system

Abstract

We provide the system. [Solution] A means for recording passenger profile information and selection history in a database, A means of using an AI algorithm that recommends the optimal seat based on passenger information obtained from the aforementioned database, A means of sending a reservation confirmation notification to the passenger's device, A means of updating and displaying real-time seat availability information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In public transportation, it is necessary to solve the problems that passengers cannot easily find an optimal seat according to their own needs, and it is difficult to secure an appropriate seat during congestion. In addition, the means for passengers to obtain real-time information are limited, and confusion occurs when choosing a seat, which is an issue.

Means for Solving the Problems

[0005] <000003> This invention provides a system that includes means for recording passenger profile information and selection history in a database, and means for recommending the optimal seat using an AI algorithm based on this information. Furthermore, by including means for sending a reservation completion notification to the passenger's terminal and updating and displaying real-time seat availability information, the system facilitates obtaining seats that meet passenger needs and ensures that appropriate seats are available even during peak hours. In addition, by analyzing passenger movement patterns and providing congestion mitigation measures that consider seat occupancy rates, passengers can experience a smoother journey.

[0006] "Passenger" refers to people who use public transportation, and services are provided to them based on their individual travel needs and behavioral patterns.

[0007] "Profile information" refers to information including individual characteristics such as passenger name, age, and travel history, as well as past usage patterns. This information is used by the system to provide personalized services.

[0008] "Selection history" refers to records of seats and routes previously selected by passengers, and this data is used to analyze passenger preferences and patterns.

[0009] A "database" refers to an internal storage mechanism within a system used to record and manage passenger profile information and selection history.

[0010] An "AI algorithm" refers to a calculation procedure or model that analyzes a passenger's past behavior patterns and profile information, and then recommends the most suitable seat based on that analysis.

[0011] A "terminal" refers to a digital device used by passengers to select seats or check reservation information, and primarily includes smartphones and dedicated applications.

[0012] "Real-time information" refers to the latest information regarding seat availability and vehicle congestion, which is instantly accessible to passengers and constantly updated.

[0013] "Seat occupancy rate" refers to the percentage of seats used by passengers within a particular public transportation vehicle, and is used as an indicator to evaluate the degree of congestion. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

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

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

[0017] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a seat management system for public transportation that improves passenger convenience. This system uses passenger profile information and seat selection history to recommend seats optimized for the individual needs of each passenger. The embodiments thereof are described in detail below.

[0036] The server records passenger profile information and selection history in a database. This allows for the accumulation of individual passenger preferences and travel patterns. Based on this data, the server utilizes AI algorithms to dynamically recommend seats suitable for each passenger. The AI ​​analyzes diverse passenger data to help passengers choose the most comfortable and efficient seats.

[0037] Users can use their own devices to check for available seats and select a seat. After selection, the device sends a reservation request to the server. The server determines whether the seat is available based on the received request and notifies the user's device of the result. This notification allows the user to immediately know whether their reservation has been confirmed.

[0038] Furthermore, the server updates seat availability information in real time, instantly reflecting the status of available seats on public transport vehicles. Based on this updated information, terminals display available seat information to passengers via apps and in-vehicle displays. This allows users to quickly find currently available seats and travel with peace of mind.

[0039] As a concrete example, when this system is used on a crowded line during the morning commute, users can instantly check the current seat availability on the train via the app and receive seat recommendations from AI. Users can then select and reserve a seat through the app and receive a confirmation notification on their smartphone, allowing them to begin their journey without stress.

[0040] As described above, the seat management system of the present invention provides passengers with a highly personalized seat selection service and improves the experience of using public transportation.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server stores passenger profile information and selection history in a database. This records each passenger's individual preferences and past usage patterns, ready to be used for future seat recommendations.

[0044] Step 2:

[0045] Users access the application using devices such as smartphones and tablets to check seat information. The app displays a list of currently available seats and provides them to the user in an easy-to-read format.

[0046] Step 3:

[0047] When a user selects a seat on the app, the server uses an AI algorithm to recommend the optimal seat based on their individual preference patterns. The recommendation results are displayed on the user's device in real time.

[0048] Step 4:

[0049] Users select their preferred seat through the app, taking into account seat recommendations from the server. After completing their selection, they confirm the reservation request on the app and begin the reservation process.

[0050] Step 5:

[0051] The terminal sends the user's seat selection and reservation request to the server. During this process, the terminal implements security measures to protect user information.

[0052] Step 6:

[0053] The server processes the received seat reservation request and checks if the seat is available. If the seat is available, it updates the reservation information in the database and simultaneously sends a reservation confirmation notification to the user's device.

[0054] Step 7:

[0055] The device receives a reservation confirmation notification from the server and displays the notification to the user. The user can confirm that the reservation is complete on the app screen and prepare for boarding.

[0056] Step 8:

[0057] The server retrieves real-time information on available seats in the vehicles and updates the database with the latest status. This ensures that the seat information shared with all users is always up-to-date.

[0058] Step 9:

[0059] The terminal provides passengers with the latest seat availability information through an app and in-vehicle displays. This helps users more easily select a suitable seat when boarding.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Providing passengers with the most suitable seats quickly on public transportation has been challenging. Conventional systems struggled to select seats based on individual passenger needs and preferences, and insufficient real-time updates of seat information prevented significant improvements in passenger convenience and satisfaction. A new system is needed to address these issues.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for recording passengers' personal information and choice history in a data storage unit, means for using a generative AI model that recommends appropriate seats based on passenger information obtained from the data storage unit, and means for sending a reservation confirmation message to the passenger's information terminal. This enables real-time seat recommendations tailored to the individual needs of passengers, allowing passengers to quickly secure the optimal seat and significantly improving the public transport user experience.

[0065] "Passenger personal information" refers to basic attribute data about passengers, including information about their name and seating preferences.

[0066] "Selection history" refers to records of passengers' past seat selections and usage patterns.

[0067] A "data storage unit" is a storage device, such as a database, for securely storing passengers' personal information and their choice history.

[0068] A "generative AI model" is an artificial intelligence algorithm that analyzes data to recommend the best seat for each passenger.

[0069] An "information terminal" is a device that passengers can use to receive messages such as reservation confirmations.

[0070] "Seat availability" refers to information indicating the current status of available seats on public transportation.

[0071] An "information display device" is a device such as a display or screen that provides passengers with real-time information, such as seat availability.

[0072] This invention provides a seat management system for public transport that enables personalized seat recommendations for passengers. The system has the following configuration:

[0073] Data processing by the server:

[0074] The server records passengers' personal information and seat selection history in a data storage unit. This allows the server to understand passenger preferences and travel patterns, creating a foundation for efficient analysis. The server feeds this data into a generative AI model to recommend the most suitable seat for each passenger. The generative AI model learns optimal seating patterns based on past data and dynamically optimizes new seat selections.

[0075] User actions:

[0076] Users operate the application using information terminals such as smartphones to check seat availability in real time. Through the app, users can view seat recommendations provided by the server, select the seat that best suits them, and make a reservation. Once the reservation is complete, a confirmation message is displayed on the device. This allows users to secure a seat smoothly.

[0077] Real-time updates:

[0078] The server constantly monitors seat availability on public transport and displays the information in real time. Through this updated information sent to terminals, users can travel while being aware of the latest seat availability.

[0079] Specific usage scenarios:

[0080] For example, during the morning rush hour, if a user uses the app saying, "I'm on the 6 AM train to work. I want to open the app to check the current seat availability and find the best seat for me," the AI ​​model will recommend the most suitable seat. In this way, users can experience highly convenient travel in real time.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server records passengers' personal information and seat selection history in a data storage unit.

[0084] Input: Personal information and past seat selection history provided by the user.

[0085] Data processing: Organize this information and convert it into a format that can be stored in a database.

[0086] Output: The organized passenger data is stored in the data storage unit.

[0087] Step 2:

[0088] The server inputs the aforementioned data into a generating AI model and recommends a suitable seat.

[0089] Input: Passenger personal information and seat selection history stored in the data storage unit.

[0090] Data processing: Generative AI models identify patterns and relationships in data and perform analysis for seat recommendations.

[0091] Output: Recommended seat information is generated.

[0092] Step 3:

[0093] Users use their devices to view and select recommended seats within the app.

[0094] Input: Seat recommendation information received from the server.

[0095] Specific operation: The user visually checks the recommended seats through a GUI on the terminal and makes a selection using their finger or mouse.

[0096] Output: Reservation request for the seat selected by the user.

[0097] Step 4:

[0098] The server processes reservation requests from users and determines the availability of seats.

[0099] Input: Seat reservation request based on user selection.

[0100] Data processing: Check the database for seat availability. If a reservation is possible, update the database with the reservation.

[0101] Output: Result of whether it is available or unavailable for reservation.

[0102] Step 5:

[0103] The device notifies the user of the reservation result.

[0104] Input: Reservation result notification from the server.

[0105] Specific operation: The device will use push notifications or in-app messaging to display the reservation status to the user.

[0106] Output: Reservation confirmation message sent to the user.

[0107] Step 6:

[0108] The server updates seat availability in real time and reflects it on the information display.

[0109] Input: All reservation and cancellation information.

[0110] Data processing: Re-evaluate the current seating situation and update the availability status to reflect the latest information.

[0111] Output: The updated seat availability information is reflected on the information display device and terminal.

[0112] (Application Example 1)

[0113] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0114] In recent years, with the proliferation of autonomous vehicles, the importance of seat management systems for providing an efficient and comfortable travel experience has increased. However, existing systems often result in congestion and inconvenience due to insufficient real-time updates of seat information and inadequate seat recommendations optimized for individual passenger needs. To solve this problem, a system is needed that can accurately grasp passenger profile information and travel trends and update seat information in real time.

[0115] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0116] In this invention, the server includes means for recording passenger attribute information and selection history in an information storage device, means for using an artificial intelligence algorithm that recommends an optimized seat based on the passenger information obtained from the information storage device, and means for transmitting reservation completion information to the passenger's information terminal. This enables passengers to have a comfortable and efficient travel experience in an autonomous vehicle.

[0117] "Passenger attribute information" refers to information about each passenger's individual characteristics, preferences, and history, and is data used to make optimal suggestions when selecting seats.

[0118] An "information storage device" is a system or device that stores digital information and allows it to be retrieved as needed.

[0119] An "optimized seat" is a seat selected to maximize passenger comfort and travel efficiency, taking into account the passenger's attribute information and history.

[0120] An "artificial intelligence algorithm" is a computational method that mimics the ability of computers to analyze large amounts of data and think and make decisions like humans.

[0121] An "information terminal" is an electronic device used by passengers to input their own information or to receive information from a system.

[0122] The seat management system for realizing this application operates using a combination of the following hardware and software: Hardware includes a server, passenger information terminals (smartphones and tablets), and displays within the autonomous vehicle. Software includes a database management system, a program for executing artificial intelligence algorithms, and network software for real-time communication.

[0123] The server is responsible for recording passenger attribute information and seat selection history in an information storage device. This allows for the accumulation of individual passenger travel history and preference information. Based on the accumulated data, the server activates an artificial intelligence algorithm to recommend optimized seats, identifying seats that enhance passenger comfort and seating efficiency. After the reservation is complete, the server sends this information to the passenger's information terminal to inform them that a seat has been secured.

[0124] The terminal displays real-time seat availability information to passengers and allows them to make reservations using their smartphones or other information devices. The terminal receives reservation completion notifications from the server and immediately notifies the user. This allows passengers to quickly and efficiently select seats based on the latest seat availability.

[0125] As a concrete example, when using this system during a family trip, it becomes easier for all family members to choose seats close to each other, allowing them to enjoy a comfortable journey from the very beginning. An example of a prompt sentence for the generative AI model related to this technology is shown below.

[0126] Example of a prompt:

[0127] Please propose effective methods for designing and implementing a system that reflects real-time seat availability and recommends personalized seats based on passenger attribute information.

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The server receives attribute information and seat selection history as input from passenger information terminals. This information is recorded in a database management system, and the data is processed to create a profile for each passenger. This profile serves as the basis for recommending the most suitable seat.

[0131] Step 2:

[0132] The server inputs passenger profile information stored in the database into an artificial intelligence algorithm. The AI ​​algorithm recommends seats that maximize passenger comfort and seating efficiency. This calculation takes into account past seat selection patterns and attribute information.

