system

Abstract

The system according to this embodiment aims to optimize seat usage on trains during commuting hours and to promote a culture of giving up seats to others. [Solution] The system according to the embodiment comprises a reception unit, a prediction unit, a display unit, a reward unit, and a usage unit. The reception unit receives input from the passenger's intended disembarking station. The prediction unit predicts available seats based on the information entered by the reception unit. The display unit displays the available seat information predicted by the prediction unit. The reward unit rewards the act of giving up a seat as points. The usage unit uses the points awarded by the reward unit.

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 the conventional technology, there is a problem that the seat utilization in trains during commuting and school commuting hours is not optimized, and the culture of giving up seats is not sufficiently promoted.

[0005] The system according to the embodiment aims to optimize the seat utilization in trains during commuting and school commuting hours and promote the culture of giving up seats.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a prediction unit, a display unit, a reward unit, and a usage unit. The reception unit receives input from the passenger's intended disembarking station. The prediction unit predicts available seats based on the information entered by the reception unit. The display unit displays the available seat information predicted by the prediction unit. The reward unit rewards the act of giving up a seat in the form of points. The usage unit uses the points awarded by the reward unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize seat usage on trains during commuting hours and promote a culture of giving up seats to others. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Happy Seat system according to an embodiment of the present invention is a platform for optimizing seat usage on trains during commuting hours and promoting a culture of giving up seats. In the Happy Seat system, passengers input their intended disembarking station through an app, and an AI predicts which seats will become available. Giving up a seat is rewarded as points, which can be used for train fares and at participating stores. First, in the Happy Seat system, passengers input their intended disembarking station through an app. For example, a commuter passenger inputs "I plan to get off at Shinjuku Station." This information is sent to the AI, which analyzes past boarding and alighting data and real-time congestion status to predict which seats will become available. For example, if the AI ​​predicts that "three seats will become available at the next station," this information is displayed in the passenger's app in real time. Next, in the Happy Seat system, giving up a seat is rewarded as points. For example, if passenger A gives up their seat when disembarking, points are awarded within the app. These points can be used to pay for train fares at participating stores and for discounts at participating stores. This fosters a spirit of mutual assistance among passengers and promotes a culture of giving up seats. Furthermore, the Happy Seat system notifies passengers of when seats will become available through real-time seat availability predictions using AI. For example, by receiving a notification that "a seat will become available at the next station," passengers can minimize the time they spend standing unnecessarily. In addition, the Happy Seat system allows passengers to check the current seat status on the app, increasing their sense of security. This mechanism optimizes seat usage on trains during commuting hours and promotes a culture of giving up seats. Passengers can enjoy a comfortable travel experience, and satisfaction with public transport will improve. Moreover, the concept of social mutual assistance will spread, improving people's quality of life. In summary, the Happy Seat system can optimize seat usage on trains during commuting hours and promote a culture of giving up seats.

[0029] The Happy Seat system according to this embodiment comprises a reception unit, a prediction unit, a display unit, a reward unit, and a usage unit. The reception unit inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. The reception unit accepts, for example, input from a passenger saying "I plan to disembark at Shinjuku Station." The reception unit can also accept input of the intended disembarking station using voice input. For example, it accepts input from a passenger saying "I will get off at the next station." Furthermore, the reception unit can also suggest disembarking stations based on past disembarking history. For example, it can automatically display stations that the passenger has frequently disembarked at in the past as candidates. The prediction unit predicts available seats based on the information input by the reception unit. The prediction unit predicts available seats by analyzing, for example, past boarding and alighting data and real-time congestion status. The prediction unit predicts seats that will become available at the next station by analyzing past boarding and alighting data using AI. The prediction unit can also predict available seats based on real-time congestion status. For example, it can predict available seats by analyzing sensor information and camera images. Furthermore, the prediction unit can estimate passengers' emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if a passenger is relaxed, it will perform seat availability predictions at a leisurely pace. The display unit displays the seat availability information predicted by the prediction unit. For example, the display unit displays seat availability information in real time on the app. For example, the display unit notifies passengers' smartphones of seats that will become available at the next station. The display unit can also visually display seat availability information. For example, it displays the location of available seats on a map. Furthermore, the display unit can estimate passengers' emotions and adjust the display method based on those emotions. For example, if a passenger is tense, it provides a simple and highly visible display method. The reward unit rewards the act of giving up a seat as points. For example, the reward unit awards points in the app when a passenger gives up their seat when disembarking. For example, the reward unit awards 10 points for the act of giving up a seat. The reward unit can also estimate passengers' emotions and adjust the point awarding criteria based on those emotions. For example, if a passenger is in a hurry, additional points may be awarded for quick action. The usage unit uses the points awarded by the reward unit.The user unit can, for example, use points to pay for train fares on partner lines. The user unit can, for example, receive a discount on train fares for 100 points. The user unit can also use points for discounts at partner stores. For example, points can be used for shopping at partner stores. Furthermore, the user unit can estimate the passenger's emotions and adjust the way points are used based on those emotions. For example, if the passenger is relaxed, it will suggest the usual way of using points. Thus, the Happy Seat system according to this embodiment can input the passenger's intended disembarking station, predict and display available seats, reward the act of giving up a seat, and allow the use of points.

[0030] The reception desk inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. Specifically, the app's interface is intuitive and easy to use, allowing passengers to easily select their intended disembarking station. For example, they can select their intended disembarking station simply by tapping on a list of station names displayed on the app screen. They can also input their intended disembarking station using voice input. Utilizing voice recognition technology, the system automatically recognizes the station and completes the input simply by the passenger saying, "I'm getting off at the next station." Furthermore, the reception desk can also suggest disembarking stations based on past disembarking history. For example, it can automatically display stations that the passenger has frequently disembarked at in the past as suggestions, and the passenger only needs to select from them. This saves passengers the trouble of entering the same station every time. Through these functions, the reception desk supports passengers in quickly and accurately inputting their intended disembarking station. In addition, the reception desk saves the entered information to a database in real time and collaborates with other departments to improve the overall efficiency of the system. For example, information about the intended disembarking station is immediately shared with the prediction and display units and used for seat availability predictions and information display. This allows the reception unit to enhance passenger convenience while also optimizing the overall system performance.

[0031] The prediction unit predicts available seats based on information entered by the reception unit. For example, the prediction unit analyzes past boarding and alighting data and real-time congestion levels to predict available seats. Specifically, it uses AI to analyze past boarding and alighting data and predict seats that will become available at the next station. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future seat availability with high accuracy. The prediction unit can also predict available seats based on real-time congestion levels. For example, it analyzes sensor information and camera footage to understand the current level of congestion in the train and identify seats that are likely to become available at the next station. Furthermore, the prediction unit can estimate passenger emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if passengers are relaxed, it will perform a more relaxed pace of seat availability prediction. This allows the prediction unit to provide predictions that consider passenger comfort. Through these functions, the prediction unit supports passengers in securing comfortable seats. The prediction unit also updates prediction results in real time, providing predictions based on the latest information. For example, if a passenger's planned disembarking station changes, or if the congestion level inside the train changes rapidly, the prediction unit immediately incorporates the new data and updates the prediction results. This allows the prediction unit to always provide highly accurate predictions based on the latest information, improving passenger convenience.

[0032] The display unit shows seat availability information predicted by the prediction unit. For example, the display unit displays seat availability information in real time on the app. Specifically, the app interface is designed to be visually intuitive, allowing passengers to easily check information about seats available at the next station. For example, it displays the location of seats available at the next station on a map, visually showing passengers which carriage to move to to secure a seat. The display unit can also visually display seat availability information. For example, it can display the location of available seats using different colors, allowing passengers to quickly grasp the location of available seats. Furthermore, the display unit can estimate passenger emotions and adjust the display method based on those emotions. For example, if a passenger is stressed, it provides a simple and highly visible display method to help the passenger quickly understand the information. Through these functions, the display unit supports passengers in comfortably securing seats. The display unit also updates its content in real time, providing the latest information. For example, if new seat availability information is provided by the prediction unit, the display unit immediately reflects that information, providing passengers with the most up-to-date seat availability information. This allows the display unit to provide highly accurate information based on the latest data at all times, improving passenger convenience.

