternative, the server connects with the operator's reservation system based on the selection and confirms the new reservation.

[0548] Terminal (user device)

[0549] The user's device receives real-time notifications from the server via email, SMS, or mobile app push notifications. The user can review the notifications and view details of the proposed alternatives through the mobile app or web portal.

[0550] Once the user has selected the desired alternative, the terminal can send this information to the server and proceed with confirming the new reservation.

[0551] User

[0552] The user can view the cancellation information through notifications displayed on their device, select their preferred alternatives from the suggested options, complete the booking, and finally receive a confirmation from the server that the new transportation has been secured.

[0553] Specific examples

[0554] For example, if User A is booking a flight from Tokyo to Osaka, the following scenario is possible:

[0555] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0556] 2. The server saves the cancellation information in the database and compares it with User A's reservation data.

[0557] 3. The server sends a flight cancellation notification to User A via a push notification on the mobile app.

[0558] 4. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best suit User A's desired conditions.

[0559] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Next Flight."

[0560] 6. The server confirms the reservation for the "next flight" by connecting with the reservation system and notifies User A of the details.

[0561] This allows passengers to find alternatives quickly and efficiently, and improves the quality of service for operators.

[0562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0563] Step 1:

[0564] The server monitors flight status in real time from the operator's database or public API. This monitoring process is performed by sending periodic API requests. For example, a request is sent to the API every five minutes to obtain flight cancellation information. The server's input is the response data to the API request, and the output is the result of extracting the cancellation information. If cancellation information is detected, the data is passed on to the next step.

[0565] Step 2:

[0566] The server stores the acquired flight cancellation information in a database and compares it with the reservation data of the corresponding users. First, the cancellation information is inserted into the database, and then the reservation data is searched using an SQL query. The input is the cancellation information and reservation data, and the output is the identification information of the users who have reservations on the canceled flights. This comparison identifies the affected users.

[0567] Step 3:

[0568] The server sends flight cancellation notifications to affected customers via email, SMS, and mobile app push notifications, for example, using Firebase Cloud Messaging. The input is the identity and contact information of the affected customers, and the output is the notification sent.

[0569] Step 4:

[0570] The server collects and analyzes seat availability and operation information from multiple operators in real time. Web scraping technology (BeautifulSoup and Scrapy) and public APIs are used for collection. The input is data obtained from the operators, and the output is a list of seat availability and operation information. The collected information is used in the next step.

[0571] Step 5:

[0572] The server uses a generative AI model to suggest alternatives that best fit the user's desired conditions. A specific prompt is input into the generative AI model (for example, OpenAI's GPT-3), which lists the best alternatives. The input is seat availability information, flight information, and the user's desired conditions, and the output is a suggested alternative. An example of a prompt is, "The flight from Tokyo to Osaka has been canceled, so please list the next available flights. My desired conditions are as follows: Departure time: As early as possible, Price: Within budget, Convenience: Prefer direct flights."

[0573] Step 6:

[0574] The server notifies the user of the generated alternative suggestions via email, SMS, or mobile app push notification. The input is the alternative information, and the output is a notification sent to the user, which includes details of the suggested alternative.

[0575] Step 7:

[0576] The user uses the device to review and select alternatives. The input is the notification and alternative information sent from the server, and the output is the user's selection. The user reviews the list of suggested alternative flights through the mobile app or web portal and selects the desired alternative.

[0577] Step 8:

[0578] The server confirms the new reservation selected by the user. Based on the selected reservation information, it connects with the operator's reservation system and confirms the new reservation. The input is the information on the alternative means selected by the user, and the output is a confirmation of the new reservation. If the reservation is successful, the details are notified to the user.

[0579] (Application example 1)

[0580] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0581] With conventional systems, it is difficult for users to efficiently find alternative means of transportation when a service is canceled, which leads to a decrease in user satisfaction. Furthermore, in the food delivery industry, delays due to delivery vehicle breakdowns and traffic congestion frequently occur, significantly affecting customer satisfaction and service quality. There is a need for a system that can solve these problems and quickly and efficiently propose and determine alternative means of transportation.

[0582] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0583] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for saving the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing seat availability information and flight information from multiple operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for monitoring the operation status of the delivery means, acquiring delay information and saving it in a database, means for using a generative AI model to propose an alternative delivery means based on the acquired delay information, and means for generating the alternative means proposal as a prompt message and notifying the delivery person and the customer. This makes it possible to quickly and efficiently propose the optimal alternative means to users affected by cancellations or delays and confirm reservations and re-delivery.

[0584] "Operator" refers to the entire organization that provides transportation, such as airlines, bus companies, and train companies.

[0585] "Database" refers to an information system for efficiently managing, storing, searching, and using data.

[0586] A "public API" is a protocol that makes the functions provided by a specific service available externally.

[0587] "Real-time" means nearly simultaneous, i.e., with little delay.

[0588] A "generative AI model" refers to an artificial intelligence technology that uses advanced algorithms to generate specific patterns and predictions from data.

[0589] A "prompt sentence" refers to a sentence that is input as an instruction or question to an AI model.

[0590] "Push notification" refers to the instantaneous delivery of a message from a server to a client.

[0591] "Available seat information" is data on currently available seats.

[0592] "Delay Information" means data containing details of time delays occurring relative to scheduled trips or deliveries.

[0593] "Alternative means" refers to other means used in place of the original means when it is not available.

[0594] "Reservation Data" refers to data including registration information that a user has made in advance to use a specific service.

[0595] System configuration

[0596] The system of the present invention is mainly composed of three elements: a server, a terminal (user device), and a user.

[0597] server

[0598] The server monitors flight status in real time via the operator's database and public API. Specifically, it periodically executes API calls to obtain flight information. The obtained cancellation information is stored in the database and then matched with the reservation data of the corresponding passenger. This matching process identifies affected passengers. Affected passengers are notified of the cancellation information via email, SMS, and mobile app push notifications.

[0599] Next, the server collects real-time seat availability and operation information from multiple operators using APIs and web scraping technology. Based on the collected data, it uses a generative AI model to suggest the best alternatives for the user. These alternatives are generated as prompts and notified to the user. If the user selects a new reservation, the server connects with the corresponding operator's reservation system to confirm the new reservation.

[0600] Similarly, in a delivery service, the server monitors the operation status of delivery vehicles in real time. For example, if a delivery vehicle is delayed, the information is stored in a database and the affected delivery person and customer are notified. An alternative delivery vehicle is proposed using a generative AI model, and a re-delivery is confirmed.

[0601] Terminal (user device)

[0602] The user's device receives the notification sent from the server. The notification is displayed via email, SMS, or the mobile app's push notification function. The user can check the notification and view a list of alternative means in the event of a cancellation or delay. The user selects an alternative means and proceeds to confirm the new reservation through the app.

[0603] User

[0604] The user checks the notification displayed on the terminal and becomes aware of the flight cancellation or delay. Then, the user selects the desired alternative flight or transportation method from multiple alternative flights and transportation methods proposed by the server. After completing the reservation of the selected alternative flight, the user receives a confirmation notification sent from the server.

[0605] Specific examples

[0606] For example, suppose User A requests food delivery from one city to another, and the delivery vehicle gets stuck in traffic. The server monitors the operation status and obtains this information. The server then stores the delay information in a database and notifies the relevant delivery person and customer. The server uses a generative AI model to examine available alternatives and generate the best alternative as a prompt. This prompt is sent to the delivery person and customer. An example prompt is, "Please display the next available delivery option." The delivery person selects from the proposed alternatives and confirms the re-delivery. This process allows for fast and efficient delivery.

[0607] In this way, the collaboration between the server, terminal, and user components realizes a system that can efficiently deal with cancellations of transportation services and delivery delays. This system allows users to quickly and stress-free secure alternative means of transportation, improving the quality of service. It also contributes to cost reductions by streamlining the operations of transportation services and delivery services.

[0608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0609] Step 1:

[0610] The server monitors the operation status in real time via the operator's database and public API. It periodically executes API calls to obtain operation information. The input is the API endpoint of each operator, and the output is the obtained real-time operation information.

[0611] Step 2:

[0612] The server stores the flight cancellation information it acquires in a database and compares it with the reservation data of the corresponding users. The input is the acquired flight information and the reservation data in the database, and the output is a list of users affected by the cancellation. Data processing involves comparing the reservation data with the cancellation information to identify matches.

[0613] Step 3:

[0614] The server sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications. The input is a list of users affected by the cancellation and the notification content, and the output is the notification sent. Specifically, it retrieves the contact information of the affected users and sends notifications via each contact method.

[0615] Step 4:

[0616] The server collects and analyzes seat availability and operation information from multiple operators in real time. The input is data obtained from each operator's seat availability API and web scraping, and the output is a list of collected seat availability information. A generative AI model is used to analyze the data and identify the optimal alternative.

[0617] Step 5:

[0618] The server notifies the user of the alternative suggestions it has generated. The notification is generated as a prompt and sent to the user. The input is the data on alternatives analyzed by the generative AI model, and the output is the sent notification of the suggestions. Specifically, the details of each alternative are generated as a prompt and a notification is sent.

[0619] Step 6:

[0620] The server confirms the new reservation selected by the user. The input is the alternative means selected by the user and its reservation information, and the output is the confirmed new reservation information. The server communicates with the corresponding operator's reservation system to confirm the new reservation and notify the user of the details.

[0621] Step 7:

[0622] The server monitors the operation status of delivery vehicles, acquires delay information, and stores it in a database. The input is the operation information API for delivery vehicles, and the output is the acquired delay information. Data processing involves analyzing the location information of delivery vehicles and generating delay information.

[0623] Step 8:

[0624] Based on the delay information acquired by the server, a generative AI model is used to propose alternative delivery methods. The input is the delay information and data on alternative delivery methods, and the output is the proposed alternative method. Specifically, the delay information is analyzed, and the generative AI model identifies available alternative methods.

[0625] Step 9:

[0626] The server generates a prompt to suggest alternative means and notifies the delivery person and the customer. The input is the generated alternative means data, and the output is the sent suggestion notification. An example of a prompt is "Please display the next available additional delivery means."

[0627] Step 10:

[0628] The terminal receives the notification sent from the server and confirms the notification. The input is the notification content from the server, and the output is the displayed notification. The user confirms the notification and obtains information to select alternatives to cancellations and delays.

[0629] Step 11:

[0630] The terminal displays a list of alternatives proposed by the server and the user selects the desired option. The input is the list of proposed alternatives and the output is the alternative selected by the user. The user makes the selection through the terminal and confirms the new reservation or re-delivery.

[0631] Step 12:

[0632] The server saves the details of the new reservation or redelivery in a database and sends a confirmation to the user. The input is the confirmed new reservation or redelivery information, and the output is the sent confirmation. Specifically, it works by coordinating with the corresponding delivery or transportation system to confirm the reservation or redelivery.

[0633] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0634] The system of the present invention helps users to efficiently find alternative means when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. Specific embodiments of the system are described below.

[0635] System configuration

[0636] This system mainly consists of four elements: the server, the terminal, the user, and the emotion engine. The roles and operations of each are described below.

[0637] server

[0638] 1. Monitoring flight cancellation information

[0639] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[0640] 2. Saving and checking cancellation information

[0641] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[0642] 3. Sending notifications

[0643] The server generates flight cancellation notifications for the relevant users, customizes the content of the notifications based on the user's emotional state, and sends them via communication methods (email, SMS, mobile app push notifications).

[0644] 4. Collection of seat availability and operation information

[0645] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[0646] 5. Proposing alternative solutions

[0647] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best suit the user's desired conditions (destination, time, budget) and emotional state. A list of alternative flight options is generated and notified to the user.

[0648] 6. Confirmation of reservation

[0649] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[0650] Terminal (user device)

[0651] 1. Receiving notifications

[0652] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[0653] 2. Review and select alternatives

[0654] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[0655] User

[0656] 1. Check notifications

[0657] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[0658] 2. Choosing an alternative

[0659] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[0660] 3. Completing the reservation

[0661] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[0662] Emotion Engine

[0663] 1. Emotional awareness

[0664] The emotion engine analyzes the user's emotions based on their voice, text input, and device sensor information. The emotion engine evaluates their stress level and mood and sends the results back to the server.

[0665] 2. Customized Notifications

[0666] The server customizes the notification content based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification content may be changed to a more gentle tone and emphasize prompt action.

[0667] 3. Adjusting priorities

[0668] The server adjusts the content and priorities of alternative suggestions based on the stress level information obtained from the emotion engine. For users with high stress levels, it prioritizes suggestions for the nearest available flight and more comfortable transportation options.

[0669] Specific examples

[0670] For example, if User B has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation:

[0671] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0672] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User B.

[0673] 3. The server retrieves User B's contact information from the database and sends a push notification to the mobile app. Because the emotion engine recognizes User B's high stress state, the notification content is expressed in a particularly gentle manner.

[0674] 4. The server collects information on available seats on other operators and uses the generative AI model to list the flights that best match User B's desired conditions and stress level. In this case, priority is given to the nearest flight and the most comfortable means of transportation.

[0675] 5. User B checks the list of suggested alternative flights through the mobile app and selects "Airline C's flight departing at 14:00."

[0676] 6. The server connects the new reservation to Airline C's reservation system and confirms the reservation. If the reservation is successful, it notifies User B of the corresponding reservation number and details.

[0677] This allows User B to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's call centers and counters.

[0678] The processing flow will be explained below.

[0679] Step 1:

[0680] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is managed by a configured timer, and the server calls the API at regular intervals to obtain flight status information.

[0681] Step 2:

[0682] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the canceled flight (flight number, departure and arrival times, etc.) in the database.

[0683] Step 3:

[0684] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[0685] Step 4:

[0686] The emotion engine evaluates the user's emotional state based on their past voice and text inputs and sensor information, and provides feedback on their stress level and mood to the server.