[0133] Step 3:

[0134] The server outputs seat information recommended by the AI ​​algorithm to the passenger's information terminal. This allows the terminal to display seat options optimized for the user. The user then selects a seat on the terminal based on this information.

[0135] Step 4:

[0136] The user selects their desired seat using an information terminal and sends a reservation request to the server. This reservation information is processed by the server, which then determines whether the reservation is valid or not.

[0137] Step 5:

[0138] The server determines whether the reservation is complete and sends the result as output to the user's information terminal. This notification is immediate, allowing the user to receive reservation confirmation and prepare to use their seat with peace of mind.

[0139] Step 6:

[0140] The terminal receives real-time seat availability information from the server and displays the current availability to the user. This allows the user to update their seat selection as needed based on the latest information.

[0141] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0142] This invention relates to a system that recognizes the emotional state of passengers and manages seating arrangements on public transportation accordingly. By incorporating an emotion engine, the system aims to provide optimal seating based on passengers' real-time emotions. An embodiment of this system is described in detail below.

[0143] The server first stores passenger profile information and past selection history in a database. Based on this information, the server prepares to provide passengers with the most suitable seats using an AI algorithm. At the same time, the server recognizes passengers' current emotions in real time via an emotion engine and uses that data to recommend seats.

[0144] Users can use their smartphones or tablets to view seating information tailored to their mood through the application. The app displays analysis results from an emotion engine and suggests the most comfortable and stress-free seat for the user. This seating selection is based on factors such as recommending a quiet zone if the user wants to spend time in peace, or a more comfortable seat if they want to relax.

[0145] After making their selection, the user sends a reservation request to the server through the app. Based on this request, the server determines whether a seat is available and reserves the seat best suited to the user's emotional state. The server sends a reservation confirmation to the user's device and records the reservation information in its database.

[0146] Furthermore, the server checks seat availability in real time and updates the database with seat usage information, including passenger sentiment. This allows both sentiment data and seat occupancy data to be considered, contributing to congestion reduction measures that ensure a comfortable experience for all passengers.

[0147] As a concrete example, consider the case where this system is used during rush hour when buses are nearing full capacity. When a user selects a seat using the app, the emotion engine senses the passenger's stress and fatigue and suggests the most suitable seat for that state. The user can then reserve the suggested seat and confirm it in the app, allowing them to board with peace of mind.

[0148] As described above, according to the embodiments of the present invention, users of public transportation can enjoy a comfortable journey that takes into account their mood at any given time.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The server stores passenger profile information and selection history in a database. This information is used as foundational data for future seat recommendations and personalized service.

[0152] Step 2:

[0153] The device activates the emotion engine when the user logs into the application. It uses cameras and sensors to collect the user's facial expressions and biometric information, and analyzes their current emotional state in real time.

[0154] Step 3:

[0155] The server receives the analyzed sentiment data and compares it with profile information and past selection history in the database. Based on this information, the AI ​​algorithm recommends the seat that best suits the user's current emotional state.

[0156] Step 4:

[0157] The device displays seat recommendation information sent from the server to the user. The user can then use this recommendation to select their preferred seat within the app.

[0158] Step 5:

[0159] The user uses the app to send a seat reservation request to the server to confirm their reservation for the selected seat.

[0160] Step 6:

[0161] The server checks seat availability based on the received reservation request. If seats are available, it updates the reservation information in the database and sends a reservation confirmation notification to the user's device.

[0162] Step 7:

[0163] The terminal receives a reservation confirmation notification from the server and displays the reservation completion information to the user. This allows the user to confirm that their seat has been secured.

[0164] Step 8:

[0165] The server updates real-time seat availability information on buses and trains, and also records seat usage status based on sentiment analysis in its database.

[0166] Step 9:

[0167] The terminal provides users with real-time updated seat information via an app or in-car display. This allows users to enjoy a comfortable journey.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] In conventional public transportation, providing optimal seating based on passengers' emotions and individual needs has been difficult, leading to reduced comfort and increased stress during travel. In particular, the lack of consideration for seat selection based on passengers' emotional states can lead to decreased satisfaction and increased stress for users. Therefore, the present invention aims to improve the comfort of public transportation by accurately recognizing passengers' emotional states and providing seat recommendations based on those states.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for recording passenger attribute data and historical data in a data management device, means for using an artificial intelligence processing device that recommends the optimal location based on passenger information acquired from the data management device, and means for acquiring passenger emotional information in real time using an emotional analysis engine. This makes it possible to select the optimal seat according to the passenger's emotional state and provide it in real time, thereby significantly improving the comfort of users in public transportation.

[0173] "Passenger attribute data" refers to information that includes personal characteristics of passengers, such as name, travel history, and seating preferences.

[0174] "Historical data" refers to information that includes records of past actions and choices, particularly data representing previous seat selection and reservation history.

[0175] A "data management device" is a device that records, stores, and manages information, and specifically takes the form of a database system.

[0176] An "artificial intelligence processing device" refers to a combination of software and hardware used to analyze data and make decisions and predictions according to specific purposes, and includes AI algorithms.

[0177] The "emotion analysis engine" is a system that identifies passengers' emotional states from various input data and outputs them as numerical values ​​or categories, utilizing a machine learning model.

[0178] "Location" refers to a specific seat or space within public transport, a place that is physically occupied or usable by passengers.

[0179] In an embodiment of this invention, a system is realized that provides seating tailored to the feelings of passengers within public transportation. The system consists of three main elements: a server, a terminal, and a user.

[0180] Server role:

[0181] The server first records passenger attribute and history data in a data management device. This data management device is typically implemented as an SQL database system (e.g., MySQL®). Next, the server uses an artificial intelligence (AI) processing unit to recommend the optimal seat based on the passenger information. This process utilizes AI algorithms, and platforms such as Tensorflow® and PyTorch are available. Furthermore, a sentiment analysis engine is used to analyze voice and text data provided by the passenger's device (e.g., prompts such as "Please tell us your current emotional state") to obtain emotional information in real time. This information is used by the AI ​​processing unit to recommend the optimal seat. After the reservation is complete, the server records this information in the data management device and updates the seat availability in real time.

[0182] Terminal role:

[0183] Passengers launch a dedicated application on their mobile devices, such as smartphones or tablets. The application interacts with a server and displays results from an emotion analysis engine. This allows the user to be offered seats that match their emotional state. Once a selection is made, the device sends a reservation request to the server and receives and displays a reservation confirmation notification.

[0184] User roles:

[0185] Users typically access the application using a smartphone or tablet. When passengers respond to prompts, the application provides real-time information on the best seating options. Based on this information, users can choose a seat and use public transport with peace of mind. This implementation allows users to enhance comfort and reduce stress during their journey.

[0186] This system is designed to be used especially during crowded times such as rush hour, and is capable of further improving the passenger travel experience.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The server receives passenger attribute and history data transmitted from terminals and records it in the data management device. Inputs include the passenger's name, previous booking history, and seat preferences. Based on the recorded data, a foundation is created for providing personalized seating suggestions to individual passengers. This data is stored in an SQL database.

[0190] Step 2:

[0191] The server uses an emotion analysis engine to analyze voice and text data transmitted from the terminal. The data analyzed is input by the user in response to the prompt, "Please tell me your current emotional state." Through this process, the server outputs the current emotional state as numerical or categorical data, which is used as input data in the next processing step.

[0192] Step 3:

[0193] The server uses an artificial intelligence processing unit to calculate the optimal seat based on collected passenger profile data and emotional data. The AI ​​algorithm selects a seat location appropriate to the passenger's emotional state and past behavior. It generates output tailored to individual needs, such as suggesting a seat in a quiet zone if the passenger's emotions indicate a desire for relaxation.

[0194] Step 4:

[0195] Users review seat suggestions provided by the server using a dedicated application on their terminal. The application displays sentiment analysis and optimization results, offering passengers choices. Users select their preferred seat from the presented options, and their selection is sent from the terminal to the server.

[0196] Step 5:

[0197] The server receives reservation requests from users and checks seat availability in real time. If an available seat is found, the server reserves the seat and notifies the terminal that the reservation is complete. The server records the reservation completion information in a data management device, accumulating data to improve the accuracy of future recommendations.

[0198] Step 6:

[0199] The server periodically evaluates the overall seat occupancy rate and updates the database, taking into account available seats and the emotional state of each passenger. This real-time update improves the overall efficiency of seat allocation and contributes to a comfortable travel experience for passengers.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0202] Modern public transportation systems struggle to provide services based on passengers' emotional states, resulting in passengers being unable to find comfortable seats that suit their mood. Furthermore, the inability to manage seating based on emotions has led to insufficient stress reduction measures during peak hours. Therefore, there is a need for the development of seating management systems that take passenger emotions into consideration.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes a recording device that stores passenger profile information and selection history, a device that uses a machine learning algorithm to recommend an appropriate seat based on passenger data retrieved from the recording device, and an emotion analysis device that recognizes the passenger's emotional state and reflects it in seat recommendations. This makes it possible to provide appropriate seats that take passenger emotions into consideration.

[0205] A "recording device" is a data storage device for saving passenger profile information and selection history.

[0206] A "device using machine learning algorithms" is a computing device that utilizes machine learning to recommend appropriate seats based on passenger data.

[0207] An "information terminal" is a communication device used by passengers that has the function of receiving reservation notifications.

[0208] A "real-time update device" is a system that updates seat availability information in real time, providing the most up-to-date information.

[0209] A "sentiment analysis device" is a data processing device that recognizes and analyzes the emotional state of passengers and reflects the results in seat management.

[0210] A "seat arrangement suggestion device" is a system that suggests the optimal seat arrangement based on the emotional state of the passengers.

[0211] The system for realizing this application consists of a device that recognizes the passenger's emotional state and suggests the optimal seat based on that state. The server stores the passenger's profile information and past seat selection history in a recording device. Next, when the passenger accesses the system via an information terminal such as a smartphone or tablet, the server uses a device employing machine learning algorithms to perform calculations to select the optimal seat.

[0212] The server uses an emotion analysis device to identify passengers' real-time emotional states and incorporates the results into the seat recommendation process. This makes it possible to suggest comfortable seats tailored to each passenger's emotional state. For example, if a passenger is seeking quiet, the server can guide them to a seat where extraneous noises are less audible.

[0213] For terminals, the server uses a seat placement suggestion device to process the reservation of the suggested seat. A notification of reservation completion is sent to the terminal, and passengers can check this information in real time. For example, if a passenger is detected as fatigued, a seat with a reclining function can be suggested.

[0214] An example of a prompt using a generative AI model is, "When passengers are experiencing high stress levels, please suggest what seating characteristics would allow them to relax more," which provides guidance on specific seating characteristics. In this way, the server, terminal, and user elements work together to create a system that provides a comfortable riding experience.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The server stores passenger profile information and selection history in a recording device. Past passenger selection data is used as input for data storage operations. The output is a well-organized database usable for subsequent processing.

[0218] Step 2:

[0219] Users access the system using smartphones or tablets. In this process, the information terminal launches an application and sends a connection request to the server. The input is the user's connection request data, and the output is the establishment of a communication session with the server.

[0220] Step 3:

[0221] The server uses machine learning algorithms to select the optimal seat based on passenger data acquired from recording devices. Passenger profile information and emotional state are provided as input, and the server analyzes this data to output the most suitable seat candidates.

[0222] Step 4:

[0223] The emotion analysis device identifies the user's real-time emotional state. It takes the user's facial image and voice data as input, analyzes them, and outputs the emotional state. The server receives this emotional data.

[0224] Step 5:

[0225] The server uses data from the sentiment analysis device to perform a seat recommendation process and transmits the results to the terminal via the seat placement suggestion device. The inputs are sentiment analysis results and seat data, and the output provides the most suitable seat information.

[0226] Step 6:

[0227] The terminal displays seat suggestions sent from the server to the user. The user reviews them and makes a reservation selection as needed. The input is the seat suggestion data on the terminal, and the output is the user's seat reservation selection.

[0228] Step 7:

[0229] When a user reserves a seat, the server sends a reservation confirmation notification to the terminal. The server receives the user's reservation selection data as input, and a reservation confirmation notification is sent to the terminal as output.