[0033] The rewards unit rewards passengers for giving up their seats in the form of points. For example, when a passenger gives up their seat when disembarking, the rewards unit awards points within the app. Specifically, the system detects when a passenger gives up their seat and automatically awards points. For example, 10 points are awarded for giving up a seat. The rewards unit can also estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, if a passenger is in a hurry, additional points may be awarded for quick action. In this way, the rewards unit can encourage passenger behavior and promote the act of giving up seats. Through these functions, the rewards unit supports passengers in actively giving up their seats. The rewards unit also manages the point awarding status in real time and provides passengers with a highly transparent rewards system. For example, passengers can check their point earning history on the app, allowing them to understand the rewards for their actions. In this way, the rewards unit can encourage passenger behavior and improve the overall usability and fairness of the system.

[0034] The Usage Unit utilizes points awarded by the Rewards Unit. For example, the Usage Unit can use points to pay for train fares at partner stations. Specifically, passengers can use points through the app to receive discounts on train fares. For example, 100 points can be used to receive a discount on train fares. The Usage Unit can also use points for discounts at partner stores. For example, points can be used for shopping at partner stores. Furthermore, the Usage Unit can estimate the passenger's mood and adjust the way points are used based on that mood. For example, if the passenger is relaxed, it will suggest the usual way to use points. In this way, the Usage Unit supports passengers in effectively using points. The Usage Unit also manages point usage in real time and provides passengers with a highly transparent point usage system. For example, it allows passengers to check their point usage history on the app, so that they can understand how they are using their points. In this way, the Usage Unit can improve passenger convenience and increase the reliability of the overall system.

[0035] The prediction unit can predict seat availability by analyzing past boarding and alighting data and real-time congestion status. For example, the prediction unit can predict seat availability based on past boarding and alighting data. For example, the prediction unit can analyze boarding and alighting data from the past year to predict seat availability for specific time slots or days of the week. The prediction unit can also predict seat availability based on real-time congestion status. For example, the prediction unit can analyze sensor information and camera images to understand the current congestion status and predict seat availability. Furthermore, the prediction unit can also predict seat availability by combining past boarding and alighting data with real-time congestion status. For example, the prediction unit can integrate past and current data to make more accurate seat availability predictions. This improves the accuracy of seat availability prediction by analyzing past boarding and alighting data and real-time congestion status. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can predict seat availability using an AI model that takes past boarding and alighting data and real-time congestion status as input and outputs seat availability predictions.

[0036] The display unit can display seat availability information in real time. For example, the display unit can display seat availability information in real time on an app. For example, the display unit can notify passengers' smartphones of seats that will become available at the next station. The display unit can also display seat availability information visually. For example, the display unit can display the location of available seats on a map. Furthermore, the display unit can update seat availability information in real time. For example, the display unit can update seat availability information every minute to provide the latest information. This allows passengers to instantly check seat availability information by displaying it in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display seat availability information using an AI model that acquires and displays seat availability information in real time.

[0037] The rewards unit can award points for the act of giving up a seat. For example, the rewards unit awards points within the app when a passenger gives up their seat upon disembarking. For example, the rewards unit awards 10 points for the act of giving up a seat. The rewards unit can also adjust the points awarded based on the frequency and timing of the act of giving up a seat. For example, the rewards unit awards additional points for giving up a seat during peak hours. Furthermore, the rewards unit can estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, the rewards unit awards additional points for quick action if the passenger is in a hurry. This fosters a spirit of mutual assistance among passengers by awarding points for the act of giving up a seat. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can award points using an AI model that takes data on the act of giving up a seat as input and outputs points.

[0038] The user unit allows users to use the awarded points for affiliated train fares and at participating stores. For example, the user unit can use points to pay for affiliated train fares. For example, the user unit can receive a discount on train fares for 100 points. The user unit can also use points for discounts at affiliated stores. For example, the user unit can use points for shopping at affiliated stores. Furthermore, the user unit can customize how points are used. For example, the user unit can suggest the optimal usage method based on the passenger's current situation. This improves passenger convenience by allowing them to use the awarded points for affiliated train fares and at participating stores. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can use points with an AI model that takes point usage data as input and outputs the optimal usage method.

[0039] The reception desk can analyze a passenger's past disembarkation history and select the optimal input method. For example, the reception desk can automatically display disembarkation stations that the passenger has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the passenger has used in the past. The reception desk can also predict and suggest disembarkation stations to be used during specific time periods based on the passenger's past disembarkation history. In this way, the optimal input method can be provided by analyzing past disembarkation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select an input method using an AI model that takes past disembarkation history data as input and outputs the optimal input method.

[0040] The reception desk can filter passengers based on their current schedule and areas of interest when they input their intended disembarking station. For example, the reception desk can refer to the passenger's calendar information and suggest a disembarking station based on their schedule. For example, the reception desk can suggest stations near relevant events or places based on the passenger's areas of interest. The reception desk can also filter and display the most suitable disembarking station according to the passenger's schedule. This allows for the suggestion of a more appropriate disembarking station by filtering based on the passenger's schedule and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest a disembarking station using an AI model that takes passenger schedule data and areas of interest data as input and outputs the most suitable disembarking station.

[0041] The reception desk can prioritize inputting stations that are highly relevant to the passenger's intended disembarking station, taking into account the passenger's geographical location information. For example, the reception desk can automatically display the station closest to the passenger's current location as a candidate. For example, the reception desk can prioritize displaying stations that are highly relevant based on the passenger's direction of travel. The reception desk can also suggest the optimal disembarking station based on the passenger's geographical location information. This allows for the priority input of highly relevant stations by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest disembarking stations using an AI model that takes the passenger's geographical location information as input and outputs highly relevant stations.

[0042] The reception desk can analyze a passenger's social media activity when they input their intended disembarking station and input relevant stations. For example, the reception desk can suggest stations near places the passenger has checked in to on social media. For example, the reception desk can suggest stations near relevant events or places based on the content of the passenger's social media posts. The reception desk can also suggest stations near places visited by the passenger's social media friends. In this way, relevant stations can be suggested by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest disembarking stations using an AI model that takes the passenger's social media data as input and outputs relevant stations.

[0043] The prediction unit can improve the accuracy of its seat availability predictions based on past boarding and alighting data. For example, the prediction unit can predict seat availability for specific time slots or days of the week based on past boarding and alighting data. For example, the prediction unit can analyze past boarding and alighting data to predict seat availability in order to avoid congestion. The prediction unit can also make the most efficient seat availability prediction based on past boarding and alighting data. By improving the accuracy of predictions based on past boarding and alighting data, more accurate seat availability predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of its predictions by using an AI model that takes past boarding and alighting data as input and outputs seat availability predictions.

[0044] The prediction unit can make seat availability predictions while considering real-time congestion levels. For example, the prediction unit can make optimal seat availability predictions based on real-time congestion levels. For example, the prediction unit can make optimal seat availability predictions while considering real-time public transportation operating conditions. The prediction unit can also suggest alternative routes based on real-time road construction information. This allows for more accurate seat availability predictions by considering real-time congestion levels. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes real-time congestion data as input and outputs seat availability predictions.

[0045] The prediction unit can make seat availability predictions by considering the geographical distribution of passengers. For example, the prediction unit can make the optimal seat availability prediction based on the geographical distribution of passengers. For example, the prediction unit can make highly relevant seat availability predictions based on the direction of passenger movement. The prediction unit can also make the most efficient seat availability prediction based on the geographical distribution of passengers. This makes it possible to make more accurate seat availability predictions by considering the geographical distribution of passengers. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes geographical distribution data of passengers as input and outputs seat availability predictions.