[0687] Step 5:

[0688] The server customizes the content of the cancellation notification based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification may include more gentle language and an emphasis on immediate action. The server then sends the notification as an email, SMS, or mobile app push notification.

[0689] Step 6:

[0690] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology. The collected data is stored in a database and updated.

[0691] Step 7:

[0692] The server analyzes the collected data using a generative AI model, which considers the user's destination, time, budget, and emotional state to select the most suitable alternative flight or other transportation option, and generates a list of selected alternative flights.

[0693] Step 8:

[0694] The server then notifies the user of the list of alternative flights. The notification content is customized according to the user's emotional state. The notification includes details of the alternative flights (operating company name, departure and arrival times, fares, etc.).

[0695] Step 9:

[0696] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[0697] Step 10:

[0698] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[0699] Step 11:

[0700] After the server confirms the successful reservation, it generates and sends a reservation confirmation notice to the user. The notice contains details of the new reservation (flight number, departure and arrival times, reservation number, etc.). For high-stress users, the notice includes a message emphasizing that the reservation has been completed.

[0701] Examples:

[0702] For example, if User C has booked a flight from Tokyo to Fukuoka, the following scenario will unfold when the server receives information about a flight cancellation.

[0703] 1. The server obtains information about the cancellation of a flight from Tokyo to Fukuoka through the airline's API.

[0704] 2. The server saves the cancellation information in the database and compares it with the reservation data of User C, the relevant user.

[0705] 3. The server retrieves user C's contact information from the database, and the emotion engine analyzes past communication and usage history.

[0706] 4. The emotion engine recognizes User C's high stress state and provides feedback to the server.

[0707] 5. Based on the feedback from the emotion engine, the server generates a notification that emphasizes calm expressions and prompt responses, and sends it as a push notification to the mobile app.

[0708] 6. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best fit your desired conditions and stress level, prioritizing the earliest flights and most comfortable travel options.

[0709] 7. User C checks the list of suggested alternative flights through the mobile app and selects "Operator D's flight departing at 16:00."

[0710] 8. The server connects the new reservation to Operator D's reservation system and confirms the reservation. If the reservation is successful, it retrieves the corresponding reservation details and stores them in the database.

[0711] 9. After the server confirms the success of the reservation, it generates and sends a reservation confirmation notification to User C. For User C who is under high stress, the notification emphasizes that the reservation has been completed.

[0712] This allows User C to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's counters and call centers.

[0713] Example 2

[0714] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0715] When a service is canceled, it is difficult for passengers to find alternative means quickly and efficiently, which tends to increase the stress they feel. Furthermore, conventional systems are unable to provide services that take into account the emotional state of the passenger, so there is a need to reduce the psychological burden on passengers.

[0716] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0717] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for analyzing using a generative AI model and proposing an alternative means that best suits the user's desired conditions and emotional state, and means for confirming the new reservation selected by the user. This allows users to quickly and efficiently find alternative means and further reduces psychological burden by receiving appropriate responses according to their emotional state.

[0718] "Transportation agency" refers to an organization that provides passenger or freight transportation services, such as an airline, railroad, or bus company.

[0719] A "database" is a system that organizes and stores information in a particular way, allowing the information to be efficiently searched, managed, and updated.

[0720] "Public API" refers to an application programming interface that is publicly available for developers to use and is used to connect with other systems and applications.

[0721] "Cancellation information" refers to information indicating that a scheduled flight will not operate for some reason.

[0722] "User" refers to an individual or legal entity that uses the services of an operating company.

[0723] "Notification" refers to the act of transmitting specific information to a specific target, and includes email, SMS, app push notifications, etc.

[0724] "Available seat information" refers to information about available seats on an operating company, and indicates that they are available for reservation.

[0725] "Operation information" refers to general information regarding operations, such as flight schedules, operation status, delays, and cancellations.

[0726] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and generate output according to a purpose.

[0727] An "emotion engine" refers to a system that analyzes a user's emotional state in real time and uses the results to provide an appropriate response.

[0728] "New Booking" means a booking for a new means of transportation selected by a User for a flight or means of transportation that has become necessary due to a cancellation.

[0729] "Real-time" refers to data acquisition and processing occurring almost instantaneously.

[0730] "Matching" refers to the act of comparing different pieces of information to see if they match.

[0731] This invention is a system that helps users efficiently find alternative means of transportation when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The roles and operations of each are described in detail below.

[0732] server

[0733] The server is the core of the system. First, it monitors flight status in real time using the public API of the operator. For example, it uses the Python requests library to send HTTP requests and parses the responses in JSON format. This cancellation information is stored in a PostgreSQL database and compared with user reservation data using SQL queries. Based on the results of the comparison, Twilio API and Firebase Cloud Messaging are used to send cancellation information notifications to affected users.

[0734] The server then polls the APIs of multiple operators to collect seat availability and flight information. The collected information is stored in a database, always kept up to date. This information is then analyzed using a generative AI model (e.g., OpenAI's GPT-3) to suggest optimal alternatives based on the user's desired conditions and emotional state. As a result, a list of alternative flight options is generated and notified to the user again.

[0735] Terminal (user device)

[0736] The device receives a notification sent from the server. Specifically, the notification is displayed via the email app, SMS app, or the mobile app's push notification function (e.g., APNs on iOS or FCM on Android). After receiving the notification, the user can view the proposed alternatives through the mobile app or web portal. Through a user interface built with React Native or Flutter, the user selects the desired option and completes the process to confirm the new reservation.

[0737] User

[0738] The user confirms the notification displayed on the terminal and becomes aware that a flight cancellation has occurred. After receiving the notification, the user selects the desired alternative flight or transportation method from the multiple alternative flights and transportation methods proposed by the server. The server then confirms the new reservation and receives details (such as a reservation number) if successful.

[0739] Emotion Engine

[0740] The emotion engine analyzes the user's emotions in real time based on the user's voice, text input, and device sensor information. This analysis uses natural language processing technology. The emotion engine feeds back the analysis results to the server, which then customizes the notification content based on the results. For example, if the server recognizes that the user is in a state of high stress, it will change the notification content to be particularly gentle. In addition, when the server suggests alternative solutions, it also adjusts the priority taking the user's emotional state into account.

[0741] Specific examples

[0742] For example, if a user has booked a flight from Tokyo to Osaka, and the server receives information from an airline's API that the flight from Tokyo to Osaka has been cancelled, the following scenario will unfold:

[0743] 1. The server stores the acquired cancellation information in a database and compares it with the reservation data of the corresponding user.

[0744] 2. The server retrieves the user's contact information from the database and sends a push notification to the mobile app. The notification is delivered in a particularly gentle manner because the emotion engine recognizes the user's high stress state.

[0745] 3. The server collects information on available seats from other operators and uses a generative AI model to list the flights that best match the user's desired conditions and stress level. For example, the "earliest flight" or "comfortable transportation" is prioritized.

[0746] 4. The user reviews the list of suggested alternative flights through the mobile app and selects one.

[0747] 5. The server confirms the new reservation and notifies the user of the details.

[0748] Such a system allows users to find alternatives quickly and stress-free.

[0749] Additionally, the following example prompt sentence is used as input to the generative AI model: "Please suggest alternative flights that meet the following conditions: destination: Osaka, desired departure time: 14:00, budget: within 30,000 yen, user's emotional state: high stress."

[0750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] The server monitors the operation status in real time through the operator's public API. Specifically, it periodically sends HTTP requests using the Python requests library and receives responses in JSON format. The input data is operation status information obtained from the operator's API endpoint, and the output is cancellation information in JSON format. Once the cancellation information is obtained, the server proceeds to the next step.

[0753] Step 2:

[0754] The server stores the cancellation information in a PostgreSQL database. This operation uses the psycopg2 library. The input data is the cancellation information, and the output is the cancellation entry stored in the database. It then matches this with the user's reservation data using an SQL query to identify the corresponding user.

[0755] Step 3:

[0756] The server generates a notification of the flight cancellation information for the identified user. First, it retrieves the user's contact information from the database. Next, it generates a notification message and queries the emotion engine for the user's emotional state. The input data is the contact information and the flight cancellation information, and the output is a customized notification message. Specifically, it sends the notification using the Twilio API or Firebase Cloud Messaging.

[0757] Step 4:

[0758] The emotion engine analyzes the user's emotional state in real time. The input data is the user's voice, text input, and device sensor information, and the output is an evaluation of the user's emotional state. Based on this evaluation, the server customizes the notification content as needed.

[0759] Step 5:

[0760] The server uses the APIs of multiple operators to collect seat availability information and operation information. Web scraping technology may also be used. The input data is the response of the operator's various APIs, and the output is seat availability information and operation information stored in the database. This ensures that the latest information is always maintained.

[0761] Step 6:

[0762] The server uses a generative AI model (e.g., OpenAI's GPT-3) to analyze the collected seat availability information and flight information. The input data are seat availability information, flight information, the user's desired conditions, and a prompt sentence about the user's emotional state, and the output is a list of suggested alternatives. An example of a prompt sentence is, "Please suggest alternative flights that meet the following conditions: destination: destination, desired departure time: desired time, budget: budget, user's emotional state: emotional state."

[0763] Step 7:

[0764] The server notifies the user of the generated list of suggested alternatives. The input data is the list of suggested alternatives, and the output is a notification sent to the user's device. Specifically, the server sends the notification using the Twilio API or Firebase Cloud Messaging again.

[0765] Step 8:

[0766] The user checks the list of suggested alternatives on their device and selects the option they want. The input data is the notification from the server, and the output is the information about the alternative selected by the user.

[0767] Step 9:

[0768] The server communicates with the corresponding operator's reservation system to confirm a new reservation based on the alternative selected by the user. The input data is the alternative selected by the user, and the output is a confirmation of the reservation. If the reservation is successful, the server notifies the user of the details (such as the reservation number).

[0769] Step 10:

[0770] The user receives a notification confirming that the new reservation has been confirmed and receives further details (such as the reservation number). The input data are the reservation details from the server, and the output is the user's confirmation of the new reservation.

[0771] (Application example 2)

[0772] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0773] In modern society, delays in transportation and delivery services cause significant stress and difficulty for users. Furthermore, due to a lack of systems that can quickly obtain information about cancellations and delays and suggest appropriate alternatives, users are often forced to resolve issues on their own. Furthermore, the lack of response that takes into account the user's emotional state can lead to increased stress and dissatisfaction. The objective of this invention is to solve these problems and provide a system that provides a fast, user-friendly response when transportation or delivery services are delayed.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring operation status in real time from the database and public API of transportation operators, means for saving the acquired cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and operation information from multiple transportation operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for collecting delivery delay information and recognizing the user's emotional state to customize the notification content, and means for suggesting alternative means when delivery is delayed and confirming a new order. This enables a quick and user-friendly response when transportation or delivery service delays occur.

[0775] "Transportation" refers to all modes of transportation, including public transportation and private transportation services.

[0776] "Cancellation Information" means information indicating that a scheduled service of an operator has been cancelled.

[0777] "Emotion engine" refers to a software or hardware component for recognizing and analyzing a user's emotional state.

[0778] "Alternative Means" means the alternative means of travel or service offered to a Passenger in the event of a cancellation or delay.

[0779] "Real-time" means that data is processed as soon as it is acquired, with results available almost immediately.

[0780] "API" stands for Application Programming Interface, and refers to an interface that enables communication between different software applications.

[0781] "Web scraping" refers to the automated process of extracting information from websites.

[0782] A "generative AI model" refers to a model that includes algorithms that use artificial intelligence techniques to analyze data and generate new data or suggestions.

[0783] "Prompt" means an instruction or question input to a generative AI model, and is text used to guide the model to an appropriate output.

[0784] "Notification" means an informational message sent by the System to a User, which may take the form of email, SMS, mobile app push notification, etc.

[0785] "New Order" means a replacement order selected by a User when an existing order is cancelled or delayed.

[0786] "Database" means an electronic system for efficiently storing, retrieving, and managing structured data.

[0787] "User" refers to any individual or organization that uses this system or service.

[0788] "Stress state" indicates the user's mental or emotional state of tension and is analyzed by the emotion engine.

[0789] System configuration

[0790] The embodiment of the present invention consists of three elements: a server, a terminal, and a user. The system provides a fast and user-friendly response when there is a delay in the food delivery service through the cooperative operation of these elements.

[0791] server

[0792] The server has the following main functions:

[0793] 1. Monitoring delay information:

[0794] The server periodically calls the delivery company's API endpoint to monitor the delivery status in real time. The information obtained from this API endpoint is saved in the following format, for example.

[0795] 2. Storage and collation of delayed information:

[0796] The server stores the acquired delay information in a database and compares it with the order data of the corresponding user. At this time, the server uses a database management system (e.g., MySQL or PostgreSQL).

[0797] 3. Sending notifications:

[0798] If a delay is confirmed, the server uses an emotion engine to customize the notification content, taking into account the user's emotional state. For example, if the user is in a high-stress state, the notification content may be changed to a more calming tone.

[0799] 4. Proposing alternatives using generative AI models:

[0800] The server uses the generative AI model to generate the best alternative based on the user's desired conditions and emotional state. As input to the generative AI model, the prompt sentence is set as follows:

[0801] The user's requirements are as follows:

[0802] Chicken dishes

[0803] Delivery time within 30 minutes

[0804] The price is less than 1,500 yen

[0805] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0806] Based on this criteria, suggest the best alternative restaurant and menu.

[0807] 5. Confirming a new order:

[0808] After the user selects an alternative, the server will contact the corresponding delivery system to place a new order, and once the order is placed, the server will notify the user of the order details.

[0809] Terminal (user device)

[0810] The user's device has the following capabilities:

[0811] 1. Receiving notifications:

[0812] Receive delay notifications sent by the server, which can appear as email, SMS, or mobile app push notifications.