[0230] Step 8:

[0231] The server updates seat availability and passenger sentiment in real time. This process involves inputting newly collected passenger sentiment data and seat usage information, using it to update the database, and then sharing the updated seat information across the entire system as output.

[0232] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0233] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0234] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0238] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0239] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0240] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0241] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0242] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0243] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0244] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0245] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0246] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0247] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0248] This invention relates to a seat management system for public transportation that improves passenger convenience. This system uses passenger profile information and seat selection history to recommend seats optimized for the individual needs of each passenger. The embodiments thereof are described in detail below.

[0249] The server records passenger profile information and selection history in a database. This allows for the accumulation of individual passenger preferences and travel patterns. Based on this data, the server utilizes AI algorithms to dynamically recommend seats suitable for each passenger. The AI ​​analyzes diverse passenger data to help passengers choose the most comfortable and efficient seats.

[0250] Users can use their own devices to check for available seats and select a seat. After selection, the device sends a reservation request to the server. The server determines whether the seat is available based on the received request and notifies the user's device of the result. This notification allows the user to immediately know whether their reservation has been confirmed.

[0251] Furthermore, the server updates seat availability information in real time, instantly reflecting the status of available seats on public transport vehicles. Based on this updated information, terminals display available seat information to passengers via apps and in-vehicle displays. This allows users to quickly find currently available seats and travel with peace of mind.

[0252] As a concrete example, when this system is used on a crowded line during the morning commute, users can instantly check the current seat availability on the train via the app and receive seat recommendations from AI. Users can then select and reserve a seat through the app and receive a confirmation notification on their smartphone, allowing them to begin their journey without stress.

[0253] As described above, the seat management system of the present invention provides passengers with a highly personalized seat selection service and improves the experience of using public transportation.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The server stores passenger profile information and selection history in a database. This records each passenger's individual preferences and past usage patterns, ready to be used for future seat recommendations.

[0257] Step 2:

[0258] Users access the application using devices such as smartphones and tablets to check seat information. The app displays a list of currently available seats and provides them to the user in an easy-to-read format.

[0259] Step 3:

[0260] When a user selects a seat on the app, the server uses an AI algorithm to recommend the optimal seat based on their individual preference patterns. The recommendation results are displayed on the user's device in real time.

[0261] Step 4:

[0262] Users select their preferred seat through the app, taking into account seat recommendations from the server. After completing their selection, they confirm the reservation request on the app and begin the reservation process.

[0263] Step 5:

[0264] The terminal sends the user's seat selection and reservation request to the server. During this process, the terminal implements security measures to protect user information.

[0265] Step 6:

[0266] The server processes the received seat reservation request and checks if the seat is available. If the seat is available, it updates the reservation information in the database and simultaneously sends a reservation confirmation notification to the user's device.

[0267] Step 7:

[0268] The device receives a reservation confirmation notification from the server and displays the notification to the user. The user can confirm that the reservation is complete on the app screen and prepare for boarding.

[0269] Step 8:

[0270] The server retrieves real-time information on available seats in the vehicles and updates the database with the latest status. This ensures that the seat information shared with all users is always up-to-date.

[0271] Step 9:

[0272] The terminal provides passengers with the latest seat availability information through an app and in-vehicle displays. This helps users more easily select a suitable seat when boarding.

[0273] (Example 1)

[0274] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] Providing passengers with the most suitable seats quickly on public transportation has been challenging. Conventional systems struggled to select seats based on individual passenger needs and preferences, and insufficient real-time updates of seat information prevented significant improvements in passenger convenience and satisfaction. A new system is needed to address these issues.

[0276] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0277] In this invention, the server includes means for recording passengers' personal information and choice history in a data storage unit, means for using a generative AI model that recommends appropriate seats based on passenger information obtained from the data storage unit, and means for sending a reservation confirmation message to the passenger's information terminal. This enables real-time seat recommendations tailored to the individual needs of passengers, allowing passengers to quickly secure the optimal seat and significantly improving the public transport user experience.

[0278] "Passenger's personal information" refers to basic attribute data related to passengers, including information such as names and seat preferences.

[0279] "Selection behavior history" refers to records related to seat selections and usage patterns made by passengers in the past.

[0280] "Data storage unit" refers to a storage device such as a database for safely storing passengers' personal information and selection behavior history.

[0281] "Generated AI model" refers to an artificial intelligence algorithm for analyzing data and recommending the most suitable seats for passengers.

[0282] "Information terminal" refers to a device that passengers can use and is used to receive messages such as reservation confirmations.

[0283] "Seat availability" refers to information indicating the current availability status of seats in public transportation.

[0284] "Information display device" refers to devices such as displays and screens for providing passengers with real-time information such as seat availability.

[0285] This invention provides a seat management system for public transportation that enables personalized seat recommendations for passengers. This system has the following configuration.

[0286] Data processing by the server:

[0287] The server records passengers' personal information and seat selection history in the data storage unit. Thereby, the server grasps passengers' preferences and movement patterns and constructs a basis for efficient analysis. The server inputs this data into the generated AI model and recommends the most suitable seats for passengers. The generated AI model learns the optimal seat pattern based on past data and dynamically optimizes new seat selections.

[0288] User actions:

[0289] Users operate the application using information terminals such as smartphones to check seat availability in real time. Through the app, users can view seat recommendations provided by the server, select the seat that best suits them, and make a reservation. Once the reservation is complete, a confirmation message is displayed on the device. This allows users to secure a seat smoothly.

[0290] Real-time updates:

[0291] The server constantly monitors seat availability on public transport and displays the information in real time. Through this updated information sent to terminals, users can travel while being aware of the latest seat availability.

[0292] Specific usage scenarios:

[0293] For example, during the morning rush hour, if a user uses the app saying, "I'm on the 6 AM train to work. I want to open the app to check the current seat availability and find the best seat for me," the AI ​​model will recommend the most suitable seat. In this way, users can experience highly convenient travel in real time.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The server records passengers' personal information and seat selection history in a data storage unit.

[0297] Input: Personal information and past seat selection history provided by the user.

[0298] Data processing: Organize this information and convert it into a format that can be stored in a database.

[0299] Output: The organized passenger data is saved in the data storage unit.

[0300] Step 2:

[0301] The server inputs the aforementioned data into the generation AI model and recommends appropriate seats.

[0302] Input: The personal information of passengers and seat selection history saved in the data storage unit.

[0303] Data calculation: The generation AI model finds patterns and correlations from the data and performs analysis for seat recommendations.

[0304] Output: The recommended seat information is generated.

[0305] Step 3:

[0306] The user uses the terminal to check and select the seat recommendation information within the app.

[0307] Input: The seat recommendation information received from the server.

[0308] Specific operation: The user visually checks the recommended seats through the GUI on the terminal and makes a selection by operating a finger or mouse.

[0309] Output: A reservation request for the seat selected by the user.

[0310] Step 4:

[0311] The server processes the reservation request from the user and determines the availability of the seat.

[0312] Input: A seat reservation request based on the user's selection.

[0313] Data calculation: Check the database to confirm the seat availability. If the reservation is possible, reflect the reservation in the database.

[0314] Output: Result of whether it is available or unavailable for reservation.

[0315] Step 5:

[0316] The device notifies the user of the reservation result.

[0317] Input: Reservation result notification from the server.

[0318] Specific operation: The device will use push notifications or in-app messaging to display the reservation status to the user.

[0319] Output: Reservation confirmation message sent to the user.

[0320] Step 6:

[0321] The server updates seat availability in real time and reflects it on the information display.

[0322] Input: All reservation and cancellation information.

[0323] Data processing: Re-evaluate the current seating situation and update the availability status to reflect the latest information.

[0324] Output: The updated seat availability information is reflected on the information display device and terminal.

[0325] (Application Example 1)

[0326] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] In recent years, with the proliferation of autonomous vehicles, the importance of seat management systems for providing an efficient and comfortable travel experience has increased. However, existing systems often result in congestion and inconvenience due to insufficient real-time updates of seat information and inadequate seat recommendations optimized for individual passenger needs. To solve this problem, a system is needed that can accurately grasp passenger profile information and travel trends and update seat information in real time.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0329] In this invention, the server includes means for recording passenger attribute information and selection history in an information storage device, means for using an artificial intelligence algorithm that recommends an optimized seat based on the passenger information obtained from the information storage device, and means for transmitting reservation completion information to the passenger's information terminal. This enables passengers to have a comfortable and efficient travel experience in an autonomous vehicle.

[0330] "Passenger attribute information" refers to information about each passenger's individual characteristics, preferences, and history, and is data used to make optimal suggestions when selecting seats.

[0331] An "information storage device" is a system or device that stores digital information and allows it to be retrieved as needed.

[0332] An "optimized seat" is a seat selected to maximize passenger comfort and travel efficiency, taking into account the passenger's attribute information and history.

[0333] An "artificial intelligence algorithm" is a computational method that mimics the ability of computers to analyze large amounts of data and think and make decisions like humans.

[0334] An "information terminal" is an electronic device used by passengers to input their own information or to receive information from a system.

[0335] The seat management system for realizing this application operates using a combination of the following hardware and software: Hardware includes a server, passenger information terminals (smartphones and tablets), and displays within the autonomous vehicle. Software includes a database management system, a program for executing artificial intelligence algorithms, and network software for real-time communication.

[0336] The server is responsible for recording passenger attribute information and seat selection history in an information storage device. This allows for the accumulation of individual passenger travel history and preference information. Based on the accumulated data, the server activates an artificial intelligence algorithm to recommend optimized seats, identifying seats that enhance passenger comfort and seating efficiency. After the reservation is complete, the server sends this information to the passenger's information terminal to inform them that a seat has been secured.

[0337] The terminal displays real-time seat availability information to passengers and allows them to make reservations using their smartphones or other information devices. The terminal receives reservation completion notifications from the server and immediately notifies the user. This allows passengers to quickly and efficiently select seats based on the latest seat availability.

[0338] As a concrete example, when using this system during a family trip, it becomes easier for all family members to choose seats close to each other, allowing them to enjoy a comfortable journey from the very beginning. An example of a prompt sentence for the generative AI model related to this technology is shown below.

[0339] Example of a prompt:

[0340] Please propose effective methods for designing and implementing a system that reflects real-time seat availability and recommends personalized seats based on passenger attribute information.

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The server receives attribute information and seat selection history as input from passenger information terminals. This information is recorded in a database management system, and the data is processed to create a profile for each passenger. This profile serves as the basis for recommending the most suitable seat.

[0344] Step 2:

[0345] The server inputs passenger profile information stored in the database into an artificial intelligence algorithm. The AI ​​algorithm recommends seats that maximize passenger comfort and seating efficiency. This calculation takes into account past seat selection patterns and attribute information.

[0346] Step 3:

[0347] The server outputs seat information recommended by the AI ​​algorithm to the passenger's information terminal. This allows the terminal to display seat options optimized for the user. The user then selects a seat on the terminal based on this information.

[0348] Step 4:

[0349] The user selects their desired seat using an information terminal and sends a reservation request to the server. This reservation information is processed by the server, which then determines whether the reservation is valid or not.

[0350] Step 5:

[0351] The server determines whether the reservation is complete and sends the result as output to the user's information terminal. This notification is immediate, allowing the user to receive reservation confirmation and prepare to use their seat with peace of mind.

[0352] Step 6:

[0353] The terminal receives real-time seat availability information from the server and displays the current availability to the user. This allows the user to update their seat selection as needed based on the latest information.

[0354] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0355] This invention relates to a system that recognizes the emotional state of passengers and manages seating arrangements on public transportation accordingly. By incorporating an emotion engine, the system aims to provide optimal seating based on passengers' real-time emotions. An embodiment of this system is described in detail below.

[0356] The server first stores passenger profile information and past selection history in a database. Based on this information, the server prepares to provide passengers with the most suitable seats using an AI algorithm. At the same time, the server recognizes passengers' current emotions in real time via an emotion engine and uses that data to recommend seats.

[0357] Users can use their smartphones or tablets to view seating information tailored to their mood through the application. The app displays analysis results from an emotion engine and suggests the most comfortable and stress-free seat for the user. This seating selection is based on factors such as recommending a quiet zone if the user wants to spend time in peace, or a more comfortable seat if they want to relax.