[0046] The prediction unit can improve the accuracy of its predictions by referring to relevant traffic data when predicting seat availability. For example, the prediction unit can make optimal seat availability predictions based on real-time traffic congestion information. For example, the prediction unit can make optimal seat availability predictions by considering the real-time operating status of public transportation. The prediction unit can also suggest detour routes based on real-time road construction information. This makes it possible to make more accurate seat availability predictions by referring to relevant traffic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of its predictions by using an AI model that takes relevant traffic data as input and outputs seat availability predictions.

[0047] The display unit can select the optimal display method when displaying seat availability information by referring to the passenger's past usage history. For example, the display unit may suggest the optimal display method based on the routes the passenger has used in the past. For example, the display unit may suggest a display method that avoids congestion based on the passenger's past usage history. The display unit can also analyze the passenger's past usage history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to past usage history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the passenger's past usage history data as input and outputs the optimal display method.

[0048] The display unit can customize the displayed content based on the passenger's current status when displaying seat availability information. For example, the display unit can display the most suitable seat availability information based on the passenger's current location information. For example, the display unit can display relevant seat availability information based on the passenger's current schedule. The display unit can also display the most suitable seat availability information considering the passenger's current status. This makes it possible to provide more appropriate information by customizing the displayed content based on the current status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes the passenger's current status data as input and customizes the displayed content.

[0049] The display unit can select the optimal display method when displaying seat availability information, taking into account the passenger's device information. For example, if the passenger is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the passenger is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the passenger is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the passenger's device information as input and outputs the optimal display method.

[0050] The display unit can analyze passengers' social media activity and customize the displayed content when showing seat availability information. For example, the display unit can display seat availability information near locations where the passenger has checked in on social media. For example, the display unit can display relevant seat availability information based on the content of the passenger's social media posts. The display unit can also display seat availability information near locations visited by the passenger's social media friends. This allows for the provision of more appropriate displayed content by analyzing social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes passenger social media data as input and customizes the displayed content.

[0051] The rewards unit can analyze a passenger's past behavior history to select the optimal reward method when awarding points. For example, the rewards unit may award additional points if a passenger has frequently given up their seat in the past. For example, the rewards unit may award bonus points for specific actions based on a passenger's past behavior history. The rewards unit can also propose the optimal reward method based on a passenger's past behavior history. In this way, the optimal reward method can be provided by analyzing past behavior history. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can select a reward method using an AI model that takes a passenger's past behavior history data as input and outputs the optimal reward method.

[0052] The rewards unit can customize rewards based on the passenger's current situation when awarding points. For example, the rewards unit can suggest the optimal reward based on the passenger's current location information. For example, the rewards unit can suggest relevant rewards based on the passenger's current schedule. The rewards unit can also suggest the optimal reward considering the passenger's current situation. This allows for the provision of more appropriate rewards by customizing rewards based on the current situation. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can customize rewards using an AI model that takes the passenger's current situation data as input and customizes the rewards.

[0053] The rewards unit can select the optimal reward method when awarding points, taking into account the passenger's geographical location information. For example, the rewards unit may award points that can be used at the nearest partner store to the passenger's current location. For example, the rewards unit may suggest a highly relevant reward method based on the passenger's direction of travel. The rewards unit can also suggest the optimal reward method based on the passenger's geographical location information. In this way, the optimal reward method can be provided by considering geographical location information. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can select a reward method using an AI model that takes the passenger's geographical location information as input and outputs the optimal reward method.

[0054] The rewards department can analyze passengers' social media activity and customize rewards when awarding points. For example, the rewards department can award points that can be used at partner stores near locations where passengers have checked in on social media. For example, the rewards department can suggest relevant rewards based on the content of passengers' social media posts. The rewards department can also award points that can be used at partner stores near locations visited by passengers' social media friends. This allows for the provision of more appropriate rewards by analyzing social media activity. Some or all of the above processing in the rewards department may be performed using AI, for example, or not. For example, the rewards department can customize rewards using an AI model that takes passengers' social media data as input and customizes reward content.

[0055] The user unit can analyze a passenger's past usage history to select the optimal usage method when points are used. For example, the user unit may prioritize suggesting methods that the passenger has frequently used in the past. For example, the user unit may award bonus points for specific usage methods based on the passenger's past usage history. The user unit can also suggest the optimal usage method based on the passenger's past usage history. In this way, the optimal usage method can be provided by analyzing past usage history. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit may select a usage method using an AI model that takes the passenger's past usage history data as input and outputs the optimal usage method.

[0056] The user unit can customize the usage content based on the passenger's current situation when points are used. For example, the user unit can suggest the optimal usage content based on the passenger's current location information. For example, the user unit can suggest relevant usage content based on the passenger's current schedule. The user unit can also suggest the optimal usage content considering the passenger's current situation. This allows for a more appropriate usage method by customizing the usage content based on the current situation. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can use an AI model that takes the passenger's current situation data as input to customize the usage content.

[0057] The user unit can select the optimal usage method when points are used, taking into account the passenger's geographical location information. For example, the user unit may suggest a method of use at the partner store closest to the passenger's current location. For example, the user unit may suggest a highly relevant usage method based on the passenger's direction of travel. The user unit can also suggest the optimal usage method based on the passenger's geographical location information. In this way, the optimal usage method can be provided by considering geographical location information. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can select a usage method using an AI model that takes the passenger's geographical location information as input and outputs the optimal usage method.

[0058] The user interface can analyze a passenger's social media activity and customize the usage content when they redeem points. For example, the user interface can suggest ways to use points at partner stores near locations where the passenger has checked in on social media. For example, the user interface can suggest relevant usage content based on the content of the passenger's social media posts. The user interface can also suggest ways to use points at partner stores near locations visited by the passenger's social media friends. This allows for the provision of more appropriate usage content by analyzing social media activity. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface can use an AI model that takes the passenger's social media data as input to customize the usage content.

[0059] The user unit can select the optimal usage method when a passenger uses points, taking into account their health condition. For example, if a passenger is tired, the user unit may suggest a method that allows them to use the nearest partner store. If a passenger is seeking healthy exercise, the user unit may suggest a method that allows them to use a partner store that is a little further away. Furthermore, if a passenger is feeling unwell, the user unit may suggest a method that allows them to use a partner store that includes a rest point. In this way, the user unit can provide the optimal usage method by taking the passenger's health condition into consideration. Some or all of the above processing in the user unit may be performed using AI, for example, or not using AI. For example, the user unit can select a usage method using an AI model that takes passenger health condition data as input and outputs the optimal usage method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The Happy Seat system can be enhanced with features to monitor passengers' health and encourage seat transfers based on their condition. For example, if a passenger is tired, the system can notify other passengers to give up their seat and award bonus points if the transfer is made. If a passenger is healthy, the system can suggest giving up their seat and display a thank-you message if the transfer is made. Furthermore, if a passenger is feeling unwell, the system can promptly encourage a seat transfer and offer special rewards if it is made. This promotes seat transfers based on passenger health, providing a more comfortable travel experience.

[0062] The Happy Seat system can analyze passengers' past behavior history and add features to encourage seat transfers based on that history. For example, if a passenger has frequently given up their seat in the past, the system can award them additional points. If a passenger has not given up their seat in the past, the system can suggest that they do so and display a thank-you message if they do. Furthermore, if a passenger has given up their seat during a specific time period in the past, the system can encourage seat transfers during that time period and offer special rewards if a transfer is made. This promotes seat transfers based on passengers' past behavior history, providing a more comfortable travel experience.

[0063] The Happy Seat system can be enhanced with features that encourage seat transfers based on passengers' geographical location. For example, if a passenger is alighting at the station nearest their current location, the system will notify other passengers to give up their seat and award bonus points if the transfer is made. It can also suggest that passengers give up their seats based on their direction of travel and display a thank-you message if the transfer is made. Furthermore, if a passenger is in a specific geographical location, the system can encourage a quick seat transfer and offer special rewards if the transfer is made. This promotes seat transfers based on passengers' geographical location, providing a more comfortable travel experience.