[0813] 2. Review and select alternatives:

[0814] Through a mobile app or web portal, users can review the alternatives suggested by the server and choose the option that best suits their needs.

[0815] User

[0816] The user performs the following actions:

[0817] 1. Check notifications:

[0818] The user checks the delay notification displayed on the terminal.

[0819] 2. Alternative Selection:

[0820] From the multiple alternatives proposed by the server, you can choose the one that best suits your requirements.

[0821] 3. Completing a new order:

[0822] Confirm the selected alternative and receive the new order details from the server.

[0823] Specific examples

[0824] For example, if a user orders a chicken dish from a particular food delivery service, the following scenario unfolds when the server detects that the delivery will be delayed:

[0825] 1. The server learns through the delivery company's API that the user's order is 30 minutes late.

[0826] 2. Store the delay information in a database and match it with the order data of the corresponding user.

[0827] 3. Analyze the stress state using an emotion engine based on the user's past feedback.

[0828] 4. If the user is under high stress, notify them of delays in a gentler manner.

[0829] 5. The server uses a generative AI model to suggest alternatives that fit the user's preferences:

[0830] The user's requirements are as follows:

[0831] Chicken dishes

[0832] Delivery time within 30 minutes

[0833] The price is less than 1,500 yen

[0834] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0835] Based on this criteria, suggest the best alternative restaurant and menu.

[0836] 6. The user reviews the proposed alternatives through the mobile app and selects the preferred option.

[0837] 7. The server confirms the new order and notifies the user of the details.

[0838] This allows users to secure alternative means quickly and stress-free, minimizing delivery disruptions.

[0839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0840] Step 1: Monitor latency information

[0841] The server periodically calls the delivery company's API endpoint to monitor delivery status in real time. The input is the API endpoint URL, and the output is the retrieved delivery status information. The server stores this information in a database.

[0842] Step 2: Storing and verifying delay information

[0843] The server saves the acquired delivery status information in a database and compares it with the corresponding user's order data. The input is the delivery status information and the user's order data, and the output is a list of delayed orders. The server identifies delayed orders based on the comparison results.

[0844] Step 3: Sending notifications

[0845] The server sends notifications of delays to the appropriate users. The input is a list of delayed orders and user contact information, and the output is a notification message to the user. Notifications can be sent via email, SMS, or mobile app push notifications.

[0846] Step 4: Analyze emotional state

[0847] The server uses an emotion engine to analyze the user's emotional state. The input is the user's past feedback and sensor information, and the output is the analyzed emotional state (e.g., high stress). The notification content is customized based on this information.

[0848] Step 5: Propose alternatives

[0849] The server uses a generative AI model to suggest optimal alternatives based on the user's desired conditions and emotional state. The input is the user's desired conditions, emotional state, and prompt, and the output is a generated list of alternatives. An example prompt is as follows:

[0850] The user's requirements are as follows:

[0851] Chicken dishes

[0852] Delivery time within 30 minutes

[0853] The price is less than 1,500 yen

[0854] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[0855] Based on this criteria, suggest the best alternative restaurant and menu.

[0856] Step 6: Review and select alternatives

[0857] The device receives the notification and the user checks the list of alternatives sent by the server. The input is the list of alternatives sent by the server and the output is the selected alternative. The user selects the desired option through the mobile app or web portal.

[0858] Step 7: Confirm your new order

[0859] The server places a new order based on the selected alternative. The inputs are the selected alternative and the delivery system's API, and the output is a new order confirmation message. The server notifies the user with the details of the new order.

[0860] These steps enable the system to provide a fast, user-friendly response when food delivery services are delayed, allowing users to secure alternatives without stress.

[0861] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0862] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0863] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0864] [Third embodiment]

[0865] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0866] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0867] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0868] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0869] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0870] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0871] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0872] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0873] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0874] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0875] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0876] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0877] The system of the present invention helps users to efficiently find alternative means when a flight is canceled. A specific embodiment of the system will be described below.

[0878] System configuration

[0879] This system mainly consists of three elements: a server, a terminal, and a user. The roles and operations of each are described below.

[0880] server

[0881] 1. Monitoring flight cancellation information

[0882] The server monitors flight status in real time via the operator's database and public API. This monitoring is carried out periodically with the aim of quickly detecting flight cancellations.

[0883] 2. Saving and checking cancellation information

[0884] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[0885] 3. Sending notifications

[0886] The server then sends flight cancellation notifications to affected users via email, SMS, mobile app push notifications, etc. The notifications include details of the cancellation and next steps to take.

[0887] 4. Collection of seat availability and operation information

[0888] The server collects real-time information on seat availability and operation from multiple operators, using APIs and web scraping technology.

[0889] 5. Proposing alternative solutions

[0890] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best fit the user's desired conditions, thereby suggesting the best options to the user.

[0891] 6. Confirmation of reservation

[0892] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[0893] Terminal (user device)

[0894] 1. Receiving notifications

[0895] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[0896] 2. Review and select alternatives

[0897] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[0898] User

[0899] 1. Check notifications

[0900] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[0901] 2. Choosing an alternative

[0902] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[0903] 3. Completing the reservation

[0904] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[0905] Specific examples

[0906] For example, if User A has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation.

[0907] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0908] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User A.

[0909] 3. The server retrieves User A's contact information from the database and sends a push notification to the mobile app.

[0910] 4. The server collects seat availability information from other operators and uses a generative AI model to list the flights that best meet User A's desired conditions.

[0911] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Airline B's flight departing at 15:00."

[0912] 6. The server connects the new reservation to Airline B's reservation system and confirms the reservation. If the reservation is successful, it notifies User A of the corresponding reservation number and details.

[0913] This allows User A to reserve a replacement flight more quickly and without stress than with conventional methods. It also reduces congestion at the operator's call centers and counters.

[0914] The processing flow will be explained below.

[0915] Step 1:

[0916] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[0917] Step 2:

[0918] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the cancelled flight (flight number, departure and arrival times, etc.) in the database.

[0919] Step 3:

[0920] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[0921] Step 4:

[0922] The server generates and sends a cancellation notification to the identified user via a communication method (email, SMS, or mobile app push notification), including details of the cancellation and next steps to take.

[0923] Step 5:

[0924] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[0925] Step 6:

[0926] The server analyzes the collected data using a generative AI model to select the alternative flight that best suits the user's desired conditions (destination, time, budget, etc.) and generates a list of alternative flight candidates.

[0927] Step 7:

[0928] The server generates a list of alternative flight options based on the analysis results and sends it to the user in the form of a notification, which includes details of the alternative flights (such as the name of the operating company, departure and arrival times, and fares).

[0929] Step 8:

[0930] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[0931] Step 9:

[0932] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[0933] Step 10:

[0934] After the server confirms the successful booking, it generates and sends a booking confirmation notice to the user, which includes details of the new booking (flight number, departure and arrival times, booking number, etc.).

[0935] In this way, the system can provide users with quick and appropriate alternative flight arrangements, minimizing disruption in the event of a flight cancellation.

[0936] Example 1

[0937] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0938] In the past, when a flight was canceled, passengers had to find an alternative themselves, which often required a great deal of time and effort. Furthermore, the response of the airline's call center or service desk was limited, making it difficult for many passengers to find an alternative quickly and efficiently. This situation could lead to a decline in passenger satisfaction and negatively impact the airline's reputation, so a system that could automatically and efficiently suggest alternatives when a flight was canceled was needed.

[0939] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0940] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for using a generative AI model to propose an alternative means that best suits the user's desired conditions, means for notifying the user of the generated alternative means proposal, and means for confirming the new reservation selected by the user, thereby enabling users to find alternative means quickly and efficiently.

[0941] "Operator" refers to public transportation such as airplanes, trains, buses, and ferries, and includes companies and organizations that provide these means of transportation.

[0942] A "database" is a system for efficiently storing and managing large amounts of information, and refers to a structure that allows information to be searched and data to be saved and updated.

[0943] "Public API" means an application programming interface that is publicly available for use by third parties and provides functionality for retrieving and manipulating data.

[0944] "Real-time" means that processing is done immediately and the results are reflected instantly. This refers to the state in which the latest data is available immediately when collecting information on flight status and seat availability.

[0945] "Cancellation information" refers to information about a scheduled flight or service being canceled for any reason.

[0946] "Matching" refers to the act of comparing multiple pieces of data to see if they match.

[0947] "Notification" means a message or alert that conveys specific information to a User and may be sent by means of email, SMS, push notification, or other means.

[0948] "Availability information" refers to data indicating the number and status of available seats on a public transport flight.

[0949] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate new information or suggestions.

[0950] A "proposal" is the act or content of presenting multiple options or solutions from which a user can choose.

[0951] "Reservation" refers to the process and result of reserving a particular service or seat in advance.

[0952] "Push notifications" are alerts or messages sent from the server to a client application, and are a means of instantly conveying information to users.

[0953] "Web scraping" refers to the techniques and methods of automatically extracting data from websites.

[0954] "User" means a person who uses a system or service, and refers to a customer who receives services from an operating agency.

[0955] System configuration

[0956] The system of this invention is composed of three main elements: a server, a terminal, and a user. How each of these elements functions will be described in detail below.

[0957] server

[0958] The server periodically monitors the operator's database and public API to obtain real-time flight status. For example, the public API of an airline is used for this monitoring. If flight cancellation information is confirmed based on the monitored information, the information is saved in the database and compared with the user's reservation data.

[0959] The server then sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications, using Firebase Cloud Messaging or similar.

[0960] Furthermore, the server collects and analyzes real-time seat availability and operation information from multiple operators, for example, using web scraping technology (BeautifulSoup and Scrapy) and public APIs.

[0961] The server then uses the acquired data to suggest the best alternatives to the user using a generative AI model (such as OpenAI's GPT-3), which generates candidates based on a specific prompt.

[0962] Using the following prompt as an example:

[0963] "My flight from Tokyo to Osaka has been cancelled. Please list the next available flight. My requirements are as follows:

[0964] Departure time: As early as possible

[0965] Amount: Within budget

[0966] Convenience: Direct flights preferred

[0967] By inputting the collected data into a generative AI model, the best alternatives are listed and notified to the user. After the user selects an alternative, the server connects with the operator's reservation system based on the selection and confirms the new reservation.

[0968] Terminal (user device)

[0969] The user's device receives real-time notifications from the server via email, SMS, or mobile app push notifications. The user can review the notifications and view details of the proposed alternatives through the mobile app or web portal.

[0970] Once the user has selected the desired alternative, the terminal can send this information to the server and proceed with confirming the new reservation.

[0971] User

[0972] The user can view the cancellation information through notifications displayed on their device, select their preferred alternatives from the suggested options, complete the booking, and finally receive a confirmation from the server that the new transportation has been secured.

[0973] Specific examples

[0974] For example, if User A is booking a flight from Tokyo to Osaka, the following scenario is possible:

[0975] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[0976] 2. The server saves the cancellation information in the database and compares it with User A's reservation data.

[0977] 3. The server sends a flight cancellation notification to User A via a push notification on the mobile app.

[0978] 4. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best suit User A's desired conditions.

[0979] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Next Flight."

[0980] 6. The server confirms the reservation for the "next flight" by connecting with the reservation system and notifies User A of the details.

[0981] This allows passengers to find alternatives quickly and efficiently, and improves the quality of service for operators.

[0982] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0983] Step 1:

[0984] The server monitors flight status in real time from the operator's database or public API. This monitoring process is performed by sending periodic API requests. For example, a request is sent to the API every five minutes to obtain flight cancellation information. The server's input is the response data to the API request, and the output is the result of extracting the cancellation information. If cancellation information is detected, the data is passed on to the next step.

[0985] Step 2:

[0986] The server stores the acquired flight cancellation information in a database and compares it with the reservation data of the corresponding users. First, the cancellation information is inserted into the database, and then the reservation data is searched using an SQL query. The input is the cancellation information and reservation data, and the output is the identification information of the users who have reservations on the canceled flights. This comparison identifies the affected users.

[0987] Step 3:

[0988] The server sends flight cancellation notifications to affected customers via email, SMS, and mobile app push notifications, for example, using Firebase Cloud Messaging. The input is the identity and contact information of the affected customers, and the output is the notification sent.

[0989] Step 4:

[0990] The server collects and analyzes seat availability and operation information from multiple operators in real time. Web scraping technology (BeautifulSoup and Scrapy) and public APIs are used for collection. The input is data obtained from the operators, and the output is a list of seat availability and operation information. The collected information is used in the next step.

[0991] Step 5:

[0992] The server uses a generative AI model to suggest alternatives that best fit the user's desired conditions. A specific prompt is input into the generative AI model (for example, OpenAI's GPT-3), which lists the best alternatives. The input is seat availability information, flight information, and the user's desired conditions, and the output is a suggested alternative. An example of a prompt is, "The flight from Tokyo to Osaka has been canceled, so please list the next available flights. My desired conditions are as follows: Departure time: As early as possible, Price: Within budget, Convenience: Prefer direct flights."

[0993] Step 6:

[0994] The server notifies the user of the generated alternative suggestions via email, SMS, or mobile app push notification. The input is the alternative information, and the output is a notification sent to the user, which includes details of the suggested alternative.

[0995] Step 7:

[0996] The user uses the device to review and select alternatives. The input is the notification and alternative information sent from the server, and the output is the user's selection. The user reviews the list of suggested alternative flights through the mobile app or web portal and selects the desired alternative.

[0997] Step 8:

[0998] The server confirms the new reservation selected by the user. Based on the selected reservation information, it connects with the operator's reservation system and confirms the new reservation. The input is the information on the alternative means selected by the user, and the output is a confirmation of the new reservation. If the reservation is successful, the details are notified to the user.