[0358] After making their selection, the user sends a reservation request to the server through the app. Based on this request, the server determines whether a seat is available and reserves the seat best suited to the user's emotional state. The server sends a reservation confirmation to the user's device and records the reservation information in its database.

[0359] Furthermore, the server checks seat availability in real time and updates the database with seat usage information, including passenger sentiment. This allows both sentiment data and seat occupancy data to be considered, contributing to congestion reduction measures that ensure a comfortable experience for all passengers.

[0360] As a concrete example, consider the case where this system is used during rush hour when buses are nearing full capacity. When a user selects a seat using the app, the emotion engine senses the passenger's stress and fatigue and suggests the most suitable seat for that state. The user can then reserve the suggested seat and confirm it in the app, allowing them to board with peace of mind.

[0361] As described above, according to the embodiments of the present invention, users of public transportation can enjoy a comfortable journey that takes into account their mood at any given time.

[0362] The following describes the processing flow.

[0363] Step 1:

[0364] The server stores passenger profile information and selection history in a database. This information is used as foundational data for future seat recommendations and personalized service.

[0365] Step 2:

[0366] The device activates the emotion engine when the user logs into the application. It uses cameras and sensors to collect the user's facial expressions and biometric information, and analyzes their current emotional state in real time.

[0367] Step 3:

[0368] The server receives the analyzed sentiment data and compares it with profile information and past selection history in the database. Based on this information, the AI ​​algorithm recommends the seat that best suits the user's current emotional state.

[0369] Step 4:

[0370] The device displays seat recommendation information sent from the server to the user. The user can then use this recommendation to select their preferred seat within the app.

[0371] Step 5:

[0372] The user uses the app to send a seat reservation request to the server to confirm their reservation for the selected seat.

[0373] Step 6:

[0374] The server checks seat availability based on the received reservation request. If seats are available, it updates the reservation information in the database and sends a reservation confirmation notification to the user's device.

[0375] Step 7:

[0376] The terminal receives a reservation confirmation notification from the server and displays the reservation completion information to the user. This allows the user to confirm that their seat has been secured.

[0377] Step 8:

[0378] The server updates real-time seat availability information on buses and trains, and also records seat usage status based on sentiment analysis in its database.

[0379] Step 9:

[0380] The terminal provides users with real-time updated seat information via an app or in-car display. This allows users to enjoy a comfortable journey.

[0381] (Example 2)

[0382] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0383] In conventional public transportation, providing optimal seating based on passengers' emotions and individual needs has been difficult, leading to reduced comfort and increased stress during travel. In particular, the lack of consideration for seat selection based on passengers' emotional states can lead to decreased satisfaction and increased stress for users. Therefore, the present invention aims to improve the comfort of public transportation by accurately recognizing passengers' emotional states and providing seat recommendations based on those states.

[0384] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0385] In this invention, the server includes means for recording passenger attribute data and historical data in a data management device, means for using an artificial intelligence processing device that recommends the optimal location based on passenger information acquired from the data management device, and means for acquiring passenger emotional information in real time using an emotional analysis engine. This makes it possible to select the optimal seat according to the passenger's emotional state and provide it in real time, thereby significantly improving the comfort of users in public transportation.

[0386] "Passenger attribute data" refers to information that includes personal characteristics of passengers, such as name, travel history, and seating preferences.

[0387] "Historical data" refers to information that includes records of past actions and choices, particularly data representing previous seat selection and reservation history.

[0388] A "data management device" is a device that records, stores, and manages information, and specifically takes the form of a database system.

[0389] An "artificial intelligence processing device" refers to a combination of software and hardware used to analyze data and make decisions and predictions according to specific purposes, and includes AI algorithms.

[0390] The "emotion analysis engine" is a system that identifies passengers' emotional states from various input data and outputs them as numerical values ​​or categories, utilizing a machine learning model.

[0391] "Location" refers to a specific seat or space within public transport, a place that is physically occupied or usable by passengers.

[0392] In an embodiment of this invention, a system is realized that provides seating tailored to the feelings of passengers within public transportation. The system consists of three main elements: a server, a terminal, and a user.

[0393] Server role:

[0394] The server first records passenger attribute and history data in a data management device. This data management device is typically implemented as an SQL database system (e.g., MySQL). Next, the server uses an artificial intelligence (AI) processing unit to recommend the optimal seat based on the passenger information. This process utilizes AI algorithms, and platforms such as TensorFlow and PyTorch are available. Furthermore, a sentiment analysis engine is used to analyze voice and text data provided by the passenger's device (e.g., prompts such as "Please tell us your current emotional state") to obtain emotional information in real time. This information is used by the AI ​​processing unit to recommend the optimal seat. After the reservation is complete, the server records this information in the data management device and updates the seat availability in real time.

[0395] Terminal role:

[0396] Passengers launch a dedicated application on their mobile devices, such as smartphones or tablets. The application interacts with a server and displays results from an emotion analysis engine. This allows the user to be offered seats that match their emotional state. Once a selection is made, the device sends a reservation request to the server and receives and displays a reservation confirmation notification.

[0397] User roles:

[0398] Users typically access the application using a smartphone or tablet. When passengers respond to prompts, the application provides real-time information on the best seating options. Based on this information, users can choose a seat and use public transport with peace of mind. This implementation allows users to enhance comfort and reduce stress during their journey.

[0399] This system is designed to be used especially during crowded times such as rush hour, and is capable of further improving the passenger travel experience.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The server receives passenger attribute and history data transmitted from terminals and records it in the data management device. Inputs include the passenger's name, previous booking history, and seat preferences. Based on the recorded data, a foundation is created for providing personalized seating suggestions to individual passengers. This data is stored in an SQL database.

[0403] Step 2:

[0404] The server uses an emotion analysis engine to analyze voice and text data transmitted from the terminal. The data analyzed is input by the user in response to the prompt, "Please tell me your current emotional state." Through this process, the server outputs the current emotional state as numerical or categorical data, which is used as input data in the next processing step.

[0405] Step 3:

[0406] The server uses an artificial intelligence processing unit to calculate the optimal seat based on collected passenger profile data and emotional data. The AI ​​algorithm selects a seat location appropriate to the passenger's emotional state and past behavior. It generates output tailored to individual needs, such as suggesting a seat in a quiet zone if the passenger's emotions indicate a desire for relaxation.

[0407] Step 4:

[0408] Users review seat suggestions provided by the server using a dedicated application on their terminal. The application displays sentiment analysis and optimization results, offering passengers choices. Users select their preferred seat from the presented options, and their selection is sent from the terminal to the server.

[0409] Step 5:

[0410] The server receives reservation requests from users and checks seat availability in real time. If an available seat is found, the server reserves the seat and notifies the terminal that the reservation is complete. The server records the reservation completion information in a data management device, accumulating data to improve the accuracy of future recommendations.

[0411] Step 6:

[0412] The server periodically evaluates the overall seat occupancy rate and updates the database, taking into account available seats and the emotional state of each passenger. This real-time update improves the overall efficiency of seat allocation and contributes to a comfortable travel experience for passengers.

[0413] (Application Example 2)

[0414] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0415] Modern public transportation systems struggle to provide services based on passengers' emotional states, resulting in passengers being unable to find comfortable seats that suit their mood. Furthermore, the inability to manage seating based on emotions has led to insufficient stress reduction measures during peak hours. Therefore, there is a need for the development of seating management systems that take passenger emotions into consideration.

[0416] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0417] In this invention, the server includes a recording device that stores passenger profile information and selection history, a device that uses a machine learning algorithm to recommend an appropriate seat based on passenger data retrieved from the recording device, and an emotion analysis device that recognizes the passenger's emotional state and reflects it in seat recommendations. This makes it possible to provide appropriate seats that take passenger emotions into consideration.

[0418] A "recording device" is a data storage device for saving passenger profile information and selection history.

[0419] A "device using machine learning algorithms" is a computing device that utilizes machine learning to recommend appropriate seats based on passenger data.

[0420] An "information terminal" is a communication device used by passengers that has the function of receiving reservation notifications.

[0421] A "real-time update device" is a system that updates seat availability information in real time, providing the most up-to-date information.

[0422] A "sentiment analysis device" is a data processing device that recognizes and analyzes the emotional state of passengers and reflects the results in seat management.

[0423] A "seat arrangement suggestion device" is a system that suggests the optimal seat arrangement based on the emotional state of the passengers.

[0424] The system for realizing this application consists of a device that recognizes the passenger's emotional state and suggests the optimal seat based on that state. The server stores the passenger's profile information and past seat selection history in a recording device. Next, when the passenger accesses the system via an information terminal such as a smartphone or tablet, the server uses a device employing machine learning algorithms to perform calculations to select the optimal seat.

[0425] The server uses an emotion analysis device to identify passengers' real-time emotional states and incorporates the results into the seat recommendation process. This makes it possible to suggest comfortable seats tailored to each passenger's emotional state. For example, if a passenger is seeking quiet, the server can guide them to a seat where extraneous noises are less audible.

[0426] For terminals, the server uses a seat placement suggestion device to process the reservation of the suggested seat. A notification of reservation completion is sent to the terminal, and passengers can check this information in real time. For example, if a passenger is detected as fatigued, a seat with a reclining function can be suggested.

[0427] An example of a prompt using a generative AI model is, "When passengers are experiencing high stress levels, please suggest what seating characteristics would allow them to relax more," which provides guidance on specific seating characteristics. In this way, the server, terminal, and user elements work together to create a system that provides a comfortable riding experience.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The server stores passenger profile information and selection history in a recording device. Past passenger selection data is used as input for data storage operations. The output is a well-organized database usable for subsequent processing.

[0431] Step 2:

[0432] Users access the system using smartphones or tablets. In this process, the information terminal launches an application and sends a connection request to the server. The input is the user's connection request data, and the output is the establishment of a communication session with the server.

[0433] Step 3:

[0434] The server uses machine learning algorithms to select the optimal seat based on passenger data acquired from recording devices. Passenger profile information and emotional state are provided as input, and the server analyzes this data to output the most suitable seat candidates.

[0435] Step 4:

[0436] The emotion analysis device identifies the user's real-time emotional state. It takes the user's facial image and voice data as input, analyzes them, and outputs the emotional state. The server receives this emotional data.

[0437] Step 5:

[0438] The server uses data from the sentiment analysis device to perform a seat recommendation process and transmits the results to the terminal via the seat placement suggestion device. The inputs are sentiment analysis results and seat data, and the output provides the most suitable seat information.

[0439] Step 6:

[0440] The terminal displays seat suggestions sent from the server to the user. The user reviews them and makes a reservation selection as needed. The input is the seat suggestion data on the terminal, and the output is the user's seat reservation selection.

[0441] Step 7:

[0442] When a user reserves a seat, the server sends a reservation confirmation notification to the terminal. The server receives the user's reservation selection data as input, and a reservation confirmation notification is sent to the terminal as output.

[0443] Step 8:

[0444] The server updates seat availability and passenger sentiment in real time. This process involves inputting newly collected passenger sentiment data and seat usage information, using it to update the database, and then sharing the updated seat information across the entire system as output.

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0446] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0451] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0453] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0455] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0456] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0457] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0458] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0460] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0461] This invention relates to a seat management system for public transportation that improves passenger convenience. This system uses passenger profile information and seat selection history to recommend seats optimized for the individual needs of each passenger. The embodiments thereof are described in detail below.

[0462] The server records passenger profile information and selection history in a database. This allows for the accumulation of individual passenger preferences and travel patterns. Based on this data, the server utilizes AI algorithms to dynamically recommend seats suitable for each passenger. The AI ​​analyzes diverse passenger data to help passengers choose the most comfortable and efficient seats.

[0463] Users can use their own devices to check for available seats and select a seat. After selection, the device sends a reservation request to the server. The server determines whether the seat is available based on the received request and notifies the user's device of the result. This notification allows the user to immediately know whether their reservation has been confirmed.

[0464] Furthermore, the server updates seat availability information in real time, instantly reflecting the status of available seats on public transport vehicles. Based on this updated information, terminals display available seat information to passengers via apps and in-vehicle displays. This allows users to quickly find currently available seats and travel with peace of mind.