[0064] The Happy Seat system can analyze passengers' social media activity and add features to encourage seat transfers based on that activity. For example, if a passenger disembarks at a station near a location they checked in to on social media, the system will notify other passengers to give up their seat and award bonus points if the transfer is made. It can also suggest giving up a seat based on a passenger's social media posts and display a thank-you message if the transfer is made. Furthermore, if a passenger disembarks at a station near a location visited by a friend on social media, the system can encourage a quick seat transfer and offer special rewards if the transfer is made. This promotes seat transfers based on social media activity and provides a more comfortable travel experience.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. The reception desk accepts, for example, a passenger's input of "I intend to disembark at Shinjuku Station." The reception desk can also accept disembarking station input using voice input. For example, a passenger's voice input of "I will get off at the next station" will be accepted. Furthermore, the reception desk can suggest disembarking stations based on past disembarking history. For example, stations that the passenger has frequently disembarked at in the past can be automatically displayed as suggestions. Step 2: The prediction unit predicts available seats based on the information entered by the reception unit. The prediction unit predicts available seats by analyzing, for example, past boarding and alighting data or real-time congestion status. The prediction unit can, for example, use AI to analyze past boarding and alighting data and predict seats that will become available at the next station. The prediction unit can also predict available seats based on real-time congestion status. For example, it can predict available seats by analyzing sensor information or camera footage. Furthermore, the prediction unit can estimate passenger emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if passengers are relaxed, it will perform seat availability prediction at a relaxed pace. Step 3: The display unit displays the seat availability information predicted by the prediction unit. The display unit can, for example, display seat availability information in real time on an app. The display unit can, for example, notify passengers' smartphones of seats that will become available at the next station. The display unit can also visually display seat availability information. For example, it can display the location of available seats on a map. Furthermore, the display unit can estimate the passenger's emotions and adjust the display method based on those emotions. For example, if the passenger is nervous, it can provide a simple and highly visible display method. Step 4: The rewards unit rewards the act of giving up a seat as points. For example, the rewards unit awards points within the app when a passenger gives up their seat upon disembarking. For example, the rewards unit might award 10 points for giving up a seat. The rewards unit can also estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, if the passenger is in a hurry, it might award additional points for quick action. Step 5: The Usage Unit uses the points awarded by the Rewards Unit. For example, the Usage Unit can use points to pay for train fares at partner stations. For example, the Usage Unit can receive a discount on train fares for 100 points. The Usage Unit can also use points for discounts at partner stores. For example, it can use points for shopping at partner stores. Furthermore, the Usage Unit can estimate the passenger's emotions and adjust the way points are used based on those emotions. For example, if the passenger is relaxed, it will suggest the usual way of using points.

[0067] (Example of form 2) The Happy Seat system according to an embodiment of the present invention is a platform for optimizing seat usage on trains during commuting hours and promoting a culture of giving up seats. In the Happy Seat system, passengers input their intended disembarking station through an app, and an AI predicts which seats will become available. Giving up a seat is rewarded as points, which can be used for train fares and at participating stores. First, in the Happy Seat system, passengers input their intended disembarking station through an app. For example, a commuter passenger inputs "I plan to get off at Shinjuku Station." This information is sent to the AI, which analyzes past boarding and alighting data and real-time congestion status to predict which seats will become available. For example, if the AI ​​predicts that "three seats will become available at the next station," this information is displayed in the passenger's app in real time. Next, in the Happy Seat system, giving up a seat is rewarded as points. For example, if passenger A gives up their seat when disembarking, points are awarded within the app. These points can be used to pay for train fares at participating stores and for discounts at participating stores. This fosters a spirit of mutual assistance among passengers and promotes a culture of giving up seats. Furthermore, the Happy Seat system notifies passengers of when seats will become available through real-time seat availability predictions using AI. For example, by receiving a notification that "a seat will become available at the next station," passengers can minimize the time they spend standing unnecessarily. In addition, the Happy Seat system allows passengers to check the current seat status on the app, increasing their sense of security. This mechanism optimizes seat usage on trains during commuting hours and promotes a culture of giving up seats. Passengers can enjoy a comfortable travel experience, and satisfaction with public transport will improve. Moreover, the concept of social mutual assistance will spread, improving people's quality of life. In summary, the Happy Seat system can optimize seat usage on trains during commuting hours and promote a culture of giving up seats.

[0068] The Happy Seat system according to this embodiment comprises a reception unit, a prediction unit, a display unit, a reward unit, and a usage unit. The reception unit inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. The reception unit accepts, for example, input from a passenger saying "I plan to disembark at Shinjuku Station." The reception unit can also accept input of the intended disembarking station using voice input. For example, it accepts input from a passenger saying "I will get off at the next station." Furthermore, the reception unit can also suggest disembarking stations based on past disembarking history. For example, it can automatically display stations that the passenger has frequently disembarked at in the past as candidates. The prediction unit predicts available seats based on the information input by the reception unit. The prediction unit predicts available seats by analyzing, for example, past boarding and alighting data and real-time congestion status. The prediction unit predicts seats that will become available at the next station by analyzing past boarding and alighting data using AI. The prediction unit can also predict available seats based on real-time congestion status. For example, it can predict available seats by analyzing sensor information and camera images. Furthermore, the prediction unit can estimate passengers' emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if a passenger is relaxed, it will perform seat availability predictions at a leisurely pace. The display unit displays the seat availability information predicted by the prediction unit. For example, the display unit displays seat availability information in real time on the app. For example, the display unit notifies passengers' smartphones of seats that will become available at the next station. The display unit can also visually display seat availability information. For example, it displays the location of available seats on a map. Furthermore, the display unit can estimate passengers' emotions and adjust the display method based on those emotions. For example, if a passenger is tense, it provides a simple and highly visible display method. The reward unit rewards the act of giving up a seat as points. For example, the reward unit awards points in the app when a passenger gives up their seat when disembarking. For example, the reward unit awards 10 points for the act of giving up a seat. The reward unit can also estimate passengers' emotions and adjust the point awarding criteria based on those emotions. For example, if a passenger is in a hurry, additional points may be awarded for quick action. The usage unit uses the points awarded by the reward unit.The user unit can, for example, use points to pay for train fares on partner lines. The user unit can, for example, receive a discount on train fares for 100 points. The user unit can also use points for discounts at partner stores. For example, points can be used for shopping at partner stores. Furthermore, the user unit can estimate the passenger's emotions and adjust the way points are used based on those emotions. For example, if the passenger is relaxed, it will suggest the usual way of using points. Thus, the Happy Seat system according to this embodiment can input the passenger's intended disembarking station, predict and display available seats, reward the act of giving up a seat, and allow the use of points.

[0069] The reception desk inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. Specifically, the app's interface is intuitive and easy to use, allowing passengers to easily select their intended disembarking station. For example, they can select their intended disembarking station simply by tapping on a list of station names displayed on the app screen. They can also input their intended disembarking station using voice input. Utilizing voice recognition technology, the system automatically recognizes the station and completes the input simply by the passenger saying, "I'm getting off at the next station." Furthermore, the reception desk can also suggest disembarking stations based on past disembarking history. For example, it can automatically display stations that the passenger has frequently disembarked at in the past as suggestions, and the passenger only needs to select from them. This saves passengers the trouble of entering the same station every time. Through these functions, the reception desk supports passengers in quickly and accurately inputting their intended disembarking station. In addition, the reception desk saves the entered information to a database in real time and collaborates with other departments to improve the overall efficiency of the system. For example, information about the intended disembarking station is immediately shared with the prediction and display units and used for seat availability predictions and information display. This allows the reception unit to enhance passenger convenience while also optimizing the overall system performance.