[0999] (Application example 1)

[1000] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] With conventional systems, it is difficult for users to efficiently find alternative means of transportation when a service is canceled, which leads to a decrease in user satisfaction. Furthermore, in the food delivery industry, delays due to delivery vehicle breakdowns and traffic congestion frequently occur, significantly affecting customer satisfaction and service quality. There is a need for a system that can solve these problems and quickly and efficiently propose and determine alternative means of transportation.

[1002] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1003] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for saving the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing seat availability information and flight information from multiple operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for monitoring the operation status of the delivery means, acquiring delay information and saving it in a database, means for using a generative AI model to propose an alternative delivery means based on the acquired delay information, and means for generating the alternative means proposal as a prompt message and notifying the delivery person and the customer. This makes it possible to quickly and efficiently propose the optimal alternative means to users affected by cancellations or delays and confirm reservations and re-delivery.

[1004] "Operator" refers to the entire organization that provides transportation, such as airlines, bus companies, and train companies.

[1005] "Database" refers to an information system for efficiently managing, storing, searching, and using data.

[1006] A "public API" is a protocol that makes the functions provided by a specific service available externally.

[1007] "Real-time" means nearly simultaneous, i.e., with little delay.

[1008] A "generative AI model" refers to an artificial intelligence technology that uses advanced algorithms to generate specific patterns and predictions from data.

[1009] A "prompt sentence" refers to a sentence that is input as an instruction or question to an AI model.

[1010] "Push notification" refers to the instantaneous delivery of a message from a server to a client.

[1011] "Available seat information" is data on currently available seats.

[1012] "Delay Information" means data containing details of time delays occurring relative to scheduled trips or deliveries.

[1013] "Alternative means" refers to other means used in place of the original means when it is not available.

[1014] "Reservation Data" refers to data including registration information that a user has made in advance to use a specific service.

[1015] System configuration

[1016] The system of the present invention is mainly composed of three elements: a server, a terminal (user device), and a user.

[1017] server

[1018] The server monitors flight status in real time via the operator's database and public API. Specifically, it periodically executes API calls to obtain flight information. The obtained cancellation information is stored in the database and then matched with the reservation data of the corresponding passenger. This matching process identifies affected passengers. Affected passengers are notified of the cancellation information via email, SMS, and mobile app push notifications.

[1019] Next, the server collects real-time seat availability and operation information from multiple operators using APIs and web scraping technology. Based on the collected data, it uses a generative AI model to suggest the best alternatives for the user. These alternatives are generated as prompts and notified to the user. If the user selects a new reservation, the server connects with the corresponding operator's reservation system to confirm the new reservation.

[1020] Similarly, in a delivery service, the server monitors the operation status of delivery vehicles in real time. For example, if a delivery vehicle is delayed, the information is stored in a database and the affected delivery person and customer are notified. An alternative delivery vehicle is proposed using a generative AI model, and a re-delivery is confirmed.

[1021] Terminal (user device)

[1022] The user's device receives the notification sent from the server. The notification is displayed via email, SMS, or the mobile app's push notification function. The user can check the notification and view a list of alternative means in the event of a cancellation or delay. The user selects an alternative means and proceeds to confirm the new reservation through the app.

[1023] User

[1024] The user checks the notification displayed on the terminal and becomes aware of the flight cancellation or delay. Then, the user selects the desired alternative flight or transportation method from multiple alternative flights and transportation methods proposed by the server. After completing the reservation of the selected alternative flight, the user receives a confirmation notification sent from the server.

[1025] Specific examples

[1026] For example, suppose User A requests food delivery from one city to another, and the delivery vehicle gets stuck in traffic. The server monitors the operation status and obtains this information. The server then stores the delay information in a database and notifies the relevant delivery person and customer. The server uses a generative AI model to examine available alternatives and generate the best alternative as a prompt. This prompt is sent to the delivery person and customer. An example prompt is, "Please display the next available delivery option." The delivery person selects from the proposed alternatives and confirms the re-delivery. This process allows for fast and efficient delivery.

[1027] In this way, the collaboration between the server, terminal, and user components realizes a system that can efficiently deal with cancellations of transportation services and delivery delays. This system allows users to quickly and stress-free secure alternative means of transportation, improving the quality of service. It also contributes to cost reductions by streamlining the operations of transportation services and delivery services.

[1028] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1029] Step 1:

[1030] The server monitors the operation status in real time via the operator's database and public API. It periodically executes API calls to obtain operation information. The input is the API endpoint of each operator, and the output is the obtained real-time operation information.

[1031] Step 2:

[1032] The server stores the flight cancellation information it acquires in a database and compares it with the reservation data of the corresponding users. The input is the acquired flight information and the reservation data in the database, and the output is a list of users affected by the cancellation. Data processing involves comparing the reservation data with the cancellation information to identify matches.

[1033] Step 3:

[1034] The server sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications. The input is a list of users affected by the cancellation and the notification content, and the output is the notification sent. Specifically, it retrieves the contact information of the affected users and sends notifications via each contact method.

[1035] Step 4:

[1036] The server collects and analyzes seat availability and operation information from multiple operators in real time. The input is data obtained from each operator's seat availability API and web scraping, and the output is a list of collected seat availability information. A generative AI model is used to analyze the data and identify the optimal alternative.

[1037] Step 5:

[1038] The server notifies the user of the alternative suggestions it has generated. The notification is generated as a prompt and sent to the user. The input is the data on alternatives analyzed by the generative AI model, and the output is the sent notification of the suggestions. Specifically, the details of each alternative are generated as a prompt and a notification is sent.

[1039] Step 6:

[1040] The server confirms the new reservation selected by the user. The input is the alternative means selected by the user and its reservation information, and the output is the confirmed new reservation information. The server communicates with the corresponding operator's reservation system to confirm the new reservation and notify the user of the details.

[1041] Step 7:

[1042] The server monitors the operation status of delivery vehicles, acquires delay information, and stores it in a database. The input is the operation information API for delivery vehicles, and the output is the acquired delay information. Data processing involves analyzing the location information of delivery vehicles and generating delay information.

[1043] Step 8:

[1044] Based on the delay information acquired by the server, a generative AI model is used to propose alternative delivery methods. The input is the delay information and data on alternative delivery methods, and the output is the proposed alternative method. Specifically, the delay information is analyzed, and the generative AI model identifies available alternative methods.

[1045] Step 9:

[1046] The server generates a prompt to suggest alternative means and notifies the delivery person and the customer. The input is the generated alternative means data, and the output is the sent suggestion notification. An example of a prompt is "Please display the next available additional delivery means."

[1047] Step 10:

[1048] The terminal receives the notification sent from the server and confirms the notification. The input is the notification content from the server, and the output is the displayed notification. The user confirms the notification and obtains information to select alternatives to cancellations and delays.

[1049] Step 11:

[1050] The terminal displays a list of alternatives proposed by the server and the user selects the desired option. The input is the list of proposed alternatives and the output is the alternative selected by the user. The user makes the selection through the terminal and confirms the new reservation or re-delivery.

[1051] Step 12:

[1052] The server saves the details of the new reservation or redelivery in a database and sends a confirmation to the user. The input is the confirmed new reservation or redelivery information, and the output is the sent confirmation. Specifically, it works by coordinating with the corresponding delivery or transportation system to confirm the reservation or redelivery.

[1053] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1054] The system of the present invention helps users to efficiently find alternative means when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. Specific embodiments of the system are described below.

[1055] System configuration

[1056] This system mainly consists of four elements: the server, the terminal, the user, and the emotion engine. The roles and operations of each are described below.

[1057] server

[1058] 1. Monitoring flight cancellation information

[1059] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[1060] 2. Saving and checking cancellation information

[1061] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[1062] 3. Sending notifications

[1063] The server generates flight cancellation notifications for the relevant users, customizes the content of the notifications based on the user's emotional state, and sends them via communication methods (email, SMS, mobile app push notifications).

[1064] 4. Collection of seat availability and operation information

[1065] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[1066] 5. Proposing alternative solutions

[1067] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best suit the user's desired conditions (destination, time, budget) and emotional state. A list of alternative flight options is generated and notified to the user.

[1068] 6. Confirmation of reservation

[1069] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[1070] Terminal (user device)

[1071] 1. Receiving notifications

[1072] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[1073] 2. Review and select alternatives

[1074] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[1075] User

[1076] 1. Check notifications

[1077] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[1078] 2. Choosing an alternative

[1079] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[1080] 3. Completing the reservation

[1081] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[1082] Emotion Engine

[1083] 1. Emotional awareness

[1084] The emotion engine analyzes the user's emotions based on their voice, text input, and device sensor information. The emotion engine evaluates their stress level and mood and sends the results back to the server.

[1085] 2. Customized Notifications

[1086] The server customizes the notification content based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification content may be changed to a more gentle tone and emphasize prompt action.

[1087] 3. Adjusting priorities

[1088] The server adjusts the content and priorities of alternative suggestions based on the stress level information obtained from the emotion engine. For users with high stress levels, it prioritizes suggestions for the nearest available flight and more comfortable transportation options.

[1089] Specific examples

[1090] For example, if User B has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation:

[1091] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[1092] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User B.

[1093] 3. The server retrieves User B's contact information from the database and sends a push notification to the mobile app. Because the emotion engine recognizes User B's high stress state, the notification content is expressed in a particularly gentle manner.

[1094] 4. The server collects information on available seats on other operators and uses the generative AI model to list the flights that best match User B's desired conditions and stress level. In this case, priority is given to the nearest flight and the most comfortable means of transportation.

[1095] 5. User B checks the list of suggested alternative flights through the mobile app and selects "Airline C's flight departing at 14:00."

[1096] 6. The server connects the new reservation to Airline C's reservation system and confirms the reservation. If the reservation is successful, it notifies User B of the corresponding reservation number and details.

[1097] This allows User B to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's call centers and counters.

[1098] The processing flow will be explained below.

[1099] Step 1:

[1100] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is managed by a configured timer, and the server calls the API at regular intervals to obtain flight status information.

[1101] Step 2:

[1102] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the canceled flight (flight number, departure and arrival times, etc.) in the database.

[1103] Step 3:

[1104] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[1105] Step 4:

[1106] The emotion engine evaluates the user's emotional state based on their past voice and text inputs and sensor information, and provides feedback on their stress level and mood to the server.

[1107] Step 5:

[1108] The server customizes the content of the cancellation notification based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification may include more gentle language and an emphasis on immediate action. The server then sends the notification as an email, SMS, or mobile app push notification.

[1109] Step 6:

[1110] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology. The collected data is stored in a database and updated.

[1111] Step 7:

[1112] The server analyzes the collected data using a generative AI model, which considers the user's destination, time, budget, and emotional state to select the most suitable alternative flight or other transportation option, and generates a list of selected alternative flights.

[1113] Step 8:

[1114] The server then notifies the user of the list of alternative flights. The notification content is customized according to the user's emotional state. The notification includes details of the alternative flights (operating company name, departure and arrival times, fares, etc.).

[1115] Step 9:

[1116] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[1117] Step 10:

[1118] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[1119] Step 11:

[1120] After the server confirms the successful reservation, it generates and sends a reservation confirmation notice to the user. The notice contains details of the new reservation (flight number, departure and arrival times, reservation number, etc.). For high-stress users, the notice includes a message emphasizing that the reservation has been completed.

[1121] Examples:

[1122] For example, if User C has booked a flight from Tokyo to Fukuoka, the following scenario will unfold when the server receives information about a flight cancellation.

[1123] 1. The server obtains information about the cancellation of a flight from Tokyo to Fukuoka through the airline's API.

[1124] 2. The server saves the cancellation information in the database and compares it with the reservation data of User C, the relevant user.

[1125] 3. The server retrieves user C's contact information from the database, and the emotion engine analyzes past communication and usage history.

[1126] 4. The emotion engine recognizes User C's high stress state and provides feedback to the server.

[1127] 5. Based on the feedback from the emotion engine, the server generates a notification that emphasizes calm expressions and prompt responses, and sends it as a push notification to the mobile app.

[1128] 6. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best fit your desired conditions and stress level, prioritizing the earliest flights and most comfortable travel options.

[1129] 7. User C checks the list of suggested alternative flights through the mobile app and selects "Operator D's flight departing at 16:00."

[1130] 8. The server connects the new reservation to Operator D's reservation system and confirms the reservation. If the reservation is successful, it retrieves the corresponding reservation details and stores them in the database.

[1131] 9. After the server confirms the success of the reservation, it generates and sends a reservation confirmation notification to User C. For User C who is under high stress, the notification emphasizes that the reservation has been completed.

[1132] This allows User C to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's counters and call centers.

[1133] Example 2

[1134] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1135] When a service is canceled, it is difficult for passengers to find alternative means quickly and efficiently, which tends to increase the stress they feel. Furthermore, conventional systems are unable to provide services that take into account the emotional state of the passenger, so there is a need to reduce the psychological burden on passengers.

[1136] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1137] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for analyzing using a generative AI model and proposing an alternative means that best suits the user's desired conditions and emotional state, and means for confirming the new reservation selected by the user. This allows users to quickly and efficiently find alternative means and further reduces psychological burden by receiving appropriate responses according to their emotional state.

[1138] "Transportation agency" refers to an organization that provides passenger or freight transportation services, such as an airline, railroad, or bus company.

[1139] A "database" is a system that organizes and stores information in a particular way, allowing the information to be efficiently searched, managed, and updated.

[1140] "Public API" refers to an application programming interface that is publicly available for developers to use and is used to connect with other systems and applications.

[1141] "Cancellation information" refers to information indicating that a scheduled flight will not operate for some reason.

[1142] "User" refers to an individual or legal entity that uses the services of an operating company.

[1143] "Notification" refers to the act of transmitting specific information to a specific target, and includes email, SMS, app push notifications, etc.

[1144] "Available seat information" refers to information about available seats on an operating company, and indicates that they are available for reservation.