[0465] As a concrete example, when this system is used on a crowded line during the morning commute, users can instantly check the current seat availability on the train via the app and receive seat recommendations from AI. Users can then select and reserve a seat through the app and receive a confirmation notification on their smartphone, allowing them to begin their journey without stress.

[0466] As described above, the seat management system of the present invention provides passengers with a highly personalized seat selection service and improves the experience of using public transportation.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The server stores passenger profile information and selection history in a database. This records each passenger's individual preferences and past usage patterns, ready to be used for future seat recommendations.

[0470] Step 2:

[0471] Users access the application using devices such as smartphones and tablets to check seat information. The app displays a list of currently available seats and provides them to the user in an easy-to-read format.

[0472] Step 3:

[0473] When a user selects a seat on the app, the server uses an AI algorithm to recommend the optimal seat based on their individual preference patterns. The recommendation results are displayed on the user's device in real time.

[0474] Step 4:

[0475] Users select their preferred seat through the app, taking into account seat recommendations from the server. After completing their selection, they confirm the reservation request on the app and begin the reservation process.

[0476] Step 5:

[0477] The terminal sends the user's seat selection and reservation request to the server. During this process, the terminal implements security measures to protect user information.

[0478] Step 6:

[0479] The server processes the received seat reservation request and checks if the seat is available. If the seat is available, it updates the reservation information in the database and simultaneously sends a reservation confirmation notification to the user's device.

[0480] Step 7:

[0481] The device receives a reservation confirmation notification from the server and displays the notification to the user. The user can confirm that the reservation is complete on the app screen and prepare for boarding.

[0482] Step 8:

[0483] The server retrieves real-time information on available seats in the vehicles and updates the database with the latest status. This ensures that the seat information shared with all users is always up-to-date.

[0484] Step 9:

[0485] The terminal provides passengers with the latest seat availability information through an app and in-vehicle displays. This helps users more easily select a suitable seat when boarding.

[0486] (Example 1)

[0487] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] Providing passengers with the most suitable seats quickly on public transportation has been challenging. Conventional systems struggled to select seats based on individual passenger needs and preferences, and insufficient real-time updates of seat information prevented significant improvements in passenger convenience and satisfaction. A new system is needed to address these issues.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0490] In this invention, the server includes means for recording passengers' personal information and choice history in a data storage unit, means for using a generative AI model that recommends appropriate seats based on passenger information obtained from the data storage unit, and means for sending a reservation confirmation message to the passenger's information terminal. This enables real-time seat recommendations tailored to the individual needs of passengers, allowing passengers to quickly secure the optimal seat and significantly improving the public transport user experience.

[0491] "Passenger personal information" refers to basic attribute data about passengers, including information about their name and seating preferences.

[0492] "Selection history" refers to records of passengers' past seat selections and usage patterns.

[0493] A "data storage unit" is a storage device, such as a database, for securely storing passengers' personal information and their choice history.

[0494] A "generative AI model" is an artificial intelligence algorithm that analyzes data to recommend the best seat for each passenger.

[0495] An "information terminal" is a device that passengers can use to receive messages such as reservation confirmations.

[0496] "Seat availability" refers to information indicating the current status of available seats on public transportation.

[0497] An "information display device" is a device such as a display or screen that provides passengers with real-time information, such as seat availability.

[0498] This invention provides a seat management system for public transport that enables personalized seat recommendations for passengers. The system has the following configuration:

[0499] Data processing by the server:

[0500] The server records passengers' personal information and seat selection history in a data storage unit. This allows the server to understand passenger preferences and travel patterns, creating a foundation for efficient analysis. The server feeds this data into a generative AI model to recommend the most suitable seat for each passenger. The generative AI model learns optimal seating patterns based on past data and dynamically optimizes new seat selections.

[0501] User actions:

[0502] Users operate the application using information terminals such as smartphones to check seat availability in real time. Through the app, users can view seat recommendations provided by the server, select the seat that best suits them, and make a reservation. Once the reservation is complete, a confirmation message is displayed on the device. This allows users to secure a seat smoothly.

[0503] Real-time updates:

[0504] The server constantly monitors seat availability on public transport and displays the information in real time. Through this updated information sent to terminals, users can travel while being aware of the latest seat availability.

[0505] Specific usage scenarios:

[0506] For example, during the morning rush hour, if a user uses the app saying, "I'm on the 6 AM train to work. I want to open the app to check the current seat availability and find the best seat for me," the AI ​​model will recommend the most suitable seat. In this way, users can experience highly convenient travel in real time.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] The server records passengers' personal information and seat selection history in a data storage unit.

[0510] Input: Personal information and past seat selection history provided by the user.

[0511] Data processing: Organize this information and convert it into a format that can be stored in a database.

[0512] Output: The organized passenger data is stored in the data storage unit.

[0513] Step 2:

[0514] The server inputs the aforementioned data into a generating AI model and recommends a suitable seat.

[0515] Input: Passenger personal information and seat selection history stored in the data storage unit.

[0516] Data processing: Generative AI models identify patterns and relationships in data and perform analysis for seat recommendations.

[0517] Output: Recommended seat information is generated.

[0518] Step 3:

[0519] Users use their devices to view and select recommended seats within the app.

[0520] Input: Seat recommendation information received from the server.

[0521] Specific operation: The user visually checks the recommended seats through a GUI on the terminal and makes a selection using their finger or mouse.

[0522] Output: Reservation request for the seat selected by the user.

[0523] Step 4:

[0524] The server processes reservation requests from users and determines the availability of seats.

[0525] Input: Seat reservation request based on user selection.

[0526] Data processing: Check the database for seat availability. If a reservation is possible, update the database with the reservation.

[0527] Output: Result of whether it is available or unavailable for reservation.

[0528] Step 5:

[0529] The device notifies the user of the reservation result.

[0530] Input: Reservation result notification from the server.

[0531] Specific operation: The device will use push notifications or in-app messaging to display the reservation status to the user.

[0532] Output: Reservation confirmation message sent to the user.

[0533] Step 6:

[0534] The server updates seat availability in real time and reflects it on the information display.

[0535] Input: All reservation and cancellation information.

[0536] Data processing: Re-evaluate the current seating situation and update the availability status to reflect the latest information.

[0537] Output: The updated seat availability information is reflected on the information display device and terminal.

[0538] (Application Example 1)

[0539] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] In recent years, with the proliferation of autonomous vehicles, the importance of seat management systems for providing an efficient and comfortable travel experience has increased. However, existing systems often result in congestion and inconvenience due to insufficient real-time updates of seat information and inadequate seat recommendations optimized for individual passenger needs. To solve this problem, a system is needed that can accurately grasp passenger profile information and travel trends and update seat information in real time.

[0541] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0542] In this invention, the server includes means for recording passenger attribute information and selection history in an information storage device, means for using an artificial intelligence algorithm that recommends an optimized seat based on the passenger information obtained from the information storage device, and means for transmitting reservation completion information to the passenger's information terminal. This enables passengers to have a comfortable and efficient travel experience in an autonomous vehicle.

[0543] "Passenger attribute information" refers to information about each passenger's individual characteristics, preferences, and history, and is data used to make optimal suggestions when selecting seats.

[0544] An "information storage device" is a system or device that stores digital information and allows it to be retrieved as needed.

[0545] An "optimized seat" is a seat selected to maximize passenger comfort and travel efficiency, taking into account the passenger's attribute information and history.

[0546] An "artificial intelligence algorithm" is a computational method that mimics the ability of computers to analyze large amounts of data and think and make decisions like humans.

[0547] An "information terminal" is an electronic device used by passengers to input their own information or to receive information from a system.

[0548] The seat management system for realizing this application operates using a combination of the following hardware and software: Hardware includes a server, passenger information terminals (smartphones and tablets), and displays within the autonomous vehicle. Software includes a database management system, a program for executing artificial intelligence algorithms, and network software for real-time communication.

[0549] The server is responsible for recording passenger attribute information and seat selection history in an information storage device. This allows for the accumulation of individual passenger travel history and preference information. Based on the accumulated data, the server activates an artificial intelligence algorithm to recommend optimized seats, identifying seats that enhance passenger comfort and seating efficiency. After the reservation is complete, the server sends this information to the passenger's information terminal to inform them that a seat has been secured.

[0550] The terminal displays real-time seat availability information to passengers and allows them to make reservations using their smartphones or other information devices. The terminal receives reservation completion notifications from the server and immediately notifies the user. This allows passengers to quickly and efficiently select seats based on the latest seat availability.

[0551] As a concrete example, when using this system during a family trip, it becomes easier for all family members to choose seats close to each other, allowing them to enjoy a comfortable journey from the very beginning. An example of a prompt sentence for the generative AI model related to this technology is shown below.

[0552] Example of a prompt:

[0553] Please propose effective methods for designing and implementing a system that reflects real-time seat availability and recommends personalized seats based on passenger attribute information.

[0554] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0555] Step 1:

[0556] The server receives attribute information and seat selection history as input from passenger information terminals. This information is recorded in a database management system, and the data is processed to create a profile for each passenger. This profile serves as the basis for recommending the most suitable seat.

[0557] Step 2:

[0558] The server inputs passenger profile information stored in the database into an artificial intelligence algorithm. The AI ​​algorithm recommends seats that maximize passenger comfort and seating efficiency. This calculation takes into account past seat selection patterns and attribute information.

[0559] Step 3:

[0560] The server outputs seat information recommended by the AI ​​algorithm to the passenger's information terminal. This allows the terminal to display seat options optimized for the user. The user then selects a seat on the terminal based on this information.

[0561] Step 4:

[0562] The user selects their desired seat using an information terminal and sends a reservation request to the server. This reservation information is processed by the server, which then determines whether the reservation is valid or not.

[0563] Step 5:

[0564] The server determines whether the reservation is complete and sends the result as output to the user's information terminal. This notification is immediate, allowing the user to receive reservation confirmation and prepare to use their seat with peace of mind.

[0565] Step 6:

[0566] The terminal receives real-time seat availability information from the server and displays the current availability to the user. This allows the user to update their seat selection as needed based on the latest information.

[0567] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0568] This invention relates to a system that recognizes the emotional state of passengers and manages seating arrangements on public transportation accordingly. By incorporating an emotion engine, the system aims to provide optimal seating based on passengers' real-time emotions. An embodiment of this system is described in detail below.

[0569] The server first stores passenger profile information and past selection history in a database. Based on this information, the server prepares to provide passengers with the most suitable seats using an AI algorithm. At the same time, the server recognizes passengers' current emotions in real time via an emotion engine and uses that data to recommend seats.

[0570] Users can use their smartphones or tablets to view seating information tailored to their mood through the application. The app displays analysis results from an emotion engine and suggests the most comfortable and stress-free seat for the user. This seating selection is based on factors such as recommending a quiet zone if the user wants to spend time in peace, or a more comfortable seat if they want to relax.

[0571] After making their selection, the user sends a reservation request to the server through the app. Based on this request, the server determines whether a seat is available and reserves the seat best suited to the user's emotional state. The server sends a reservation confirmation to the user's device and records the reservation information in its database.

[0572] Furthermore, the server checks seat availability in real time and updates the database with seat usage information, including passenger sentiment. This allows both sentiment data and seat occupancy data to be considered, contributing to congestion reduction measures that ensure a comfortable experience for all passengers.

[0573] As a concrete example, consider the case where this system is used during rush hour when buses are nearing full capacity. When a user selects a seat using the app, the emotion engine senses the passenger's stress and fatigue and suggests the most suitable seat for that state. The user can then reserve the suggested seat and confirm it in the app, allowing them to board with peace of mind.

[0574] As described above, according to the embodiments of the present invention, users of public transportation can enjoy a comfortable journey that takes into account their mood at any given time.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server stores passenger profile information and selection history in a database. This information is used as foundational data for future seat recommendations and personalized service.

[0578] Step 2:

[0579] The device activates the emotion engine when the user logs into the application. It uses cameras and sensors to collect the user's facial expressions and biometric information, and analyzes their current emotional state in real time.

[0580] Step 3:

[0581] The server receives the analyzed sentiment data and compares it with profile information and past selection history in the database. Based on this information, the AI ​​algorithm recommends the seat that best suits the user's current emotional state.