[0070] The prediction unit predicts available seats based on information entered by the reception unit. For example, the prediction unit analyzes past boarding and alighting data and real-time congestion levels to predict available seats. Specifically, it uses AI to analyze past boarding and alighting data and predict seats that will become available at the next station. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future seat availability with high accuracy. The prediction unit can also predict available seats based on real-time congestion levels. For example, it analyzes sensor information and camera footage to understand the current level of congestion in the train and identify seats that are likely to become available at the next station. Furthermore, the prediction unit can estimate passenger emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if passengers are relaxed, it will perform a more relaxed pace of seat availability prediction. This allows the prediction unit to provide predictions that consider passenger comfort. Through these functions, the prediction unit supports passengers in securing comfortable seats. The prediction unit also updates prediction results in real time, providing predictions based on the latest information. For example, if a passenger's planned disembarking station changes, or if the congestion level inside the train changes rapidly, the prediction unit immediately incorporates the new data and updates the prediction results. This allows the prediction unit to always provide highly accurate predictions based on the latest information, improving passenger convenience.

[0071] The display unit shows seat availability information predicted by the prediction unit. For example, the display unit displays seat availability information in real time on the app. Specifically, the app interface is designed to be visually intuitive, allowing passengers to easily check information about seats available at the next station. For example, it displays the location of seats available at the next station on a map, visually showing passengers which carriage to move to to secure a seat. The display unit can also visually display seat availability information. For example, it can display the location of available seats using different colors, allowing passengers to quickly grasp the location of available seats. Furthermore, the display unit can estimate passenger emotions and adjust the display method based on those emotions. For example, if a passenger is stressed, it provides a simple and highly visible display method to help the passenger quickly understand the information. Through these functions, the display unit supports passengers in comfortably securing seats. The display unit also updates its content in real time, providing the latest information. For example, if new seat availability information is provided by the prediction unit, the display unit immediately reflects that information, providing passengers with the most up-to-date seat availability information. This allows the display unit to provide highly accurate information based on the latest data at all times, improving passenger convenience.

[0072] The rewards unit rewards passengers for giving up their seats in the form of points. For example, when a passenger gives up their seat when disembarking, the rewards unit awards points within the app. Specifically, the system detects when a passenger gives up their seat and automatically awards points. For example, 10 points are awarded for giving up a seat. The rewards unit can also estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, if a passenger is in a hurry, additional points may be awarded for quick action. In this way, the rewards unit can encourage passenger behavior and promote the act of giving up seats. Through these functions, the rewards unit supports passengers in actively giving up their seats. The rewards unit also manages the point awarding status in real time and provides passengers with a highly transparent rewards system. For example, passengers can check their point earning history on the app, allowing them to understand the rewards for their actions. In this way, the rewards unit can encourage passenger behavior and improve the overall usability and fairness of the system.

[0073] The Usage Unit utilizes points awarded by the Rewards Unit. For example, the Usage Unit can use points to pay for train fares at partner stations. Specifically, passengers can use points through the app to receive discounts on train fares. For example, 100 points can be used to receive a discount on train fares. The Usage Unit can also use points for discounts at partner stores. For example, points can be used for shopping at partner stores. Furthermore, the Usage Unit can estimate the passenger's mood and adjust the way points are used based on that mood. For example, if the passenger is relaxed, it will suggest the usual way to use points. In this way, the Usage Unit supports passengers in effectively using points. The Usage Unit also manages point usage in real time and provides passengers with a highly transparent point usage system. For example, it allows passengers to check their point usage history on the app, so that they can understand how they are using their points. In this way, the Usage Unit can improve passenger convenience and increase the reliability of the overall system.

[0074] The prediction unit can predict seat availability by analyzing past boarding and alighting data and real-time congestion status. For example, the prediction unit can predict seat availability based on past boarding and alighting data. For example, the prediction unit can analyze boarding and alighting data from the past year to predict seat availability for specific time slots or days of the week. The prediction unit can also predict seat availability based on real-time congestion status. For example, the prediction unit can analyze sensor information and camera images to understand the current congestion status and predict seat availability. Furthermore, the prediction unit can also predict seat availability by combining past boarding and alighting data with real-time congestion status. For example, the prediction unit can integrate past and current data to make more accurate seat availability predictions. This improves the accuracy of seat availability prediction by analyzing past boarding and alighting data and real-time congestion status. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can predict seat availability using an AI model that takes past boarding and alighting data and real-time congestion status as input and outputs seat availability predictions.

[0075] The display unit can display seat availability information in real time. For example, the display unit can display seat availability information in real time on an app. For example, the display unit can notify passengers' smartphones of seats that will become available at the next station. The display unit can also display seat availability information visually. For example, the display unit can display the location of available seats on a map. Furthermore, the display unit can update seat availability information in real time. For example, the display unit can update seat availability information every minute to provide the latest information. This allows passengers to instantly check seat availability information by displaying it in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display seat availability information using an AI model that acquires and displays seat availability information in real time.

[0076] The rewards unit can award points for the act of giving up a seat. For example, the rewards unit awards points within the app when a passenger gives up their seat upon disembarking. For example, the rewards unit awards 10 points for the act of giving up a seat. The rewards unit can also adjust the points awarded based on the frequency and timing of the act of giving up a seat. For example, the rewards unit awards additional points for giving up a seat during peak hours. Furthermore, the rewards unit can estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, the rewards unit awards additional points for quick action if the passenger is in a hurry. This fosters a spirit of mutual assistance among passengers by awarding points for the act of giving up a seat. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can award points using an AI model that takes data on the act of giving up a seat as input and outputs points.

[0077] The user unit allows users to use the awarded points for affiliated train fares and at participating stores. For example, the user unit can use points to pay for affiliated train fares. For example, the user unit can receive a discount on train fares for 100 points. The user unit can also use points for discounts at affiliated stores. For example, the user unit can use points for shopping at affiliated stores. Furthermore, the user unit can customize how points are used. For example, the user unit can suggest the optimal usage method based on the passenger's current situation. This improves passenger convenience by allowing them to use the awarded points for affiliated train fares and at participating stores. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can use points with an AI model that takes point usage data as input and outputs the optimal usage method.

[0078] The reception desk can estimate the passenger's emotions and adjust the input method for the intended disembarking station based on the estimated emotions. For example, if the passenger is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the passenger is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the passenger is in a hurry, allowing for quick input of the intended disembarking station. This allows for the provision of a more appropriate input method by adjusting the input method according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can take passenger emotion data as input and adjust the input method using an AI model that adjusts the input method.

[0079] The reception desk can analyze a passenger's past disembarkation history and select the optimal input method. For example, the reception desk can automatically display disembarkation stations that the passenger has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the passenger has used in the past. The reception desk can also predict and suggest disembarkation stations to be used during specific time periods based on the passenger's past disembarkation history. In this way, the optimal input method can be provided by analyzing past disembarkation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select an input method using an AI model that takes past disembarkation history data as input and outputs the optimal input method.

[0080] The reception desk can filter passengers based on their current schedule and areas of interest when they input their intended disembarking station. For example, the reception desk can refer to the passenger's calendar information and suggest a disembarking station based on their schedule. For example, the reception desk can suggest stations near relevant events or places based on the passenger's areas of interest. The reception desk can also filter and display the most suitable disembarking station according to the passenger's schedule. This allows for the suggestion of a more appropriate disembarking station by filtering based on the passenger's schedule and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest a disembarking station using an AI model that takes passenger schedule data and areas of interest data as input and outputs the most suitable disembarking station.

[0081] The reception desk can estimate the passenger's emotions and determine the priority of the intended disembarking station based on the estimated emotions. For example, if the passenger is tired, the reception desk will prioritize displaying the nearest disembarking station. If the passenger is relaxed, the reception desk will prioritize displaying stations near tourist attractions or leisure facilities. Furthermore, if the passenger is in a hurry, the reception desk can prioritize displaying the disembarking station along the shortest route. This allows for the suggestion of a more appropriate disembarking station by prioritizing disembarking stations according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can take passenger emotion data as input and determine the priority using an AI model that determines the priority of disembarking stations.