[1145] "Operation information" refers to general information regarding operations, such as flight schedules, operation status, delays, and cancellations.

[1146] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and generate output according to a purpose.

[1147] An "emotion engine" refers to a system that analyzes a user's emotional state in real time and uses the results to provide an appropriate response.

[1148] "New Booking" means a booking for a new means of transportation selected by a User for a flight or means of transportation that has become necessary due to a cancellation.

[1149] "Real-time" refers to data acquisition and processing occurring almost instantaneously.

[1150] "Matching" refers to the act of comparing different pieces of information to see if they match.

[1151] This invention is a system that helps users efficiently find alternative means of transportation when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The roles and operations of each are described in detail below.

[1152] server

[1153] The server is the core of the system. First, it monitors flight status in real time using the public API of the operator. For example, it uses the Python requests library to send HTTP requests and parses the responses in JSON format. This cancellation information is stored in a PostgreSQL database and compared with user reservation data using SQL queries. Based on the results of the comparison, Twilio API and Firebase Cloud Messaging are used to send cancellation information notifications to affected users.

[1154] The server then polls the APIs of multiple operators to collect seat availability and flight information. The collected information is stored in a database, always kept up to date. This information is then analyzed using a generative AI model (e.g., OpenAI's GPT-3) to suggest optimal alternatives based on the user's desired conditions and emotional state. As a result, a list of alternative flight options is generated and notified to the user again.

[1155] Terminal (user device)

[1156] The device receives a notification sent from the server. Specifically, the notification is displayed via the email app, SMS app, or the mobile app's push notification function (e.g., APNs on iOS or FCM on Android). After receiving the notification, the user can view the proposed alternatives through the mobile app or web portal. Through a user interface built with React Native or Flutter, the user selects the desired option and completes the process to confirm the new reservation.

[1157] User

[1158] The user confirms the notification displayed on the terminal and becomes aware that a flight cancellation has occurred. After receiving the notification, the user selects the desired alternative flight or transportation method from the multiple alternative flights and transportation methods proposed by the server. The server then confirms the new reservation and receives details (such as a reservation number) if successful.

[1159] Emotion Engine

[1160] The emotion engine analyzes the user's emotions in real time based on the user's voice, text input, and device sensor information. This analysis uses natural language processing technology. The emotion engine feeds back the analysis results to the server, which then customizes the notification content based on the results. For example, if the server recognizes that the user is in a state of high stress, it will change the notification content to be particularly gentle. In addition, when the server suggests alternative solutions, it also adjusts the priority taking the user's emotional state into account.

[1161] Specific examples

[1162] For example, if a user has booked a flight from Tokyo to Osaka, and the server receives information from an airline's API that the flight from Tokyo to Osaka has been cancelled, the following scenario will unfold:

[1163] 1. The server stores the acquired cancellation information in a database and compares it with the reservation data of the corresponding user.

[1164] 2. The server retrieves the user's contact information from the database and sends a push notification to the mobile app. The notification is delivered in a particularly gentle manner because the emotion engine recognizes the user's high stress state.

[1165] 3. The server collects information on available seats from other operators and uses a generative AI model to list the flights that best match the user's desired conditions and stress level. For example, the "earliest flight" or "comfortable transportation" is prioritized.

[1166] 4. The user reviews the list of suggested alternative flights through the mobile app and selects one.

[1167] 5. The server confirms the new reservation and notifies the user of the details.

[1168] Such a system allows users to find alternatives quickly and stress-free.

[1169] Additionally, the following example prompt sentence is used as input to the generative AI model: "Please suggest alternative flights that meet the following conditions: destination: Osaka, desired departure time: 14:00, budget: within 30,000 yen, user's emotional state: high stress."

[1170] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1171] Step 1:

[1172] The server monitors the operation status in real time through the operator's public API. Specifically, it periodically sends HTTP requests using the Python requests library and receives responses in JSON format. The input data is operation status information obtained from the operator's API endpoint, and the output is cancellation information in JSON format. Once the cancellation information is obtained, the server proceeds to the next step.

[1173] Step 2:

[1174] The server stores the cancellation information in a PostgreSQL database. This operation uses the psycopg2 library. The input data is the cancellation information, and the output is the cancellation entry stored in the database. It then matches this with the user's reservation data using an SQL query to identify the corresponding user.

[1175] Step 3:

[1176] The server generates a notification of the flight cancellation information for the identified user. First, it retrieves the user's contact information from the database. Next, it generates a notification message and queries the emotion engine for the user's emotional state. The input data is the contact information and the flight cancellation information, and the output is a customized notification message. Specifically, it sends the notification using the Twilio API or Firebase Cloud Messaging.

[1177] Step 4:

[1178] The emotion engine analyzes the user's emotional state in real time. The input data is the user's voice, text input, and device sensor information, and the output is an evaluation of the user's emotional state. Based on this evaluation, the server customizes the notification content as needed.

[1179] Step 5:

[1180] The server uses the APIs of multiple operators to collect seat availability information and operation information. Web scraping technology may also be used. The input data is the response of the operator's various APIs, and the output is seat availability information and operation information stored in the database. This ensures that the latest information is always maintained.

[1181] Step 6:

[1182] The server uses a generative AI model (e.g., OpenAI's GPT-3) to analyze the collected seat availability information and flight information. The input data are seat availability information, flight information, the user's desired conditions, and a prompt sentence about the user's emotional state, and the output is a list of suggested alternatives. An example of a prompt sentence is, "Please suggest alternative flights that meet the following conditions: destination: destination, desired departure time: desired time, budget: budget, user's emotional state: emotional state."

[1183] Step 7:

[1184] The server notifies the user of the generated list of suggested alternatives. The input data is the list of suggested alternatives, and the output is a notification sent to the user's device. Specifically, the server sends the notification using the Twilio API or Firebase Cloud Messaging again.

[1185] Step 8:

[1186] The user checks the list of suggested alternatives on their device and selects the option they want. The input data is the notification from the server, and the output is the information about the alternative selected by the user.

[1187] Step 9:

[1188] The server communicates with the corresponding operator's reservation system to confirm a new reservation based on the alternative selected by the user. The input data is the alternative selected by the user, and the output is a confirmation of the reservation. If the reservation is successful, the server notifies the user of the details (such as the reservation number).

[1189] Step 10:

[1190] The user receives a notification confirming that the new reservation has been confirmed and receives further details (such as the reservation number). The input data are the reservation details from the server, and the output is the user's confirmation of the new reservation.

[1191] (Application example 2)

[1192] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1193] In modern society, delays in transportation and delivery services cause significant stress and difficulty for users. Furthermore, due to a lack of systems that can quickly obtain information about cancellations and delays and suggest appropriate alternatives, users are often forced to resolve issues on their own. Furthermore, the lack of response that takes into account the user's emotional state can lead to increased stress and dissatisfaction. The objective of this invention is to solve these problems and provide a system that provides a fast, user-friendly response when transportation or delivery services are delayed.

[1194] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring operation status in real time from the database and public API of transportation operators, means for saving the acquired cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and operation information from multiple transportation operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for collecting delivery delay information and recognizing the user's emotional state to customize the notification content, and means for suggesting alternative means when delivery is delayed and confirming a new order. This enables a quick and user-friendly response when transportation or delivery service delays occur.

[1195] "Transportation" refers to all modes of transportation, including public transportation and private transportation services.

[1196] "Cancellation Information" means information indicating that a scheduled service of an operator has been cancelled.

[1197] "Emotion engine" refers to a software or hardware component for recognizing and analyzing a user's emotional state.

[1198] "Alternative Means" means the alternative means of travel or service offered to a Passenger in the event of a cancellation or delay.

[1199] "Real-time" means that data is processed as soon as it is acquired, with results available almost immediately.

[1200] "API" stands for Application Programming Interface, and refers to an interface that enables communication between different software applications.

[1201] "Web scraping" refers to the automated process of extracting information from websites.

[1202] A "generative AI model" refers to a model that includes algorithms that use artificial intelligence techniques to analyze data and generate new data or suggestions.

[1203] "Prompt" means an instruction or question input to a generative AI model, and is text used to guide the model to an appropriate output.

[1204] "Notification" means an informational message sent by the System to a User, which may take the form of email, SMS, mobile app push notification, etc.

[1205] "New Order" means a replacement order selected by a User when an existing order is cancelled or delayed.

[1206] "Database" means an electronic system for efficiently storing, retrieving, and managing structured data.

[1207] "User" refers to any individual or organization that uses this system or service.

[1208] "Stress state" indicates the user's mental or emotional state of tension and is analyzed by the emotion engine.

[1209] System configuration

[1210] The embodiment of the present invention consists of three elements: a server, a terminal, and a user. The system provides a fast and user-friendly response when there is a delay in the food delivery service through the cooperative operation of these elements.

[1211] server

[1212] The server has the following main functions:

[1213] 1. Monitoring delay information:

[1214] The server periodically calls the delivery company's API endpoint to monitor the delivery status in real time. The information obtained from this API endpoint is saved in the following format, for example.

[1215] 2. Storage and collation of delayed information:

[1216] The server stores the acquired delay information in a database and compares it with the order data of the corresponding user. At this time, the server uses a database management system (e.g., MySQL or PostgreSQL).

[1217] 3. Sending notifications:

[1218] If a delay is confirmed, the server uses an emotion engine to customize the notification content, taking into account the user's emotional state. For example, if the user is in a high-stress state, the notification content may be changed to a more calming tone.

[1219] 4. Proposing alternatives using generative AI models:

[1220] The server uses the generative AI model to generate the best alternative based on the user's desired conditions and emotional state. As input to the generative AI model, the prompt sentence is set as follows:

[1221] The user's requirements are as follows:

[1222] Chicken dishes

[1223] Delivery time within 30 minutes

[1224] The price is less than 1,500 yen

[1225] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1226] Based on this criteria, suggest the best alternative restaurant and menu.

[1227] 5. Confirming a new order:

[1228] After the user selects an alternative, the server will contact the corresponding delivery system to place a new order, and once the order is placed, the server will notify the user of the order details.

[1229] Terminal (user device)

[1230] The user's device has the following capabilities:

[1231] 1. Receiving notifications:

[1232] Receive delay notifications sent by the server, which can appear as email, SMS, or mobile app push notifications.

[1233] 2. Review and select alternatives:

[1234] Through a mobile app or web portal, users can review the alternatives suggested by the server and choose the option that best suits their needs.

[1235] User

[1236] The user performs the following actions:

[1237] 1. Check notifications:

[1238] The user checks the delay notification displayed on the terminal.

[1239] 2. Alternative Selection:

[1240] From the multiple alternatives proposed by the server, you can choose the one that best suits your requirements.

[1241] 3. Completing a new order:

[1242] Confirm the selected alternative and receive the new order details from the server.

[1243] Specific examples

[1244] For example, if a user orders a chicken dish from a particular food delivery service, the following scenario unfolds when the server detects that the delivery will be delayed:

[1245] 1. The server learns through the delivery company's API that the user's order is 30 minutes late.

[1246] 2. Store the delay information in a database and match it with the order data of the corresponding user.

[1247] 3. Analyze the stress state using an emotion engine based on the user's past feedback.

[1248] 4. If the user is under high stress, notify them of delays in a gentler manner.

[1249] 5. The server uses a generative AI model to suggest alternatives that fit the user's preferences:

[1250] The user's requirements are as follows:

[1251] Chicken dishes

[1252] Delivery time within 30 minutes

[1253] The price is less than 1,500 yen

[1254] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1255] Based on this criteria, suggest the best alternative restaurant and menu.

[1256] 6. The user reviews the proposed alternatives through the mobile app and selects the preferred option.

[1257] 7. The server confirms the new order and notifies the user of the details.

[1258] This allows users to secure alternative means quickly and stress-free, minimizing delivery disruptions.

[1259] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1260] Step 1: Monitor latency information

[1261] The server periodically calls the delivery company's API endpoint to monitor delivery status in real time. The input is the API endpoint URL, and the output is the retrieved delivery status information. The server stores this information in a database.

[1262] Step 2: Storing and verifying delay information

[1263] The server saves the acquired delivery status information in a database and compares it with the corresponding user's order data. The input is the delivery status information and the user's order data, and the output is a list of delayed orders. The server identifies delayed orders based on the comparison results.

[1264] Step 3: Sending notifications

[1265] The server sends notifications of delays to the appropriate users. The input is a list of delayed orders and user contact information, and the output is a notification message to the user. Notifications can be sent via email, SMS, or mobile app push notifications.

[1266] Step 4: Analyze emotional state

[1267] The server uses an emotion engine to analyze the user's emotional state. The input is the user's past feedback and sensor information, and the output is the analyzed emotional state (e.g., high stress). The notification content is customized based on this information.

[1268] Step 5: Propose alternatives

[1269] The server uses a generative AI model to suggest optimal alternatives based on the user's desired conditions and emotional state. The input is the user's desired conditions, emotional state, and prompt, and the output is a generated list of alternatives. An example prompt is as follows:

[1270] The user's requirements are as follows:

[1271] Chicken dishes

[1272] Delivery time within 30 minutes

[1273] The price is less than 1,500 yen

[1274] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1275] Based on this criteria, suggest the best alternative restaurant and menu.

[1276] Step 6: Review and select alternatives

[1277] The device receives the notification and the user checks the list of alternatives sent by the server. The input is the list of alternatives sent by the server and the output is the selected alternative. The user selects the desired option through the mobile app or web portal.

[1278] Step 7: Confirm your new order

[1279] The server places a new order based on the selected alternative. The inputs are the selected alternative and the delivery system's API, and the output is a new order confirmation message. The server notifies the user with the details of the new order.

[1280] These steps enable the system to provide a fast, user-friendly response when food delivery services are delayed, allowing users to secure alternatives without stress.