[0582] Step 4:

[0583] The device displays seat recommendation information sent from the server to the user. The user can then use this recommendation to select their preferred seat within the app.

[0584] Step 5:

[0585] The user uses the app to send a seat reservation request to the server to confirm their reservation for the selected seat.

[0586] Step 6:

[0587] The server checks seat availability based on the received reservation request. If seats are available, it updates the reservation information in the database and sends a reservation confirmation notification to the user's device.

[0588] Step 7:

[0589] The terminal receives a reservation confirmation notification from the server and displays the reservation completion information to the user. This allows the user to confirm that their seat has been secured.

[0590] Step 8:

[0591] The server updates real-time seat availability information on buses and trains, and also records seat usage status based on sentiment analysis in its database.

[0592] Step 9:

[0593] The terminal provides users with real-time updated seat information via an app or in-car display. This allows users to enjoy a comfortable journey.

[0594] (Example 2)

[0595] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0596] In conventional public transportation, providing optimal seating based on passengers' emotions and individual needs has been difficult, leading to reduced comfort and increased stress during travel. In particular, the lack of consideration for seat selection based on passengers' emotional states can lead to decreased satisfaction and increased stress for users. Therefore, the present invention aims to improve the comfort of public transportation by accurately recognizing passengers' emotional states and providing seat recommendations based on those states.

[0597] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0598] In this invention, the server includes means for recording passenger attribute data and historical data in a data management device, means for using an artificial intelligence processing device that recommends the optimal location based on passenger information acquired from the data management device, and means for acquiring passenger emotional information in real time using an emotional analysis engine. This makes it possible to select the optimal seat according to the passenger's emotional state and provide it in real time, thereby significantly improving the comfort of users in public transportation.

[0599] "Passenger attribute data" refers to information that includes personal characteristics of passengers, such as name, travel history, and seating preferences.

[0600] "Historical data" refers to information that includes records of past actions and choices, particularly data representing previous seat selection and reservation history.

[0601] A "data management device" is a device that records, stores, and manages information, and specifically takes the form of a database system.

[0602] An "artificial intelligence processing device" refers to a combination of software and hardware used to analyze data and make decisions and predictions according to specific purposes, and includes AI algorithms.

[0603] The "emotion analysis engine" is a system that identifies passengers' emotional states from various input data and outputs them as numerical values ​​or categories, utilizing a machine learning model.

[0604] "Location" refers to a specific seat or space within public transport, a place that is physically occupied or usable by passengers.

[0605] In an embodiment of this invention, a system is realized that provides seating tailored to the feelings of passengers within public transportation. The system consists of three main elements: a server, a terminal, and a user.

[0606] Server role:

[0607] The server first records passenger attribute and history data in a data management device. This data management device is typically implemented as an SQL database system (e.g., MySQL). Next, the server uses an artificial intelligence (AI) processing unit to recommend the optimal seat based on the passenger information. This process utilizes AI algorithms, and platforms such as TensorFlow and PyTorch are available. Furthermore, a sentiment analysis engine is used to analyze voice and text data provided by the passenger's device (e.g., prompts such as "Please tell us your current emotional state") to obtain emotional information in real time. This information is used by the AI ​​processing unit to recommend the optimal seat. After the reservation is complete, the server records this information in the data management device and updates the seat availability in real time.

[0608] Terminal role:

[0609] Passengers launch a dedicated application on their mobile devices, such as smartphones or tablets. The application interacts with a server and displays results from an emotion analysis engine. This allows the user to be offered seats that match their emotional state. Once a selection is made, the device sends a reservation request to the server and receives and displays a reservation confirmation notification.

[0610] User roles:

[0611] Users typically access the application using a smartphone or tablet. When passengers respond to prompts, the application provides real-time information on the best seating options. Based on this information, users can choose a seat and use public transport with peace of mind. This implementation allows users to enhance comfort and reduce stress during their journey.

[0612] This system is designed to be used especially during crowded times such as rush hour, and is capable of further improving the passenger travel experience.

[0613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0614] Step 1:

[0615] The server receives passenger attribute and history data transmitted from terminals and records it in the data management device. Inputs include the passenger's name, previous booking history, and seat preferences. Based on the recorded data, a foundation is created for providing personalized seating suggestions to individual passengers. This data is stored in an SQL database.

[0616] Step 2:

[0617] The server uses an emotion analysis engine to analyze voice and text data transmitted from the terminal. The data analyzed is input by the user in response to the prompt, "Please tell me your current emotional state." Through this process, the server outputs the current emotional state as numerical or categorical data, which is used as input data in the next processing step.

[0618] Step 3:

[0619] The server uses an artificial intelligence processing unit to calculate the optimal seat based on collected passenger profile data and emotional data. The AI ​​algorithm selects a seat location appropriate to the passenger's emotional state and past behavior. It generates output tailored to individual needs, such as suggesting a seat in a quiet zone if the passenger's emotions indicate a desire for relaxation.

[0620] Step 4:

[0621] Users review seat suggestions provided by the server using a dedicated application on their terminal. The application displays sentiment analysis and optimization results, offering passengers choices. Users select their preferred seat from the presented options, and their selection is sent from the terminal to the server.

[0622] Step 5:

[0623] The server receives reservation requests from users and checks seat availability in real time. If an available seat is found, the server reserves the seat and notifies the terminal that the reservation is complete. The server records the reservation completion information in a data management device, accumulating data to improve the accuracy of future recommendations.

[0624] Step 6:

[0625] The server periodically evaluates the overall seat occupancy rate and updates the database, taking into account available seats and the emotional state of each passenger. This real-time update improves the overall efficiency of seat allocation and contributes to a comfortable travel experience for passengers.

[0626] (Application Example 2)

[0627] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0628] Modern public transportation systems struggle to provide services based on passengers' emotional states, resulting in passengers being unable to find comfortable seats that suit their mood. Furthermore, the inability to manage seating based on emotions has led to insufficient stress reduction measures during peak hours. Therefore, there is a need for the development of seating management systems that take passenger emotions into consideration.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0630] In this invention, the server includes a recording device that stores passenger profile information and selection history, a device that uses a machine learning algorithm to recommend an appropriate seat based on passenger data retrieved from the recording device, and an emotion analysis device that recognizes the passenger's emotional state and reflects it in seat recommendations. This makes it possible to provide appropriate seats that take passenger emotions into consideration.

[0631] A "recording device" is a data storage device for saving passenger profile information and selection history.

[0632] A "device using machine learning algorithms" is a computing device that utilizes machine learning to recommend appropriate seats based on passenger data.

[0633] An "information terminal" is a communication device used by passengers that has the function of receiving reservation notifications.

[0634] A "real-time update device" is a system that updates seat availability information in real time, providing the most up-to-date information.

[0635] A "sentiment analysis device" is a data processing device that recognizes and analyzes the emotional state of passengers and reflects the results in seat management.

[0636] A "seat arrangement suggestion device" is a system that suggests the optimal seat arrangement based on the emotional state of the passengers.

[0637] The system for realizing this application consists of a device that recognizes the passenger's emotional state and suggests the optimal seat based on that state. The server stores the passenger's profile information and past seat selection history in a recording device. Next, when the passenger accesses the system via an information terminal such as a smartphone or tablet, the server uses a device employing machine learning algorithms to perform calculations to select the optimal seat.

[0638] The server uses an emotion analysis device to identify passengers' real-time emotional states and incorporates the results into the seat recommendation process. This makes it possible to suggest comfortable seats tailored to each passenger's emotional state. For example, if a passenger is seeking quiet, the server can guide them to a seat where extraneous noises are less audible.

[0639] For terminals, the server uses a seat placement suggestion device to process the reservation of the suggested seat. A notification of reservation completion is sent to the terminal, and passengers can check this information in real time. For example, if a passenger is detected as fatigued, a seat with a reclining function can be suggested.

[0640] An example of a prompt using a generative AI model is, "When passengers are experiencing high stress levels, please suggest what seating characteristics would allow them to relax more," which provides guidance on specific seating characteristics. In this way, the server, terminal, and user elements work together to create a system that provides a comfortable riding experience.

[0641] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0642] Step 1:

[0643] The server stores passenger profile information and selection history in a recording device. Past passenger selection data is used as input for data storage operations. The output is a well-organized database usable for subsequent processing.

[0644] Step 2:

[0645] Users access the system using smartphones or tablets. In this process, the information terminal launches an application and sends a connection request to the server. The input is the user's connection request data, and the output is the establishment of a communication session with the server.

[0646] Step 3:

[0647] The server uses machine learning algorithms to select the optimal seat based on passenger data acquired from recording devices. Passenger profile information and emotional state are provided as input, and the server analyzes this data to output the most suitable seat candidates.

[0648] Step 4:

[0649] The emotion analysis device identifies the user's real-time emotional state. It takes the user's facial image and voice data as input, analyzes them, and outputs the emotional state. The server receives this emotional data.

[0650] Step 5:

[0651] The server uses data from the sentiment analysis device to perform a seat recommendation process and transmits the results to the terminal via the seat placement suggestion device. The inputs are sentiment analysis results and seat data, and the output provides the most suitable seat information.

[0652] Step 6:

[0653] The terminal displays seat suggestions sent from the server to the user. The user reviews them and makes a reservation selection as needed. The input is the seat suggestion data on the terminal, and the output is the user's seat reservation selection.

[0654] Step 7:

[0655] When a user reserves a seat, the server sends a reservation confirmation notification to the terminal. The server receives the user's reservation selection data as input, and a reservation confirmation notification is sent to the terminal as output.

[0656] Step 8:

[0657] The server updates seat availability and passenger sentiment in real time. This process involves inputting newly collected passenger sentiment data and seat usage information, using it to update the database, and then sharing the updated seat information across the entire system as output.

[0658] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0659] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0660] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0661] [Fourth Embodiment]

[0662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0663] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0664] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0665] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0666] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0667] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0668] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0669] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0670] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0671] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0672] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0673] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0674] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] This invention relates to a seat management system for public transportation that improves passenger convenience. This system uses passenger profile information and seat selection history to recommend seats optimized for the individual needs of each passenger. The embodiments thereof are described in detail below.

[0676] The server records passenger profile information and selection history in a database. This allows for the accumulation of individual passenger preferences and travel patterns. Based on this data, the server utilizes AI algorithms to dynamically recommend seats suitable for each passenger. The AI ​​analyzes diverse passenger data to help passengers choose the most comfortable and efficient seats.

[0677] Users can use their own devices to check for available seats and select a seat. After selection, the device sends a reservation request to the server. The server determines whether the seat is available based on the received request and notifies the user's device of the result. This notification allows the user to immediately know whether their reservation has been confirmed.

[0678] Furthermore, the server updates seat availability information in real time, instantly reflecting the status of available seats on public transport vehicles. Based on this updated information, terminals display available seat information to passengers via apps and in-vehicle displays. This allows users to quickly find currently available seats and travel with peace of mind.

[0679] As a concrete example, when this system is used on a crowded line during the morning commute, users can instantly check the current seat availability on the train via the app and receive seat recommendations from AI. Users can then select and reserve a seat through the app and receive a confirmation notification on their smartphone, allowing them to begin their journey without stress.

[0680] As described above, the seat management system of the present invention provides passengers with a highly personalized seat selection service and improves the experience of using public transportation.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The server stores passenger profile information and selection history in a database. This records each passenger's individual preferences and past usage patterns, ready to be used for future seat recommendations.

[0684] Step 2:

[0685] Users access the application using devices such as smartphones and tablets to check seat information. The app displays a list of currently available seats and provides them to the user in an easy-to-read format.

[0686] Step 3:

[0687] When a user selects a seat on the app, the server uses an AI algorithm to recommend the optimal seat based on their individual preference patterns. The recommendation results are displayed on the user's device in real time.

[0688] Step 4:

[0689] Users select their preferred seat through the app, taking into account seat recommendations from the server. After completing their selection, they confirm the reservation request on the app and begin the reservation process.

[0690] Step 5:

[0691] The terminal sends the user's seat selection and reservation request to the server. During this process, the terminal implements security measures to protect user information.

[0692] Step 6:

[0693] The server processes the received seat reservation request and checks if the seat is available. If the seat is available, it updates the reservation information in the database and simultaneously sends a reservation confirmation notification to the user's device.