[0082] The reception desk can prioritize inputting stations that are highly relevant to the passenger's intended disembarking station, taking into account the passenger's geographical location information. For example, the reception desk can automatically display the station closest to the passenger's current location as a candidate. For example, the reception desk can prioritize displaying stations that are highly relevant based on the passenger's direction of travel. The reception desk can also suggest the optimal disembarking station based on the passenger's geographical location information. This allows for the priority input of highly relevant stations by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest disembarking stations using an AI model that takes the passenger's geographical location information as input and outputs highly relevant stations.

[0083] The reception desk can analyze a passenger's social media activity when they input their intended disembarking station and input relevant stations. For example, the reception desk can suggest stations near places the passenger has checked in to on social media. For example, the reception desk can suggest stations near relevant events or places based on the content of the passenger's social media posts. The reception desk can also suggest stations near places visited by the passenger's social media friends. In this way, relevant stations can be suggested by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest disembarking stations using an AI model that takes the passenger's social media data as input and outputs relevant stations.

[0084] The prediction unit can estimate passengers' emotions and adjust the seat availability prediction algorithm based on the estimated emotions. For example, if a passenger is relaxed, the prediction unit will make a seat availability prediction that proceeds at a relaxed pace. If a passenger is in a hurry, the prediction unit will make a seat availability prediction that emphasizes the shortest route. Furthermore, if a passenger is excited, the prediction unit can also make a seat availability prediction that adds visually stimulating effects. By adjusting the seat availability prediction algorithm according to passenger emotions, more appropriate seat availability predictions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can take passenger emotion data as input and adjust the seat availability prediction algorithm using an AI model.

[0085] The prediction unit can improve the accuracy of its seat availability predictions based on past boarding and alighting data. For example, the prediction unit can predict seat availability for specific time slots or days of the week based on past boarding and alighting data. For example, the prediction unit can analyze past boarding and alighting data to predict seat availability in order to avoid congestion. The prediction unit can also make the most efficient seat availability prediction based on past boarding and alighting data. By improving the accuracy of predictions based on past boarding and alighting data, more accurate seat availability predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of its predictions by using an AI model that takes past boarding and alighting data as input and outputs seat availability predictions.

[0086] The prediction unit can make seat availability predictions while considering real-time congestion levels. For example, the prediction unit can make optimal seat availability predictions based on real-time congestion levels. For example, the prediction unit can make optimal seat availability predictions while considering real-time public transportation operating conditions. The prediction unit can also suggest alternative routes based on real-time road construction information. This allows for more accurate seat availability predictions by considering real-time congestion levels. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes real-time congestion data as input and outputs seat availability predictions.

[0087] The prediction unit can estimate passengers' emotions and adjust the order in which seat availability predictions are displayed based on the estimated emotions. For example, if a passenger is tense, the prediction unit provides a simple and highly visible display method. For example, if a passenger is relaxed, the prediction unit provides a display method that includes detailed information. The prediction unit can also provide a concise display method if a passenger is in a hurry. This allows for more appropriate information to be provided by adjusting the order in which seat availability predictions are displayed according to passengers' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can adjust the order in which seat availability predictions are displayed using an AI model that takes passenger emotion data as input.

[0088] The prediction unit can make seat availability predictions by considering the geographical distribution of passengers. For example, the prediction unit can make the optimal seat availability prediction based on the geographical distribution of passengers. For example, the prediction unit can make highly relevant seat availability predictions based on the direction of passenger movement. The prediction unit can also make the most efficient seat availability prediction based on the geographical distribution of passengers. This makes it possible to make more accurate seat availability predictions by considering the geographical distribution of passengers. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes geographical distribution data of passengers as input and outputs seat availability predictions.

[0089] The prediction unit can improve the accuracy of its predictions by referring to relevant traffic data when predicting seat availability. For example, the prediction unit can make optimal seat availability predictions based on real-time traffic congestion information. For example, the prediction unit can make optimal seat availability predictions by considering the real-time operating status of public transportation. The prediction unit can also suggest detour routes based on real-time road construction information. This makes it possible to make more accurate seat availability predictions by referring to relevant traffic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of its predictions by using an AI model that takes relevant traffic data as input and outputs seat availability predictions.

[0090] The display unit can estimate the passenger's emotions and adjust the display method of seat availability information based on the estimated emotions. For example, if the passenger is tense, the display unit provides a simple and highly visible display method. For example, if the passenger is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the passenger is in a hurry. By adjusting the display method according to the passenger's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take passenger emotion data as input and adjust the display method using an AI model that adjusts the display method.

[0091] The display unit can select the optimal display method when displaying seat availability information by referring to the passenger's past usage history. For example, the display unit may suggest the optimal display method based on the routes the passenger has used in the past. For example, the display unit may suggest a display method that avoids congestion based on the passenger's past usage history. The display unit can also analyze the passenger's past usage history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to past usage history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the passenger's past usage history data as input and outputs the optimal display method.

[0092] The display unit can customize the displayed content based on the passenger's current status when displaying seat availability information. For example, the display unit can display the most suitable seat availability information based on the passenger's current location information. For example, the display unit can display relevant seat availability information based on the passenger's current schedule. The display unit can also display the most suitable seat availability information considering the passenger's current status. This makes it possible to provide more appropriate information by customizing the displayed content based on the current status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes the passenger's current status data as input and customizes the displayed content.

[0093] The display unit can estimate the passenger's emotions and adjust the display order of seat availability information based on the estimated emotions. For example, if the passenger is tense, the display unit provides a simple and highly visible display method. For example, if the passenger is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the passenger is in a hurry. By adjusting the display order according to the passenger's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take passenger emotion data as input and adjust the order using an AI model that adjusts the display order.

[0094] The display unit can select the optimal display method when displaying seat availability information, taking into account the passenger's device information. For example, if the passenger is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the passenger is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the passenger is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the passenger's device information as input and outputs the optimal display method.

[0095] The display unit can analyze passengers' social media activity and customize the displayed content when showing seat availability information. For example, the display unit can display seat availability information near locations where the passenger has checked in on social media. For example, the display unit can display relevant seat availability information based on the content of the passenger's social media posts. The display unit can also display seat availability information near locations visited by the passenger's social media friends. This allows for the provision of more appropriate displayed content by analyzing social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the displayed content using an AI model that takes passenger social media data as input and customizes the displayed content.

[0096] The rewards unit can estimate the passenger's emotions and adjust the point awarding criteria based on the estimated emotions. For example, if the passenger is relaxed, the rewards unit applies the normal point awarding criteria. If the passenger is in a hurry, the rewards unit awards additional points for quick actions. The rewards unit can also award bonus points for specific actions if the passenger is excited. This allows for more appropriate rewards by adjusting the point awarding criteria according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the rewards unit may be performed using AI or not. For example, the rewards unit can take passenger emotion data as input and adjust the criteria using an AI model that adjusts the point awarding criteria.

[0097] The rewards unit can analyze a passenger's past behavior history to select the optimal reward method when awarding points. For example, the rewards unit may award additional points if a passenger has frequently given up their seat in the past. For example, the rewards unit may award bonus points for specific actions based on a passenger's past behavior history. The rewards unit can also propose the optimal reward method based on a passenger's past behavior history. In this way, the optimal reward method can be provided by analyzing past behavior history. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can select a reward method using an AI model that takes a passenger's past behavior history data as input and outputs the optimal reward method.

[0098] The rewards unit can customize rewards based on the passenger's current situation when awarding points. For example, the rewards unit can suggest the optimal reward based on the passenger's current location information. For example, the rewards unit can suggest relevant rewards based on the passenger's current schedule. The rewards unit can also suggest the optimal reward considering the passenger's current situation. This allows for the provision of more appropriate rewards by customizing rewards based on the current situation. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can customize rewards using an AI model that takes the passenger's current situation data as input and customizes the rewards.