[1281] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1282] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1283] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1284] [Fourth embodiment]

[1285] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1286] 7, a 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.

[1287] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1288] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1289] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1290] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1291] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1292] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1293] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1294] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1295] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1296] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1297] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1298] The system of the present invention helps users to efficiently find alternative means when a flight is canceled. A specific embodiment of the system will be described below.

[1299] System configuration

[1300] This system mainly consists of three elements: a server, a terminal, and a user. The roles and operations of each are described below.

[1301] server

[1302] 1. Monitoring flight cancellation information

[1303] The server monitors flight status in real time via the operator's database and public API. This monitoring is carried out periodically with the aim of quickly detecting flight cancellations.

[1304] 2. Saving and checking cancellation information

[1305] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[1306] 3. Sending notifications

[1307] The server then sends flight cancellation notifications to affected users via email, SMS, mobile app push notifications, etc. The notifications include details of the cancellation and next steps to take.

[1308] 4. Collection of seat availability and operation information

[1309] The server collects real-time information on seat availability and operation from multiple operators, using APIs and web scraping technology.

[1310] 5. Proposing alternative solutions

[1311] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best fit the user's desired conditions, thereby suggesting the best options to the user.

[1312] 6. Confirmation of reservation

[1313] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[1314] Terminal (user device)

[1315] 1. Receiving notifications

[1316] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[1317] 2. Review and select alternatives

[1318] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[1319] User

[1320] 1. Check notifications

[1321] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[1322] 2. Choosing an alternative

[1323] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[1324] 3. Completing the reservation

[1325] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[1326] Specific examples

[1327] For example, if User A has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation.

[1328] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[1329] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User A.

[1330] 3. The server retrieves User A's contact information from the database and sends a push notification to the mobile app.

[1331] 4. The server collects seat availability information from other operators and uses a generative AI model to list the flights that best meet User A's desired conditions.

[1332] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Airline B's flight departing at 15:00."

[1333] 6. The server connects the new reservation to Airline B's reservation system and confirms the reservation. If the reservation is successful, it notifies User A of the corresponding reservation number and details.

[1334] This allows User A to reserve a replacement flight more quickly and without stress than with conventional methods. It also reduces congestion at the operator's call centers and counters.

[1335] The processing flow will be explained below.

[1336] Step 1:

[1337] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[1338] Step 2:

[1339] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the cancelled flight (flight number, departure and arrival times, etc.) in the database.

[1340] Step 3:

[1341] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[1342] Step 4:

[1343] The server generates and sends a cancellation notification to the identified user via a communication method (email, SMS, or mobile app push notification), including details of the cancellation and next steps to take.

[1344] Step 5:

[1345] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[1346] Step 6:

[1347] The server analyzes the collected data using a generative AI model to select the alternative flight that best suits the user's desired conditions (destination, time, budget, etc.) and generates a list of alternative flight candidates.

[1348] Step 7:

[1349] The server generates a list of alternative flight options based on the analysis results and sends it to the user in the form of a notification, which includes details of the alternative flights (such as the name of the operating company, departure and arrival times, and fares).

[1350] Step 8:

[1351] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[1352] Step 9:

[1353] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[1354] Step 10:

[1355] After the server confirms the successful booking, it generates and sends a booking confirmation notice to the user, which includes details of the new booking (flight number, departure and arrival times, booking number, etc.).

[1356] In this way, the system can provide users with quick and appropriate alternative flight arrangements, minimizing disruption in the event of a flight cancellation.

[1357] Example 1

[1358] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1359] In the past, when a flight was canceled, passengers had to find an alternative themselves, which often required a great deal of time and effort. Furthermore, the response of the airline's call center or service desk was limited, making it difficult for many passengers to find an alternative quickly and efficiently. This situation could lead to a decline in passenger satisfaction and negatively impact the airline's reputation, so a system that could automatically and efficiently suggest alternatives when a flight was canceled was needed.

[1360] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1361] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for using a generative AI model to propose an alternative means that best suits the user's desired conditions, means for notifying the user of the generated alternative means proposal, and means for confirming the new reservation selected by the user, thereby enabling users to find alternative means quickly and efficiently.

[1362] "Operator" refers to public transportation such as airplanes, trains, buses, and ferries, and includes companies and organizations that provide these means of transportation.

[1363] A "database" is a system for efficiently storing and managing large amounts of information, and refers to a structure that allows information to be searched and data to be saved and updated.

[1364] "Public API" means an application programming interface that is publicly available for use by third parties and provides functionality for retrieving and manipulating data.

[1365] "Real-time" means that processing is done immediately and the results are reflected instantly. This refers to the state in which the latest data is available immediately when collecting information on flight status and seat availability.

[1366] "Cancellation information" refers to information about a scheduled flight or service being canceled for any reason.

[1367] "Matching" refers to the act of comparing multiple pieces of data to see if they match.

[1368] "Notification" means a message or alert that conveys specific information to a User and may be sent by means of email, SMS, push notification, or other means.

[1369] "Availability information" refers to data indicating the number and status of available seats on a public transport flight.

[1370] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate new information or suggestions.

[1371] A "proposal" is the act or content of presenting multiple options or solutions from which a user can choose.

[1372] "Reservation" refers to the process and result of reserving a particular service or seat in advance.

[1373] "Push notifications" are alerts or messages sent from the server to a client application, and are a means of instantly conveying information to users.

[1374] "Web scraping" refers to the techniques and methods of automatically extracting data from websites.

[1375] "User" means a person who uses a system or service, and refers to a customer who receives services from an operating agency.

[1376] System configuration

[1377] The system of this invention is composed of three main elements: a server, a terminal, and a user. How each of these elements functions will be described in detail below.

[1378] server

[1379] The server periodically monitors the operator's database and public API to obtain real-time flight status. For example, the public API of an airline is used for this monitoring. If flight cancellation information is confirmed based on the monitored information, the information is saved in the database and compared with the user's reservation data.

[1380] The server then sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications, using Firebase Cloud Messaging or similar.

[1381] Furthermore, the server collects and analyzes real-time seat availability and operation information from multiple operators, for example, using web scraping technology (BeautifulSoup and Scrapy) and public APIs.

[1382] The server then uses the acquired data to suggest the best alternatives to the user using a generative AI model (such as OpenAI's GPT-3), which generates candidates based on a specific prompt.

[1383] Using the following prompt as an example:

[1384] "My flight from Tokyo to Osaka has been cancelled. Please list the next available flight. My requirements are as follows:

[1385] Departure time: As early as possible

[1386] Amount: Within budget

[1387] Convenience: Direct flights preferred

[1388] By inputting the collected data into a generative AI model, the best alternatives are listed and notified to the user. After the user selects an alternative, the server connects with the operator's reservation system based on the selection and confirms the new reservation.

[1389] Terminal (user device)

[1390] The user's device receives real-time notifications from the server via email, SMS, or mobile app push notifications. The user can review the notifications and view details of the proposed alternatives through the mobile app or web portal.

[1391] Once the user has selected the desired alternative, the terminal can send this information to the server and proceed with confirming the new reservation.

[1392] User

[1393] The user can view the cancellation information through notifications displayed on their device, select their preferred alternatives from the suggested options, complete the booking, and finally receive a confirmation from the server that the new transportation has been secured.

[1394] Specific examples

[1395] For example, if User A is booking a flight from Tokyo to Osaka, the following scenario is possible:

[1396] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[1397] 2. The server saves the cancellation information in the database and compares it with User A's reservation data.

[1398] 3. The server sends a flight cancellation notification to User A via a push notification on the mobile app.

[1399] 4. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best suit User A's desired conditions.

[1400] 5. User A checks the list of suggested alternative flights through the mobile app and selects "Next Flight."

[1401] 6. The server confirms the reservation for the "next flight" by connecting with the reservation system and notifies User A of the details.

[1402] This allows passengers to find alternatives quickly and efficiently, and improves the quality of service for operators.

[1403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1404] Step 1:

[1405] The server monitors flight status in real time from the operator's database or public API. This monitoring process is performed by sending periodic API requests. For example, a request is sent to the API every five minutes to obtain flight cancellation information. The server's input is the response data to the API request, and the output is the result of extracting the cancellation information. If cancellation information is detected, the data is passed on to the next step.

[1406] Step 2:

[1407] The server stores the acquired flight cancellation information in a database and compares it with the reservation data of the corresponding users. First, the cancellation information is inserted into the database, and then the reservation data is searched using an SQL query. The input is the cancellation information and reservation data, and the output is the identification information of the users who have reservations on the canceled flights. This comparison identifies the affected users.

[1408] Step 3:

[1409] The server sends flight cancellation notifications to affected customers via email, SMS, and mobile app push notifications, for example, using Firebase Cloud Messaging. The input is the identity and contact information of the affected customers, and the output is the notification sent.

[1410] Step 4:

[1411] The server collects and analyzes seat availability and operation information from multiple operators in real time. Web scraping technology (BeautifulSoup and Scrapy) and public APIs are used for collection. The input is data obtained from the operators, and the output is a list of seat availability and operation information. The collected information is used in the next step.

[1412] Step 5:

[1413] The server uses a generative AI model to suggest alternatives that best fit the user's desired conditions. A specific prompt is input into the generative AI model (for example, OpenAI's GPT-3), which lists the best alternatives. The input is seat availability information, flight information, and the user's desired conditions, and the output is a suggested alternative. An example of a prompt is, "The flight from Tokyo to Osaka has been canceled, so please list the next available flights. My desired conditions are as follows: Departure time: As early as possible, Price: Within budget, Convenience: Prefer direct flights."

[1414] Step 6:

[1415] The server notifies the user of the generated alternative suggestions via email, SMS, or mobile app push notification. The input is the alternative information, and the output is a notification sent to the user, which includes details of the suggested alternative.

[1416] Step 7:

[1417] The user uses the device to review and select alternatives. The input is the notification and alternative information sent from the server, and the output is the user's selection. The user reviews the list of suggested alternative flights through the mobile app or web portal and selects the desired alternative.

[1418] Step 8:

[1419] The server confirms the new reservation selected by the user. Based on the selected reservation information, it connects with the operator's reservation system and confirms the new reservation. The input is the information on the alternative means selected by the user, and the output is a confirmation of the new reservation. If the reservation is successful, the details are notified to the user.

[1420] (Application example 1)

[1421] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1422] With conventional systems, it is difficult for users to efficiently find alternative means of transportation when a service is canceled, which leads to a decrease in user satisfaction. Furthermore, in the food delivery industry, delays due to delivery vehicle breakdowns and traffic congestion frequently occur, significantly affecting customer satisfaction and service quality. There is a need for a system that can solve these problems and quickly and efficiently propose and determine alternative means of transportation.

[1423] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1424] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for saving the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing seat availability information and flight information from multiple operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for monitoring the operation status of the delivery means, acquiring delay information and saving it in a database, means for using a generative AI model to propose an alternative delivery means based on the acquired delay information, and means for generating the alternative means proposal as a prompt message and notifying the delivery person and the customer. This makes it possible to quickly and efficiently propose the optimal alternative means to users affected by cancellations or delays and confirm reservations and re-delivery.

[1425] "Operator" refers to the entire organization that provides transportation, such as airlines, bus companies, and train companies.

[1426] "Database" refers to an information system for efficiently managing, storing, searching, and using data.

[1427] A "public API" is a protocol that makes the functions provided by a specific service available externally.

[1428] "Real-time" means nearly simultaneous, i.e., with little delay.

[1429] A "generative AI model" refers to an artificial intelligence technology that uses advanced algorithms to generate specific patterns and predictions from data.

[1430] A "prompt sentence" refers to a sentence that is input as an instruction or question to an AI model.

[1431] "Push notification" refers to the instantaneous delivery of a message from a server to a client.

[1432] "Available seat information" is data on currently available seats.

[1433] "Delay Information" means data containing details of time delays occurring relative to scheduled trips or deliveries.

[1434] "Alternative means" refers to other means used in place of the original means when it is not available.

[1435] "Reservation Data" refers to data including registration information that a user has made in advance to use a specific service.

[1436] System configuration

[1437] The system of the present invention is mainly composed of three elements: a server, a terminal (user device), and a user.

[1438] server

[1439] The server monitors flight status in real time via the operator's database and public API. Specifically, it periodically executes API calls to obtain flight information. The obtained cancellation information is stored in the database and then matched with the reservation data of the corresponding passenger. This matching process identifies affected passengers. Affected passengers are notified of the cancellation information via email, SMS, and mobile app push notifications.

[1440] Next, the server collects real-time seat availability and operation information from multiple operators using APIs and web scraping technology. Based on the collected data, it uses a generative AI model to suggest the best alternatives for the user. These alternatives are generated as prompts and notified to the user. If the user selects a new reservation, the server connects with the corresponding operator's reservation system to confirm the new reservation.

[1441] Similarly, in a delivery service, the server monitors the operation status of delivery vehicles in real time. For example, if a delivery vehicle is delayed, the information is stored in a database and the affected delivery person and customer are notified. An alternative delivery vehicle is proposed using a generative AI model, and a re-delivery is confirmed.

[1442] Terminal (user device)

[1443] The user's device receives the notification sent from the server. The notification is displayed via email, SMS, or the mobile app's push notification function. The user can check the notification and view a list of alternative means in the event of a cancellation or delay. The user selects an alternative means and proceeds to confirm the new reservation through the app.

[1444] User

[1445] The user checks the notification displayed on the terminal and becomes aware of the flight cancellation or delay. Then, the user selects the desired alternative flight or transportation method from multiple alternative flights and transportation methods proposed by the server. After completing the reservation of the selected alternative flight, the user receives a confirmation notification sent from the server.