[0694] Step 7:

[0695] The device receives a reservation confirmation notification from the server and displays the notification to the user. The user can confirm that the reservation is complete on the app screen and prepare for boarding.

[0696] Step 8:

[0697] The server retrieves real-time information on available seats in the vehicles and updates the database with the latest status. This ensures that the seat information shared with all users is always up-to-date.

[0698] Step 9:

[0699] The terminal provides passengers with the latest seat availability information through an app and in-vehicle displays. This helps users more easily select a suitable seat when boarding.

[0700] (Example 1)

[0701] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0702] Providing passengers with the most suitable seats quickly on public transportation has been challenging. Conventional systems struggled to select seats based on individual passenger needs and preferences, and insufficient real-time updates of seat information prevented significant improvements in passenger convenience and satisfaction. A new system is needed to address these issues.

[0703] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0704] In this invention, the server includes means for recording passengers' personal information and choice history in a data storage unit, means for using a generative AI model that recommends appropriate seats based on passenger information obtained from the data storage unit, and means for sending a reservation confirmation message to the passenger's information terminal. This enables real-time seat recommendations tailored to the individual needs of passengers, allowing passengers to quickly secure the optimal seat and significantly improving the public transport user experience.

[0705] "Passenger personal information" refers to basic attribute data about passengers, including information about their name and seating preferences.

[0706] "Selection history" refers to records of passengers' past seat selections and usage patterns.

[0707] A "data storage unit" is a storage device, such as a database, for securely storing passengers' personal information and their choice history.

[0708] A "generative AI model" is an artificial intelligence algorithm that analyzes data to recommend the best seat for each passenger.

[0709] An "information terminal" is a device that passengers can use to receive messages such as reservation confirmations.

[0710] "Seat availability" refers to information indicating the current status of available seats on public transportation.

[0711] An "information display device" is a device such as a display or screen that provides passengers with real-time information, such as seat availability.

[0712] This invention provides a seat management system for public transport that enables personalized seat recommendations for passengers. The system has the following configuration:

[0713] Data processing by the server:

[0714] The server records passengers' personal information and seat selection history in a data storage unit. This allows the server to understand passenger preferences and travel patterns, creating a foundation for efficient analysis. The server feeds this data into a generative AI model to recommend the most suitable seat for each passenger. The generative AI model learns optimal seating patterns based on past data and dynamically optimizes new seat selections.

[0715] User actions:

[0716] Users operate the application using information terminals such as smartphones to check seat availability in real time. Through the app, users can view seat recommendations provided by the server, select the seat that best suits them, and make a reservation. Once the reservation is complete, a confirmation message is displayed on the device. This allows users to secure a seat smoothly.

[0717] Real-time updates:

[0718] The server constantly monitors seat availability on public transport and displays the information in real time. Through this updated information sent to terminals, users can travel while being aware of the latest seat availability.

[0719] Specific usage scenarios:

[0720] For example, during the morning rush hour, if a user uses the app saying, "I'm on the 6 AM train to work. I want to open the app to check the current seat availability and find the best seat for me," the AI ​​model will recommend the most suitable seat. In this way, users can experience highly convenient travel in real time.

[0721] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0722] Step 1:

[0723] The server records passengers' personal information and seat selection history in a data storage unit.

[0724] Input: Personal information and past seat selection history provided by the user.

[0725] Data processing: Organize this information and convert it into a format that can be stored in a database.

[0726] Output: The organized passenger data is stored in the data storage unit.

[0727] Step 2:

[0728] The server inputs the aforementioned data into a generating AI model and recommends a suitable seat.

[0729] Input: Passenger personal information and seat selection history stored in the data storage unit.

[0730] Data processing: Generative AI models identify patterns and relationships in data and perform analysis for seat recommendations.

[0731] Output: Recommended seat information is generated.

[0732] Step 3:

[0733] Users use their devices to view and select recommended seats within the app.

[0734] Input: Seat recommendation information received from the server.

[0735] Specific operation: The user visually checks the recommended seats through a GUI on the terminal and makes a selection using their finger or mouse.

[0736] Output: Reservation request for the seat selected by the user.

[0737] Step 4:

[0738] The server processes reservation requests from users and determines the availability of seats.

[0739] Input: Seat reservation request based on user selection.

[0740] Data processing: Check the database for seat availability. If a reservation is possible, update the database with the reservation.

[0741] Output: Result of whether it is available or unavailable for reservation.

[0742] Step 5:

[0743] The device notifies the user of the reservation result.

[0744] Input: Reservation result notification from the server.

[0745] Specific operation: The device will use push notifications or in-app messaging to display the reservation status to the user.

[0746] Output: Reservation confirmation message sent to the user.

[0747] Step 6:

[0748] The server updates seat availability in real time and reflects it on the information display.

[0749] Input: All reservation and cancellation information.

[0750] Data processing: Re-evaluate the current seating situation and update the availability status to reflect the latest information.

[0751] Output: The updated seat availability information is reflected on the information display device and terminal.

[0752] (Application Example 1)

[0753] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] In recent years, with the proliferation of autonomous vehicles, the importance of seat management systems for providing an efficient and comfortable travel experience has increased. However, existing systems often result in congestion and inconvenience due to insufficient real-time updates of seat information and inadequate seat recommendations optimized for individual passenger needs. To solve this problem, a system is needed that can accurately grasp passenger profile information and travel trends and update seat information in real time.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0756] In this invention, the server includes means for recording passenger attribute information and selection history in an information storage device, means for using an artificial intelligence algorithm that recommends an optimized seat based on the passenger information obtained from the information storage device, and means for transmitting reservation completion information to the passenger's information terminal. This enables passengers to have a comfortable and efficient travel experience in an autonomous vehicle.

[0757] "Passenger attribute information" refers to information about each passenger's individual characteristics, preferences, and history, and is data used to make optimal suggestions when selecting seats.

[0758] An "information storage device" is a system or device that stores digital information and allows it to be retrieved as needed.

[0759] An "optimized seat" is a seat selected to maximize passenger comfort and travel efficiency, taking into account the passenger's attribute information and history.

[0760] An "artificial intelligence algorithm" is a computational method that mimics the ability of computers to analyze large amounts of data and think and make decisions like humans.

[0761] An "information terminal" is an electronic device used by passengers to input their own information or to receive information from a system.

[0762] The seat management system for realizing this application operates using a combination of the following hardware and software: Hardware includes a server, passenger information terminals (smartphones and tablets), and displays within the autonomous vehicle. Software includes a database management system, a program for executing artificial intelligence algorithms, and network software for real-time communication.

[0763] The server is responsible for recording passenger attribute information and seat selection history in an information storage device. This allows for the accumulation of individual passenger travel history and preference information. Based on the accumulated data, the server activates an artificial intelligence algorithm to recommend optimized seats, identifying seats that enhance passenger comfort and seating efficiency. After the reservation is complete, the server sends this information to the passenger's information terminal to inform them that a seat has been secured.

[0764] The terminal displays real-time seat availability information to passengers and allows them to make reservations using their smartphones or other information devices. The terminal receives reservation completion notifications from the server and immediately notifies the user. This allows passengers to quickly and efficiently select seats based on the latest seat availability.

[0765] As a concrete example, when using this system during a family trip, it becomes easier for all family members to choose seats close to each other, allowing them to enjoy a comfortable journey from the very beginning. An example of a prompt sentence for the generative AI model related to this technology is shown below.

[0766] Example of a prompt:

[0767] Please propose effective methods for designing and implementing a system that reflects real-time seat availability and recommends personalized seats based on passenger attribute information.

[0768] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0769] Step 1:

[0770] The server receives attribute information and seat selection history as input from passenger information terminals. This information is recorded in a database management system, and the data is processed to create a profile for each passenger. This profile serves as the basis for recommending the most suitable seat.

[0771] Step 2:

[0772] The server inputs passenger profile information stored in the database into an artificial intelligence algorithm. The AI ​​algorithm recommends seats that maximize passenger comfort and seating efficiency. This calculation takes into account past seat selection patterns and attribute information.

[0773] Step 3:

[0774] The server outputs seat information recommended by the AI ​​algorithm to the passenger's information terminal. This allows the terminal to display seat options optimized for the user. The user then selects a seat on the terminal based on this information.

[0775] Step 4:

[0776] The user selects their desired seat using an information terminal and sends a reservation request to the server. This reservation information is processed by the server, which then determines whether the reservation is valid or not.

[0777] Step 5:

[0778] The server determines whether the reservation is complete and sends the result as output to the user's information terminal. This notification is immediate, allowing the user to receive reservation confirmation and prepare to use their seat with peace of mind.

[0779] Step 6:

[0780] The terminal receives real-time seat availability information from the server and displays the current availability to the user. This allows the user to update their seat selection as needed based on the latest information.

[0781] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0782] This invention relates to a system that recognizes the emotional state of passengers and manages seating arrangements on public transportation accordingly. By incorporating an emotion engine, the system aims to provide optimal seating based on passengers' real-time emotions. An embodiment of this system is described in detail below.

[0783] The server first stores passenger profile information and past selection history in a database. Based on this information, the server prepares to provide passengers with the most suitable seats using an AI algorithm. At the same time, the server recognizes passengers' current emotions in real time via an emotion engine and uses that data to recommend seats.

[0784] Users can use their smartphones or tablets to view seating information tailored to their mood through the application. The app displays analysis results from an emotion engine and suggests the most comfortable and stress-free seat for the user. This seating selection is based on factors such as recommending a quiet zone if the user wants to spend time in peace, or a more comfortable seat if they want to relax.

[0785] After making their selection, the user sends a reservation request to the server through the app. Based on this request, the server determines whether a seat is available and reserves the seat best suited to the user's emotional state. The server sends a reservation confirmation to the user's device and records the reservation information in its database.

[0786] Furthermore, the server checks seat availability in real time and updates the database with seat usage information, including passenger sentiment. This allows both sentiment data and seat occupancy data to be considered, contributing to congestion reduction measures that ensure a comfortable experience for all passengers.

[0787] As a concrete example, consider the case where this system is used during rush hour when buses are nearing full capacity. When a user selects a seat using the app, the emotion engine senses the passenger's stress and fatigue and suggests the most suitable seat for that state. The user can then reserve the suggested seat and confirm it in the app, allowing them to board with peace of mind.

[0788] As described above, according to the embodiments of the present invention, users of public transportation can enjoy a comfortable journey that takes into account their mood at any given time.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The server stores passenger profile information and selection history in a database. This information is used as foundational data for future seat recommendations and personalized service.

[0792] Step 2:

[0793] The device activates the emotion engine when the user logs into the application. It uses cameras and sensors to collect the user's facial expressions and biometric information, and analyzes their current emotional state in real time.

[0794] Step 3:

[0795] The server receives the analyzed sentiment data and compares it with profile information and past selection history in the database. Based on this information, the AI ​​algorithm recommends the seat that best suits the user's current emotional state.

[0796] Step 4:

[0797] The device displays seat recommendation information sent from the server to the user. The user can then use this recommendation to select their preferred seat within the app.

[0798] Step 5:

[0799] The user uses the app to send a seat reservation request to the server to confirm their reservation for the selected seat.

[0800] Step 6:

[0801] The server checks seat availability based on the received reservation request. If seats are available, it updates the reservation information in the database and sends a reservation confirmation notification to the user's device.

[0802] Step 7:

[0803] The terminal receives a reservation confirmation notification from the server and displays the reservation completion information to the user. This allows the user to confirm that their seat has been secured.

[0804] Step 8:

[0805] The server updates real-time seat availability information on buses and trains, and also records seat usage status based on sentiment analysis in its database.

[0806] Step 9:

[0807] The terminal provides users with real-time updated seat information via an app or in-car display. This allows users to enjoy a comfortable journey.

[0808] (Example 2)

[0809] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0810] In conventional public transportation, providing optimal seating based on passengers' emotions and individual needs has been difficult, leading to reduced comfort and increased stress during travel. In particular, the lack of consideration for seat selection based on passengers' emotional states can lead to decreased satisfaction and increased stress for users. Therefore, the present invention aims to improve the comfort of public transportation by accurately recognizing passengers' emotional states and providing seat recommendations based on those states.