[0099] The reward unit can estimate passengers' emotions and determine priority for point allocation based on the estimated emotions. For example, if a passenger is relaxed, the reward unit applies the normal point allocation criteria. If a passenger is in a hurry, the reward unit awards additional points for quick actions. The reward unit can also award bonus points for specific actions if a passenger is excited. This allows for more appropriate rewards by prioritizing point allocation according to passenger emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reward unit may be performed using AI or not. For example, the reward unit can take passenger emotion data as input and determine priority using an AI model that determines priority for point allocation.

[0100] The rewards unit can select the optimal reward method when awarding points, taking into account the passenger's geographical location information. For example, the rewards unit may award points that can be used at the nearest partner store to the passenger's current location. For example, the rewards unit may suggest a highly relevant reward method based on the passenger's direction of travel. The rewards unit can also suggest the optimal reward method based on the passenger's geographical location information. In this way, the optimal reward method can be provided by considering geographical location information. Some or all of the above processing in the rewards unit may be performed using AI, for example, or without AI. For example, the rewards unit can select a reward method using an AI model that takes the passenger's geographical location information as input and outputs the optimal reward method.

[0101] The rewards department can analyze passengers' social media activity and customize rewards when awarding points. For example, the rewards department can award points that can be used at partner stores near locations where passengers have checked in on social media. For example, the rewards department can suggest relevant rewards based on the content of passengers' social media posts. The rewards department can also award points that can be used at partner stores near locations visited by passengers' social media friends. This allows for the provision of more appropriate rewards by analyzing social media activity. Some or all of the above processing in the rewards department may be performed using AI, for example, or not. For example, the rewards department can customize rewards using an AI model that takes passengers' social media data as input and customizes reward content.

[0102] The user unit can estimate the passenger's emotions and adjust the point redemption method based on the estimated emotions. For example, if the passenger is relaxed, the user unit suggests a normal point redemption method. If the passenger is in a hurry, the user unit suggests a quick redemption method. The user unit can also award bonus points for specific redemption methods if the passenger is excited. This allows for more appropriate redemption methods to be provided by adjusting the point redemption method according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the user unit may be performed using AI or not using AI. For example, the user unit can take passenger emotion data as input and adjust the point redemption method using an AI model.

[0103] The user unit can analyze a passenger's past usage history to select the optimal usage method when points are used. For example, the user unit may prioritize suggesting methods that the passenger has frequently used in the past. For example, the user unit may award bonus points for specific usage methods based on the passenger's past usage history. The user unit can also suggest the optimal usage method based on the passenger's past usage history. In this way, the optimal usage method can be provided by analyzing past usage history. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit may select a usage method using an AI model that takes the passenger's past usage history data as input and outputs the optimal usage method.

[0104] The user unit can customize the usage content based on the passenger's current situation when points are used. For example, the user unit can suggest the optimal usage content based on the passenger's current location information. For example, the user unit can suggest relevant usage content based on the passenger's current schedule. The user unit can also suggest the optimal usage content considering the passenger's current situation. This allows for a more appropriate usage method by customizing the usage content based on the current situation. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can use an AI model that takes the passenger's current situation data as input to customize the usage content.

[0105] The user unit can estimate the passenger's emotions and determine the priority of point usage based on the estimated emotions. For example, if the passenger is relaxed, the user unit suggests a normal way to use points. For example, if the passenger is in a hurry, the user unit suggests a way to use points quickly. The user unit can also award bonus points for specific usage methods if the passenger is excited. This allows for more appropriate usage methods by determining the priority of point usage according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI or not using AI. For example, the user unit can take passenger emotion data as input and determine the priority of point usage using an AI model that determines the priority of point usage.

[0106] The user unit can select the optimal usage method when points are used, taking into account the passenger's geographical location information. For example, the user unit may suggest a method of use at the partner store closest to the passenger's current location. For example, the user unit may suggest a highly relevant usage method based on the passenger's direction of travel. The user unit can also suggest the optimal usage method based on the passenger's geographical location information. In this way, the optimal usage method can be provided by considering geographical location information. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can select a usage method using an AI model that takes the passenger's geographical location information as input and outputs the optimal usage method.

[0107] The user interface can analyze a passenger's social media activity and customize the usage content when they redeem points. For example, the user interface can suggest ways to use points at partner stores near locations where the passenger has checked in on social media. For example, the user interface can suggest relevant usage content based on the content of the passenger's social media posts. The user interface can also suggest ways to use points at partner stores near locations visited by the passenger's social media friends. This allows for the provision of more appropriate usage content by analyzing social media activity. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface can use an AI model that takes the passenger's social media data as input to customize the usage content.

[0108] The user unit can select the optimal usage method when a passenger uses points, taking into account their health condition. For example, if a passenger is tired, the user unit may suggest a method that allows them to use the nearest partner store. If a passenger is seeking healthy exercise, the user unit may suggest a method that allows them to use a partner store that is a little further away. Furthermore, if a passenger is feeling unwell, the user unit may suggest a method that allows them to use a partner store that includes a rest point. In this way, the user unit can provide the optimal usage method by taking the passenger's health condition into consideration. Some or all of the above processing in the user unit may be performed using AI, for example, or not using AI. For example, the user unit can select a usage method using an AI model that takes passenger health condition data as input and outputs the optimal usage method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The Happy Seat system can be enhanced with features that estimate passenger emotions and encourage seat transfers based on those emotions. For example, if a passenger is tired, the system can notify other passengers to give up their seat and award bonus points if the transfer is made. If a passenger is relaxed, the system can suggest that they give up their seat and display a thank-you message if the transfer is made. Furthermore, if a passenger is in a hurry, the system can encourage a quick seat transfer and offer a special reward if the transfer is made. This promotes seat transfers that are tailored to passenger emotions, providing a more comfortable travel experience.

[0111] The Happy Seat system can be enhanced with features to monitor passengers' health and encourage seat transfers based on their condition. For example, if a passenger is tired, the system can notify other passengers to give up their seat and award bonus points if the transfer is made. If a passenger is healthy, the system can suggest giving up their seat and display a thank-you message if the transfer is made. Furthermore, if a passenger is feeling unwell, the system can promptly encourage a seat transfer and offer special rewards if it is made. This promotes seat transfers based on passenger health, providing a more comfortable travel experience.

[0112] The Happy Seat system can analyze passengers' past behavior history and add features to encourage seat transfers based on that history. For example, if a passenger has frequently given up their seat in the past, the system can award them additional points. If a passenger has not given up their seat in the past, the system can suggest that they do so and display a thank-you message if they do. Furthermore, if a passenger has given up their seat during a specific time period in the past, the system can encourage seat transfers during that time period and offer special rewards if a transfer is made. This promotes seat transfers based on passengers' past behavior history, providing a more comfortable travel experience.

[0113] The Happy Seat system can be enhanced with features that encourage seat transfers based on passengers' geographical location. For example, if a passenger is alighting at the station nearest their current location, the system will notify other passengers to give up their seat and award bonus points if the transfer is made. It can also suggest that passengers give up their seats based on their direction of travel and display a thank-you message if the transfer is made. Furthermore, if a passenger is in a specific geographical location, the system can encourage a quick seat transfer and offer special rewards if the transfer is made. This promotes seat transfers based on passengers' geographical location, providing a more comfortable travel experience.

[0114] The Happy Seat system can analyze passengers' social media activity and add features to encourage seat transfers based on that activity. For example, if a passenger disembarks at a station near a location they checked in to on social media, the system will notify other passengers to give up their seat and award bonus points if the transfer is made. It can also suggest giving up a seat based on a passenger's social media posts and display a thank-you message if the transfer is made. Furthermore, if a passenger disembarks at a station near a location visited by a friend on social media, the system can encourage a quick seat transfer and offer special rewards if the transfer is made. This promotes seat transfers based on social media activity and provides a more comfortable travel experience.