[1446] Specific examples

[1447] For example, suppose User A requests food delivery from one city to another, and the delivery vehicle gets stuck in traffic. The server monitors the operation status and obtains this information. The server then stores the delay information in a database and notifies the relevant delivery person and customer. The server uses a generative AI model to examine available alternatives and generate the best alternative as a prompt. This prompt is sent to the delivery person and customer. An example prompt is, "Please display the next available delivery option." The delivery person selects from the proposed alternatives and confirms the re-delivery. This process allows for fast and efficient delivery.

[1448] In this way, the collaboration between the server, terminal, and user components realizes a system that can efficiently deal with cancellations of transportation services and delivery delays. This system allows users to quickly and stress-free secure alternative means of transportation, improving the quality of service. It also contributes to cost reductions by streamlining the operations of transportation services and delivery services.

[1449] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1450] Step 1:

[1451] The server monitors the operation status in real time via the operator's database and public API. It periodically executes API calls to obtain operation information. The input is the API endpoint of each operator, and the output is the obtained real-time operation information.

[1452] Step 2:

[1453] The server stores the flight cancellation information it acquires in a database and compares it with the reservation data of the corresponding users. The input is the acquired flight information and the reservation data in the database, and the output is a list of users affected by the cancellation. Data processing involves comparing the reservation data with the cancellation information to identify matches.

[1454] Step 3:

[1455] The server sends flight cancellation notifications to the affected users via email, SMS, and mobile app push notifications. The input is a list of users affected by the cancellation and the notification content, and the output is the notification sent. Specifically, it retrieves the contact information of the affected users and sends notifications via each contact method.

[1456] Step 4:

[1457] The server collects and analyzes seat availability and operation information from multiple operators in real time. The input is data obtained from each operator's seat availability API and web scraping, and the output is a list of collected seat availability information. A generative AI model is used to analyze the data and identify the optimal alternative.

[1458] Step 5:

[1459] The server notifies the user of the alternative suggestions it has generated. The notification is generated as a prompt and sent to the user. The input is the data on alternatives analyzed by the generative AI model, and the output is the sent notification of the suggestions. Specifically, the details of each alternative are generated as a prompt and a notification is sent.

[1460] Step 6:

[1461] The server confirms the new reservation selected by the user. The input is the alternative means selected by the user and its reservation information, and the output is the confirmed new reservation information. The server communicates with the corresponding operator's reservation system to confirm the new reservation and notify the user of the details.

[1462] Step 7:

[1463] The server monitors the operation status of delivery vehicles, acquires delay information, and stores it in a database. The input is the operation information API for delivery vehicles, and the output is the acquired delay information. Data processing involves analyzing the location information of delivery vehicles and generating delay information.

[1464] Step 8:

[1465] Based on the delay information acquired by the server, a generative AI model is used to propose alternative delivery methods. The input is the delay information and data on alternative delivery methods, and the output is the proposed alternative method. Specifically, the delay information is analyzed, and the generative AI model identifies available alternative methods.

[1466] Step 9:

[1467] The server generates a prompt to suggest alternative means and notifies the delivery person and the customer. The input is the generated alternative means data, and the output is the sent suggestion notification. An example of a prompt is "Please display the next available additional delivery means."

[1468] Step 10:

[1469] The terminal receives the notification sent from the server and confirms the notification. The input is the notification content from the server, and the output is the displayed notification. The user confirms the notification and obtains information to select alternatives to cancellations and delays.

[1470] Step 11:

[1471] The terminal displays a list of alternatives proposed by the server and the user selects the desired option. The input is the list of proposed alternatives and the output is the alternative selected by the user. The user makes the selection through the terminal and confirms the new reservation or re-delivery.

[1472] Step 12:

[1473] The server saves the details of the new reservation or redelivery in a database and sends a confirmation to the user. The input is the confirmed new reservation or redelivery information, and the output is the sent confirmation. Specifically, it works by coordinating with the corresponding delivery or transportation system to confirm the reservation or redelivery.

[1474] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1475] The system of the present invention helps users to efficiently find alternative means when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. Specific embodiments of the system are described below.

[1476] System configuration

[1477] This system mainly consists of four elements: the server, the terminal, the user, and the emotion engine. The roles and operations of each are described below.

[1478] server

[1479] 1. Monitoring flight cancellation information

[1480] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is controlled by a configured timer, and the server calls the API to retrieve and analyze flight status information.

[1481] 2. Saving and checking cancellation information

[1482] The flight cancellation information obtained by the server is first stored in a database, and then matched with the reservation data of the relevant users to identify the affected users.

[1483] 3. Sending notifications

[1484] The server generates flight cancellation notifications for the relevant users, customizes the content of the notifications based on the user's emotional state, and sends them via communication methods (email, SMS, mobile app push notifications).

[1485] 4. Collection of seat availability and operation information

[1486] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology, and stores it in a database, ensuring that the latest information is always updated.

[1487] 5. Proposing alternative solutions

[1488] The collected data is analyzed using a generative AI model to create a list of alternative flights and other transportation options that best suit the user's desired conditions (destination, time, budget) and emotional state. A list of alternative flight options is generated and notified to the user.

[1489] 6. Confirmation of reservation

[1490] After the user selects an alternative means, the server will contact the corresponding operator's reservation system to confirm the new reservation, and if the reservation is successful, will notify the user of the details.

[1491] Terminal (user device)

[1492] 1. Receiving notifications

[1493] The user's device receives the cancellation notification sent from the server, which is displayed via email, SMS, or the mobile app's push notification function.

[1494] 2. Review and select alternatives

[1495] Once the notification is received, the user can view the alternatives proposed by the server via a mobile app or web portal, select the option they prefer, and then proceed to confirm the reservation.

[1496] User

[1497] 1. Check notifications

[1498] The user checks the notification displayed on the terminal and becomes aware that a flight cancellation has occurred.

[1499] 2. Choosing an alternative

[1500] Select your preferred method of transportation from multiple alternative flights and transportation options suggested by the server.

[1501] 3. Completing the reservation

[1502] Once the selected alternative flight has been booked, the user will receive a confirmation from the server, allowing them to secure their new transportation.

[1503] Emotion Engine

[1504] 1. Emotional awareness

[1505] The emotion engine analyzes the user's emotions based on their voice, text input, and device sensor information. The emotion engine evaluates their stress level and mood and sends the results back to the server.

[1506] 2. Customized Notifications

[1507] The server customizes the notification content based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification content may be changed to a more gentle tone and emphasize prompt action.

[1508] 3. Adjusting priorities

[1509] The server adjusts the content and priorities of alternative suggestions based on the stress level information obtained from the emotion engine. For users with high stress levels, it prioritizes suggestions for the nearest available flight and more comfortable transportation options.

[1510] Specific examples

[1511] For example, if User B has booked a flight from Tokyo to Osaka, the following scenario will unfold when the server receives information about a flight cancellation:

[1512] 1. The server obtains information via the airline's API that a flight from Tokyo to Osaka has been canceled.

[1513] 2. The server saves the cancellation information in the database and compares it with the corresponding reservation data of User B.

[1514] 3. The server retrieves User B's contact information from the database and sends a push notification to the mobile app. Because the emotion engine recognizes User B's high stress state, the notification content is expressed in a particularly gentle manner.

[1515] 4. The server collects information on available seats on other operators and uses the generative AI model to list the flights that best match User B's desired conditions and stress level. In this case, priority is given to the nearest flight and the most comfortable means of transportation.

[1516] 5. User B checks the list of suggested alternative flights through the mobile app and selects "Airline C's flight departing at 14:00."

[1517] 6. The server connects the new reservation to Airline C's reservation system and confirms the reservation. If the reservation is successful, it notifies User B of the corresponding reservation number and details.

[1518] This allows User B to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's call centers and counters.

[1519] The processing flow will be explained below.

[1520] Step 1:

[1521] The server periodically polls the airline's API endpoint to monitor flight status in real time. The polling interval is managed by a configured timer, and the server calls the API at regular intervals to obtain flight status information.

[1522] Step 2:

[1523] The server analyzes the acquired flight status data and checks whether it contains any cancellation information. If it does, it stores the details of the canceled flight (flight number, departure and arrival times, etc.) in the database.

[1524] Step 3:

[1525] The server compares the information about the canceled flights with the user reservation data in the database to identify the affected users, and retrieves the contact information (email addresses, phone numbers, etc.) of the affected users from the database.

[1526] Step 4:

[1527] The emotion engine evaluates the user's emotional state based on their past voice and text inputs and sensor information, and provides feedback on their stress level and mood to the server.

[1528] Step 5:

[1529] The server customizes the content of the cancellation notification based on feedback from the emotion engine. For example, if the user is in a high-stress state, the notification may include more gentle language and an emphasis on immediate action. The server then sends the notification as an email, SMS, or mobile app push notification.

[1530] Step 6:

[1531] The server collects information on available seats and operation from multiple operators using APIs and web scraping technology. The collected data is stored in a database and updated.

[1532] Step 7:

[1533] The server analyzes the collected data using a generative AI model, which considers the user's destination, time, budget, and emotional state to select the most suitable alternative flight or other transportation option, and generates a list of selected alternative flights.

[1534] Step 8:

[1535] The server then notifies the user of the list of alternative flights. The notification content is customized according to the user's emotional state. The notification includes details of the alternative flights (operating company name, departure and arrival times, fares, etc.).

[1536] Step 9:

[1537] The user uses a device (mobile app or web portal) to check the list of alternative flight options sent from the server, and selects the desired alternative flight.

[1538] Step 10:

[1539] The server receives the information about the alternative flight selected by the user and confirms the reservation by connecting with the reservation system of the relevant operating company. If the reservation is successful, it obtains the reservation number and detailed information and stores them in a database.

[1540] Step 11:

[1541] After the server confirms the successful reservation, it generates and sends a reservation confirmation notice to the user. The notice contains details of the new reservation (flight number, departure and arrival times, reservation number, etc.). For high-stress users, the notice includes a message emphasizing that the reservation has been completed.

[1542] Examples:

[1543] For example, if User C has booked a flight from Tokyo to Fukuoka, the following scenario will unfold when the server receives information about a flight cancellation.

[1544] 1. The server obtains information about the cancellation of a flight from Tokyo to Fukuoka through the airline's API.

[1545] 2. The server saves the cancellation information in the database and compares it with the reservation data of User C, the relevant user.

[1546] 3. The server retrieves user C's contact information from the database, and the emotion engine analyzes past communication and usage history.

[1547] 4. The emotion engine recognizes User C's high stress state and provides feedback to the server.

[1548] 5. Based on the feedback from the emotion engine, the server generates a notification that emphasizes calm expressions and prompt responses, and sends it as a push notification to the mobile app.

[1549] 6. The server collects seat availability information from other operators and uses a generative AI model to create a list of flights that best fit your desired conditions and stress level, prioritizing the earliest flights and most comfortable travel options.

[1550] 7. User C checks the list of suggested alternative flights through the mobile app and selects "Operator D's flight departing at 16:00."

[1551] 8. The server connects the new reservation to Operator D's reservation system and confirms the reservation. If the reservation is successful, it retrieves the corresponding reservation details and stores them in the database.

[1552] 9. After the server confirms the success of the reservation, it generates and sends a reservation confirmation notification to User C. For User C who is under high stress, the notification emphasizes that the reservation has been completed.

[1553] This allows User C to quickly and stress-free reserve a replacement flight, and the emotional support provided by the emotion engine reduces the mental burden. It also has the effect of reducing congestion at the operator's counters and call centers.

[1554] Example 2

[1555] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1556] When a service is canceled, it is difficult for passengers to find alternative means quickly and efficiently, which tends to increase the stress they feel. Furthermore, conventional systems are unable to provide services that take into account the emotional state of the passenger, so there is a need to reduce the psychological burden on passengers.

[1557] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1558] In this invention, the server includes means for monitoring flight status in real time from the operator's database or public API, means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the flight cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and flight information from multiple operators in real time, means for analyzing using a generative AI model and proposing an alternative means that best suits the user's desired conditions and emotional state, and means for confirming the new reservation selected by the user. This allows users to quickly and efficiently find alternative means and further reduces psychological burden by receiving appropriate responses according to their emotional state.

[1559] "Transportation agency" refers to an organization that provides passenger or freight transportation services, such as an airline, railroad, or bus company.

[1560] A "database" is a system that organizes and stores information in a particular way, allowing the information to be efficiently searched, managed, and updated.

[1561] "Public API" refers to an application programming interface that is publicly available for developers to use and is used to connect with other systems and applications.

[1562] "Cancellation information" refers to information indicating that a scheduled flight will not operate for some reason.

[1563] "User" refers to an individual or legal entity that uses the services of an operating company.

[1564] "Notification" refers to the act of transmitting specific information to a specific target, and includes email, SMS, app push notifications, etc.

[1565] "Available seat information" refers to information about available seats on an operating company, and indicates that they are available for reservation.

[1566] "Operation information" refers to general information regarding operations, such as flight schedules, operation status, delays, and cancellations.

[1567] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and generate output according to a purpose.

[1568] An "emotion engine" refers to a system that analyzes a user's emotional state in real time and uses the results to provide an appropriate response.

[1569] "New Booking" means a booking for a new means of transportation selected by a User for a flight or means of transportation that has become necessary due to a cancellation.

[1570] "Real-time" refers to data acquisition and processing occurring almost instantaneously.

[1571] "Matching" refers to the act of comparing different pieces of information to see if they match.

[1572] This invention is a system that helps users efficiently find alternative means of transportation when a flight is canceled, and also recognizes the user's emotions and provides appropriate responses. This system is mainly composed of four elements: a server, a terminal, a user, and an emotion engine. The roles and operations of each are described in detail below.

[1573] server

[1574] The server is the core of the system. First, it monitors flight status in real time using the public API of the operator. For example, it uses the Python requests library to send HTTP requests and parses the responses in JSON format. This cancellation information is stored in a PostgreSQL database and compared with user reservation data using SQL queries. Based on the results of the comparison, Twilio API and Firebase Cloud Messaging are used to send cancellation information notifications to affected users.