[0811] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0812] In this invention, the server includes means for recording passenger attribute data and historical data in a data management device, means for using an artificial intelligence processing device that recommends the optimal location based on passenger information acquired from the data management device, and means for acquiring passenger emotional information in real time using an emotional analysis engine. This makes it possible to select the optimal seat according to the passenger's emotional state and provide it in real time, thereby significantly improving the comfort of users in public transportation.

[0813] "Passenger attribute data" refers to information that includes personal characteristics of passengers, such as name, travel history, and seating preferences.

[0814] "Historical data" refers to information that includes records of past actions and choices, particularly data representing previous seat selection and reservation history.

[0815] A "data management device" is a device that records, stores, and manages information, and specifically takes the form of a database system.

[0816] An "artificial intelligence processing device" refers to a combination of software and hardware used to analyze data and make decisions and predictions according to specific purposes, and includes AI algorithms.

[0817] The "emotion analysis engine" is a system that identifies passengers' emotional states from various input data and outputs them as numerical values ​​or categories, utilizing a machine learning model.

[0818] "Location" refers to a specific seat or space within public transport, a place that is physically occupied or usable by passengers.

[0819] In an embodiment of this invention, a system is realized that provides seating tailored to the feelings of passengers within public transportation. The system consists of three main elements: a server, a terminal, and a user.

[0820] Server role:

[0821] The server first records passenger attribute and history data in a data management device. This data management device is typically implemented as an SQL database system (e.g., MySQL). Next, the server uses an artificial intelligence (AI) processing unit to recommend the optimal seat based on the passenger information. This process utilizes AI algorithms, and platforms such as TensorFlow and PyTorch are available. Furthermore, a sentiment analysis engine is used to analyze voice and text data provided by the passenger's device (e.g., prompts such as "Please tell us your current emotional state") to obtain emotional information in real time. This information is used by the AI ​​processing unit to recommend the optimal seat. After the reservation is complete, the server records this information in the data management device and updates the seat availability in real time.

[0822] Terminal role:

[0823] Passengers launch a dedicated application on their mobile devices, such as smartphones or tablets. The application interacts with a server and displays results from an emotion analysis engine. This allows the user to be offered seats that match their emotional state. Once a selection is made, the device sends a reservation request to the server and receives and displays a reservation confirmation notification.

[0824] User roles:

[0825] Users typically access the application using a smartphone or tablet. When passengers respond to prompts, the application provides real-time information on the best seating options. Based on this information, users can choose a seat and use public transport with peace of mind. This implementation allows users to enhance comfort and reduce stress during their journey.

[0826] This system is designed to be used especially during crowded times such as rush hour, and is capable of further improving the passenger travel experience.

[0827] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0828] Step 1:

[0829] The server receives passenger attribute and history data transmitted from terminals and records it in the data management device. Inputs include the passenger's name, previous booking history, and seat preferences. Based on the recorded data, a foundation is created for providing personalized seating suggestions to individual passengers. This data is stored in an SQL database.

[0830] Step 2:

[0831] The server uses an emotion analysis engine to analyze voice and text data transmitted from the terminal. The data analyzed is input by the user in response to the prompt, "Please tell me your current emotional state." Through this process, the server outputs the current emotional state as numerical or categorical data, which is used as input data in the next processing step.

[0832] Step 3:

[0833] The server uses an artificial intelligence processing unit to calculate the optimal seat based on collected passenger profile data and emotional data. The AI ​​algorithm selects a seat location appropriate to the passenger's emotional state and past behavior. It generates output tailored to individual needs, such as suggesting a seat in a quiet zone if the passenger's emotions indicate a desire for relaxation.

[0834] Step 4:

[0835] Users review seat suggestions provided by the server using a dedicated application on their terminal. The application displays sentiment analysis and optimization results, offering passengers choices. Users select their preferred seat from the presented options, and their selection is sent from the terminal to the server.

[0836] Step 5:

[0837] The server receives reservation requests from users and checks seat availability in real time. If an available seat is found, the server reserves the seat and notifies the terminal that the reservation is complete. The server records the reservation completion information in a data management device, accumulating data to improve the accuracy of future recommendations.

[0838] Step 6:

[0839] The server periodically evaluates the overall seat occupancy rate and updates the database, taking into account available seats and the emotional state of each passenger. This real-time update improves the overall efficiency of seat allocation and contributes to a comfortable travel experience for passengers.

[0840] (Application Example 2)

[0841] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0842] Modern public transportation systems struggle to provide services based on passengers' emotional states, resulting in passengers being unable to find comfortable seats that suit their mood. Furthermore, the inability to manage seating based on emotions has led to insufficient stress reduction measures during peak hours. Therefore, there is a need for the development of seating management systems that take passenger emotions into consideration.

[0843] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0844] In this invention, the server includes a recording device that stores passenger profile information and selection history, a device that uses a machine learning algorithm to recommend an appropriate seat based on passenger data retrieved from the recording device, and an emotion analysis device that recognizes the passenger's emotional state and reflects it in seat recommendations. This makes it possible to provide appropriate seats that take passenger emotions into consideration.

[0845] A "recording device" is a data storage device for saving passenger profile information and selection history.

[0846] A "device using machine learning algorithms" is a computing device that utilizes machine learning to recommend appropriate seats based on passenger data.

[0847] An "information terminal" is a communication device used by passengers that has the function of receiving reservation notifications.

[0848] A "real-time update device" is a system that updates seat availability information in real time, providing the most up-to-date information.

[0849] A "sentiment analysis device" is a data processing device that recognizes and analyzes the emotional state of passengers and reflects the results in seat management.

[0850] A "seat arrangement suggestion device" is a system that suggests the optimal seat arrangement based on the emotional state of the passengers.

[0851] The system for realizing this application consists of a device that recognizes the passenger's emotional state and suggests the optimal seat based on that state. The server stores the passenger's profile information and past seat selection history in a recording device. Next, when the passenger accesses the system via an information terminal such as a smartphone or tablet, the server uses a device employing machine learning algorithms to perform calculations to select the optimal seat.

[0852] The server uses an emotion analysis device to identify passengers' real-time emotional states and incorporates the results into the seat recommendation process. This makes it possible to suggest comfortable seats tailored to each passenger's emotional state. For example, if a passenger is seeking quiet, the server can guide them to a seat where extraneous noises are less audible.

[0853] For terminals, the server uses a seat placement suggestion device to process the reservation of the suggested seat. A notification of reservation completion is sent to the terminal, and passengers can check this information in real time. For example, if a passenger is detected as fatigued, a seat with a reclining function can be suggested.

[0854] An example of a prompt using a generative AI model is, "When passengers are experiencing high stress levels, please suggest what seating characteristics would allow them to relax more," which provides guidance on specific seating characteristics. In this way, the server, terminal, and user elements work together to create a system that provides a comfortable riding experience.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The server stores passenger profile information and selection history in a recording device. Past passenger selection data is used as input for data storage operations. The output is a well-organized database usable for subsequent processing.

[0858] Step 2:

[0859] Users access the system using smartphones or tablets. In this process, the information terminal launches an application and sends a connection request to the server. The input is the user's connection request data, and the output is the establishment of a communication session with the server.

[0860] Step 3:

[0861] The server uses machine learning algorithms to select the optimal seat based on passenger data acquired from recording devices. Passenger profile information and emotional state are provided as input, and the server analyzes this data to output the most suitable seat candidates.

[0862] Step 4:

[0863] The emotion analysis device identifies the user's real-time emotional state. It takes the user's facial image and voice data as input, analyzes them, and outputs the emotional state. The server receives this emotional data.

[0864] Step 5:

[0865] The server uses data from the sentiment analysis device to perform a seat recommendation process and transmits the results to the terminal via the seat placement suggestion device. The inputs are sentiment analysis results and seat data, and the output provides the most suitable seat information.

[0866] Step 6:

[0867] The terminal displays seat suggestions sent from the server to the user. The user reviews them and makes a reservation selection as needed. The input is the seat suggestion data on the terminal, and the output is the user's seat reservation selection.

[0868] Step 7:

[0869] When a user reserves a seat, the server sends a reservation confirmation notification to the terminal. The server receives the user's reservation selection data as input, and a reservation confirmation notification is sent to the terminal as output.

[0870] Step 8:

[0871] The server updates seat availability and passenger sentiment in real time. This process involves inputting newly collected passenger sentiment data and seat usage information, using it to update the database, and then sharing the updated seat information across the entire system as output.

[0872] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0873] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0874] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0875] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0876] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0877] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0878] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0879] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0880] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0881] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0882] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0883] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0884] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0885] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0886] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0887] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0888] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0889] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0890] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0891] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0892] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0893] The following is further disclosed regarding the embodiments described above.

[0894] (Claim 1)

[0895] A means for recording passenger profile information and selection history in a database,

[0896] A means of using an AI algorithm that recommends the optimal seat based on passenger information obtained from the aforementioned database,

[0897] A means of sending a reservation confirmation notification to the passenger's device,

[0898] A means of updating and displaying real-time seat availability information,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for receiving seat status information in real time and reflecting it as the latest information.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for analyzing passenger movement patterns and providing congestion mitigation measures that take into account seat occupancy rates.

[0904] "Example 1"

[0905] (Claim 1)

[0906] A means equipped with a function to record passenger personal information and choice behavior history in a data storage unit,

[0907] A means of using a generative AI model that recommends appropriate seats based on passenger information obtained from the data storage unit,

[0908] A means of sending a reservation confirmation message to the passenger's information terminal,

[0909] A means of updating seat availability in real time and reflecting it on an information display device,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, comprising means for receiving real-time seat occupancy status and reflecting it as the latest version.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising means for analyzing passenger movement trends and proposing congestion mitigation measures that take seat occupancy rates into consideration.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] Means for recording passenger attribute information and selection history in an information storage device,

[0918] A means of using an artificial intelligence algorithm that recommends optimized seats based on passenger information acquired from the aforementioned information storage device,

[0919] A means of sending reservation completion information to passenger information terminals,

[0920] A means of updating and displaying real-time seat availability information,

[0921] In autonomous vehicles, a means of predicting passenger seating arrangements and providing a comfortable travel experience,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, comprising means for acquiring seat status information in real time and reflecting it as the latest information.

[0925] (Claim 3)

[0926] The system according to claim 1, comprising means for analyzing passenger movement trends and proposing congestion mitigation measures that take into account seat occupancy rates.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] Means for recording passenger attribute data and history data in a data management device,

[0930] A means of using an artificial intelligence processing device that recommends the optimal location based on passenger information acquired from the aforementioned data management device,

[0931] A means of acquiring passenger emotional information in real time using an emotion analysis engine,

[0932] A means of sending a reservation completion notification to the passenger's terminal device,

[0933] A means of updating and displaying real-time location availability information,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, comprising means for receiving location status information in real time and reflecting it as the latest information.

[0937] (Claim 3)

[0938] The system according to claim 1, comprising means for analyzing passenger movement characteristics and providing congestion mitigation measures that take into account seat utilization rates.

[0939] "Application example 2 of combining emotional engines"

[0940] (Claim 1)

[0941] A recording device that stores passenger profile information and selection history,

[0942] A device that uses a machine learning algorithm to recommend an appropriate location based on passenger data extracted from the aforementioned recording device,

[0943] A device that delivers a reservation completion notification to the passenger's information terminal,

[0944] A device that updates and displays seat availability information in real time,

[0945] An emotion analysis device that recognizes emotional states and reflects them in seat recommendations,

[0946] A device that proposes a seating arrangement optimized for the emotional state of passengers,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, comprising a device that receives seat status data in real time and applies it as the latest information.

[0950] (Claim 3)

[0951] The system according to claim 1, comprising a device that analyzes passenger movement behavior and provides congestion mitigation measures that take into account seat utilization rates. [Explanation of Symbols]

[0952] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for recording passenger profile information and selection history in a database, A means of using an AI algorithm that recommends the optimal seat based on passenger information obtained from the aforementioned database, A means of sending a reservation confirmation notification to the passenger's device, A means of updating and displaying real-time seat availability information, A system that includes this.

2. The system according to claim 1, comprising means for receiving seat status information in real time and reflecting it as the latest information.

3. The system according to claim 1, comprising means for analyzing passenger movement patterns and providing congestion mitigation measures that take into account seat occupancy rates.

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