[1575] The server then polls the APIs of multiple operators to collect seat availability and flight information. The collected information is stored in a database, always kept up to date. This information is then analyzed using a generative AI model (e.g., OpenAI's GPT-3) to suggest optimal alternatives based on the user's desired conditions and emotional state. As a result, a list of alternative flight options is generated and notified to the user again.

[1576] Terminal (user device)

[1577] The device receives a notification sent from the server. Specifically, the notification is displayed via the email app, SMS app, or the mobile app's push notification function (e.g., APNs on iOS or FCM on Android). After receiving the notification, the user can view the proposed alternatives through the mobile app or web portal. Through a user interface built with React Native or Flutter, the user selects the desired option and completes the process to confirm the new reservation.

[1578] User

[1579] The user confirms the notification displayed on the terminal and becomes aware that a flight cancellation has occurred. After receiving the notification, the user selects the desired alternative flight or transportation method from the multiple alternative flights and transportation methods proposed by the server. The server then confirms the new reservation and receives details (such as a reservation number) if successful.

[1580] Emotion Engine

[1581] The emotion engine analyzes the user's emotions in real time based on the user's voice, text input, and device sensor information. This analysis uses natural language processing technology. The emotion engine feeds back the analysis results to the server, which then customizes the notification content based on the results. For example, if the server recognizes that the user is in a state of high stress, it will change the notification content to be particularly gentle. In addition, when the server suggests alternative solutions, it also adjusts the priority taking the user's emotional state into account.

[1582] Specific examples

[1583] For example, if a user has booked a flight from Tokyo to Osaka, and the server receives information from an airline's API that the flight from Tokyo to Osaka has been cancelled, the following scenario will unfold:

[1584] 1. The server stores the acquired cancellation information in a database and compares it with the reservation data of the corresponding user.

[1585] 2. The server retrieves the user's contact information from the database and sends a push notification to the mobile app. The notification is delivered in a particularly gentle manner because the emotion engine recognizes the user's high stress state.

[1586] 3. The server collects information on available seats from other operators and uses a generative AI model to list the flights that best match the user's desired conditions and stress level. For example, the "earliest flight" or "comfortable transportation" is prioritized.

[1587] 4. The user reviews the list of suggested alternative flights through the mobile app and selects one.

[1588] 5. The server confirms the new reservation and notifies the user of the details.

[1589] Such a system allows users to find alternatives quickly and stress-free.

[1590] Additionally, the following example prompt sentence is used as input to the generative AI model: "Please suggest alternative flights that meet the following conditions: destination: Osaka, desired departure time: 14:00, budget: within 30,000 yen, user's emotional state: high stress."

[1591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1592] Step 1:

[1593] The server monitors the operation status in real time through the operator's public API. Specifically, it periodically sends HTTP requests using the Python requests library and receives responses in JSON format. The input data is operation status information obtained from the operator's API endpoint, and the output is cancellation information in JSON format. Once the cancellation information is obtained, the server proceeds to the next step.

[1594] Step 2:

[1595] The server stores the cancellation information in a PostgreSQL database. This operation uses the psycopg2 library. The input data is the cancellation information, and the output is the cancellation entry stored in the database. It then matches this with the user's reservation data using an SQL query to identify the corresponding user.

[1596] Step 3:

[1597] The server generates a notification of the flight cancellation information for the identified user. First, it retrieves the user's contact information from the database. Next, it generates a notification message and queries the emotion engine for the user's emotional state. The input data is the contact information and the flight cancellation information, and the output is a customized notification message. Specifically, it sends the notification using the Twilio API or Firebase Cloud Messaging.

[1598] Step 4:

[1599] The emotion engine analyzes the user's emotional state in real time. The input data is the user's voice, text input, and device sensor information, and the output is an evaluation of the user's emotional state. Based on this evaluation, the server customizes the notification content as needed.

[1600] Step 5:

[1601] The server uses the APIs of multiple operators to collect seat availability information and operation information. Web scraping technology may also be used. The input data is the response of the operator's various APIs, and the output is seat availability information and operation information stored in the database. This ensures that the latest information is always maintained.

[1602] Step 6:

[1603] The server uses a generative AI model (e.g., OpenAI's GPT-3) to analyze the collected seat availability information and flight information. The input data are seat availability information, flight information, the user's desired conditions, and a prompt sentence about the user's emotional state, and the output is a list of suggested alternatives. An example of a prompt sentence is, "Please suggest alternative flights that meet the following conditions: destination: destination, desired departure time: desired time, budget: budget, user's emotional state: emotional state."

[1604] Step 7:

[1605] The server notifies the user of the generated list of suggested alternatives. The input data is the list of suggested alternatives, and the output is a notification sent to the user's device. Specifically, the server sends the notification using the Twilio API or Firebase Cloud Messaging again.

[1606] Step 8:

[1607] The user checks the list of suggested alternatives on their device and selects the option they want. The input data is the notification from the server, and the output is the information about the alternative selected by the user.

[1608] Step 9:

[1609] The server communicates with the corresponding operator's reservation system to confirm a new reservation based on the alternative selected by the user. The input data is the alternative selected by the user, and the output is a confirmation of the reservation. If the reservation is successful, the server notifies the user of the details (such as the reservation number).

[1610] Step 10:

[1611] The user receives a notification confirming that the new reservation has been confirmed and receives further details (such as the reservation number). The input data are the reservation details from the server, and the output is the user's confirmation of the new reservation.

[1612] (Application example 2)

[1613] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1614] In modern society, delays in transportation and delivery services cause significant stress and difficulty for users. Furthermore, due to a lack of systems that can quickly obtain information about cancellations and delays and suggest appropriate alternatives, users are often forced to resolve issues on their own. Furthermore, the lack of response that takes into account the user's emotional state can lead to increased stress and dissatisfaction. The objective of this invention is to solve these problems and provide a system that provides a fast, user-friendly response when transportation or delivery services are delayed.

[1615] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring operation status in real time from the database and public API of transportation operators, means for saving the acquired cancellation information in a database and comparing it with the reservation data of the corresponding user, means for sending a notification of the cancellation information to the corresponding user, means for collecting and analyzing vacant seat information and operation information from multiple transportation operators in real time, means for notifying the user of the generated alternative means proposal, means for confirming the new reservation selected by the user, means for collecting delivery delay information and recognizing the user's emotional state to customize the notification content, and means for suggesting alternative means when delivery is delayed and confirming a new order. This enables a quick and user-friendly response when transportation or delivery service delays occur.

[1616] "Transportation" refers to all modes of transportation, including public transportation and private transportation services.

[1617] "Cancellation Information" means information indicating that a scheduled service of an operator has been cancelled.

[1618] "Emotion engine" refers to a software or hardware component for recognizing and analyzing a user's emotional state.

[1619] "Alternative Means" means the alternative means of travel or service offered to a Passenger in the event of a cancellation or delay.

[1620] "Real-time" means that data is processed as soon as it is acquired, with results available almost immediately.

[1621] "API" stands for Application Programming Interface, and refers to an interface that enables communication between different software applications.

[1622] "Web scraping" refers to the automated process of extracting information from websites.

[1623] A "generative AI model" refers to a model that includes algorithms that use artificial intelligence techniques to analyze data and generate new data or suggestions.

[1624] "Prompt" means an instruction or question input to a generative AI model, and is text used to guide the model to an appropriate output.

[1625] "Notification" means an informational message sent by the System to a User, which may take the form of email, SMS, mobile app push notification, etc.

[1626] "New Order" means a replacement order selected by a User when an existing order is cancelled or delayed.

[1627] "Database" means an electronic system for efficiently storing, retrieving, and managing structured data.

[1628] "User" refers to any individual or organization that uses this system or service.

[1629] "Stress state" indicates the user's mental or emotional state of tension and is analyzed by the emotion engine.

[1630] System configuration

[1631] The embodiment of the present invention consists of three elements: a server, a terminal, and a user. The system provides a fast and user-friendly response when there is a delay in the food delivery service through the cooperative operation of these elements.

[1632] server

[1633] The server has the following main functions:

[1634] 1. Monitoring delay information:

[1635] The server periodically calls the delivery company's API endpoint to monitor the delivery status in real time. The information obtained from this API endpoint is saved in the following format, for example.

[1636] 2. Storage and collation of delayed information:

[1637] The server stores the acquired delay information in a database and compares it with the order data of the corresponding user. At this time, the server uses a database management system (e.g., MySQL or PostgreSQL).

[1638] 3. Sending notifications:

[1639] If a delay is confirmed, the server uses an emotion engine to customize the notification content, taking into account the user's emotional state. For example, if the user is in a high-stress state, the notification content may be changed to a more calming tone.

[1640] 4. Proposing alternatives using generative AI models:

[1641] The server uses the generative AI model to generate the best alternative based on the user's desired conditions and emotional state. As input to the generative AI model, the prompt sentence is set as follows:

[1642] The user's requirements are as follows:

[1643] Chicken dishes

[1644] Delivery time within 30 minutes

[1645] The price is less than 1,500 yen

[1646] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1647] Based on this criteria, suggest the best alternative restaurant and menu.

[1648] 5. Confirming a new order:

[1649] After the user selects an alternative, the server will contact the corresponding delivery system to place a new order, and once the order is placed, the server will notify the user of the order details.

[1650] Terminal (user device)

[1651] The user's device has the following capabilities:

[1652] 1. Receiving notifications:

[1653] Receive delay notifications sent by the server, which can appear as email, SMS, or mobile app push notifications.

[1654] 2. Review and select alternatives:

[1655] Through a mobile app or web portal, users can review the alternatives suggested by the server and choose the option that best suits their needs.

[1656] User

[1657] The user performs the following actions:

[1658] 1. Check notifications:

[1659] The user checks the delay notification displayed on the terminal.

[1660] 2. Alternative Selection:

[1661] From the multiple alternatives proposed by the server, you can choose the one that best suits your requirements.

[1662] 3. Completing a new order:

[1663] Confirm the selected alternative and receive the new order details from the server.

[1664] Specific examples

[1665] For example, if a user orders a chicken dish from a particular food delivery service, the following scenario unfolds when the server detects that the delivery will be delayed:

[1666] 1. The server learns through the delivery company's API that the user's order is 30 minutes late.

[1667] 2. Store the delay information in a database and match it with the order data of the corresponding user.

[1668] 3. Analyze the stress state using an emotion engine based on the user's past feedback.

[1669] 4. If the user is under high stress, notify them of delays in a gentler manner.

[1670] 5. The server uses a generative AI model to suggest alternatives that fit the user's preferences:

[1671] The user's requirements are as follows:

[1672] Chicken dishes

[1673] Delivery time within 30 minutes

[1674] The price is less than 1,500 yen

[1675] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1676] Based on this criteria, suggest the best alternative restaurant and menu.

[1677] 6. The user reviews the proposed alternatives through the mobile app and selects the preferred option.

[1678] 7. The server confirms the new order and notifies the user of the details.

[1679] This allows users to secure alternative means quickly and stress-free, minimizing delivery disruptions.

[1680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1681] Step 1: Monitor latency information

[1682] The server periodically calls the delivery company's API endpoint to monitor delivery status in real time. The input is the API endpoint URL, and the output is the retrieved delivery status information. The server stores this information in a database.

[1683] Step 2: Storing and verifying delay information

[1684] The server saves the acquired delivery status information in a database and compares it with the corresponding user's order data. The input is the delivery status information and the user's order data, and the output is a list of delayed orders. The server identifies delayed orders based on the comparison results.

[1685] Step 3: Sending notifications

[1686] The server sends notifications of delays to the appropriate users. The input is a list of delayed orders and user contact information, and the output is a notification message to the user. Notifications can be sent via email, SMS, or mobile app push notifications.

[1687] Step 4: Analyze emotional state

[1688] The server uses an emotion engine to analyze the user's emotional state. The input is the user's past feedback and sensor information, and the output is the analyzed emotional state (e.g., high stress). The notification content is customized based on this information.

[1689] Step 5: Propose alternatives

[1690] The server uses a generative AI model to suggest optimal alternatives based on the user's desired conditions and emotional state. The input is the user's desired conditions, emotional state, and prompt, and the output is a generated list of alternatives. An example prompt is as follows:

[1691] The user's requirements are as follows:

[1692] Chicken dishes

[1693] Delivery time within 30 minutes

[1694] The price is less than 1,500 yen

[1695] The user's emotional state is "high stress" and their current order is delayed by 30 minutes.

[1696] Based on this criteria, suggest the best alternative restaurant and menu.

[1697] Step 6: Review and select alternatives

[1698] The device receives the notification and the user checks the list of alternatives sent by the server. The input is the list of alternatives sent by the server and the output is the selected alternative. The user selects the desired option through the mobile app or web portal.

[1699] Step 7: Confirm your new order

[1700] The server places a new order based on the selected alternative. The inputs are the selected alternative and the delivery system's API, and the output is a new order confirmation message. The server notifies the user with the details of the new order.

[1701] These steps enable the system to provide a fast, user-friendly response when food delivery services are delayed, allowing users to secure alternatives without stress.

[1702] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1703] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1704] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1705] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. S...

Claims

1. A means to monitor operation status in real time from the operator's database and public API, A means for storing the acquired flight cancellation information in a database and comparing it with the reservation data of the corresponding user; a means for sending a notification of flight cancellation information to the relevant user; A means of collecting and analyzing seat availability information and operation information from multiple operating agencies in real time, a means for notifying the user of the generated alternative suggestions; a means for confirming the new booking selected by the User; A system including:

2. The system according to claim 1, characterized in that API and web scraping technology are used to collect operation information of transportation companies.

3. The system according to claim 1, characterized in that email, SMS, and mobile app push notifications are used as means of contacting users.

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