[0115] The Happy Seat system can be enhanced with the ability to estimate passengers' emotions and adjust its seat availability prediction algorithm based on those emotions. For example, if a passenger is relaxed, the system will provide a relaxed seat availability prediction. If a passenger is in a hurry, the system can highlight the shortest route. Furthermore, if a passenger is excited, the system can add visually stimulating effects to the seat availability prediction. This enables seat availability predictions that are tailored to passengers' emotions, providing more relevant information.

[0116] The Happy Seat system can be enhanced with a feature that estimates passengers' emotions and adjusts how seat availability information is displayed based on those emotions. For example, if a passenger is stressed, the system can provide a simple and easy-to-read display. If a passenger is relaxed, the system can provide a display that includes more detailed information. Furthermore, if a passenger is in a hurry, the system can provide a concise display. This ensures that the display is tailored to the passenger's emotions, resulting in more appropriate information being provided.

[0117] The Happy Seat system can be enhanced with a feature that estimates passenger emotions and adjusts point-granting criteria based on those emotions. For example, if a passenger is relaxed, the system applies the normal point-granting criteria. If a passenger is in a hurry, the system can award additional points for quick actions. Furthermore, if a passenger is excited, the system can award bonus points for specific actions. This ensures that points are awarded in accordance with passenger emotions, providing more appropriate rewards.

[0118] The Happy Seat system can be enhanced with a feature that estimates passenger emotions and adjusts point usage based on those emotions. For example, if a passenger is relaxed, the system suggests the usual point usage method. If a passenger is in a hurry, the system can suggest a quicker usage method. Furthermore, if a passenger is excited, the system can even award bonus points for specific usage methods. This ensures that point usage is tailored to the passenger's emotions, providing a more appropriate usage method.

[0119] The Happy Seat system can be enhanced with a feature that estimates passenger emotions and adjusts the input method for the destination station based on those emotions. For example, if a passenger is stressed, the system can provide a simple interface and minimize the input steps. If the passenger is relaxed, the system can provide detailed input options and suggest a customizable input method. Furthermore, if a passenger is in a hurry, the system can prioritize voice input to allow for quick entry of the destination station. This provides an input method that is tailored to the passenger's emotions, resulting in more appropriate input.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk inputs the passenger's intended disembarking station. Passengers can input their intended disembarking station, for example, through an app. The reception desk accepts, for example, a passenger's input of "I intend to disembark at Shinjuku Station." The reception desk can also accept disembarking station input using voice input. For example, a passenger's voice input of "I will get off at the next station" will be accepted. Furthermore, the reception desk can suggest disembarking stations based on past disembarking history. For example, stations that the passenger has frequently disembarked at in the past can be automatically displayed as suggestions. Step 2: The prediction unit predicts available seats based on the information entered by the reception unit. The prediction unit predicts available seats by analyzing, for example, past boarding and alighting data or real-time congestion status. The prediction unit can, for example, use AI to analyze past boarding and alighting data and predict seats that will become available at the next station. The prediction unit can also predict available seats based on real-time congestion status. For example, it can predict available seats by analyzing sensor information or camera footage. Furthermore, the prediction unit can estimate passenger emotions and adjust the seat availability prediction algorithm based on those emotions. For example, if passengers are relaxed, it will perform seat availability prediction at a relaxed pace. Step 3: The display unit displays the seat availability information predicted by the prediction unit. The display unit can, for example, display seat availability information in real time on an app. The display unit can, for example, notify passengers' smartphones of seats that will become available at the next station. The display unit can also visually display seat availability information. For example, it can display the location of available seats on a map. Furthermore, the display unit can estimate the passenger's emotions and adjust the display method based on those emotions. For example, if the passenger is nervous, it can provide a simple and highly visible display method. Step 4: The rewards unit rewards the act of giving up a seat as points. For example, the rewards unit awards points within the app when a passenger gives up their seat upon disembarking. For example, the rewards unit might award 10 points for giving up a seat. The rewards unit can also estimate the passenger's emotions and adjust the point awarding criteria based on those emotions. For example, if the passenger is in a hurry, it might award additional points for quick action. Step 5: The Usage Unit uses the points awarded by the Rewards Unit. For example, the Usage Unit can use points to pay for train fares at partner stations. For example, the Usage Unit can receive a discount on train fares for 100 points. The Usage Unit can also use points for discounts at partner stores. For example, it can use points for shopping at partner stores. Furthermore, the Usage Unit can estimate the passenger's emotions and adjust the way points are used based on those emotions. For example, if the passenger is relaxed, it will suggest the usual way of using points.

[0122] 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, prediction unit, display unit, reward unit, and utilization unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts passengers entering their intended disembarking station through an app. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts available seats by analyzing past boarding and alighting data and real-time congestion status. The display unit is implemented, for example, by the output device 40 of the smart device 14 and displays the predicted available seat information in real time. The reward unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and awards points for the act of giving up a seat. The utilization unit is implemented, for example, by the control unit 46A of the smart device 14 and uses the awarded points to pay for affiliated train fares. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, prediction unit, display unit, reward unit, and utilization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the passenger's voice input of their intended disembarking station. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts available seats by analyzing past boarding and alighting data and real-time congestion status. The display unit is implemented, for example, by the speaker 240 of the smart glasses 214 and notifies the passenger of the predicted available seat information by voice. The reward unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and awards points for the act of giving up a seat. The utilization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and uses the awarded points to pay for the train fare of the partner train. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] 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.

[0149] 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.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, prediction unit, display unit, reward unit, and utilization unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the passenger's voice input of their intended disembarking station. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts available seats by analyzing past boarding and alighting data and real-time congestion status. The display unit is implemented, for example, by the display 343 of the headset terminal 314 and visually displays the predicted available seat information. The reward unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and awards points for the act of giving up a seat. The utilization unit is implemented, for example, by the control unit 46A of the headset terminal 314 and uses the awarded points to pay for the train fare of the partner train. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] 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.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] 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.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, prediction unit, display unit, reward unit, and utilization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts passengers' voice input of their intended disembarking station. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts available seats by analyzing past boarding and alighting data and real-time congestion status. The display unit is implemented by, for example, the speaker 240 of the robot 414 and notifies passengers of the predicted available seat information by voice. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and awards points for the act of giving up a seat. The utilization unit is implemented by, for example, the control unit 46A of the robot 414 and uses the awarded points to pay for the train fare. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] 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.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] 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.

[0178] 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.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] 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."

[0181] 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.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A reception area where passengers enter their intended disembarking station, A prediction unit predicts available seats based on the information entered by the reception unit, A display unit that displays the vacancy information predicted by the prediction unit, The reward system rewards the act of giving up a seat in the form of points, The system includes a usage unit that utilizes points awarded by the aforementioned reward unit. A system characterized by the following features. (Note 2) The prediction unit, By analyzing past boarding and alighting data and real-time congestion levels, we predict available seats. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display seat availability in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned compensation unit is, Points will be awarded for the act of giving up one's seat. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned utilization unit is, The points awarded can be used for train fares and at participating stores. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the passenger's emotions and adjusts the input method for the intended disembarking station based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the passenger's past disembarkation history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When passengers enter their intended disembarking station, the system filters the results based on their current schedule and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the passenger's emotions and determines the priority of the intended disembarking stations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering the intended disembarking station, the system prioritizes inputting stations that are highly relevant, taking into account the passenger's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When passengers enter their intended disembarking station, the system analyzes their social media activity and inputs relevant stations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, The system estimates passenger sentiment and adjusts the seat availability prediction algorithm based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, When predicting seat availability, we improve the accuracy of the prediction based on past boarding and alighting data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, When predicting seat availability, the prediction takes into account the real-time congestion status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, The system estimates passenger sentiment and adjusts the order in which seat availability predictions are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, When predicting seat availability, the geographical distribution of passengers is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, When predicting seat availability, we refer to relevant traffic data to improve the accuracy of the prediction. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is The system estimates passenger sentiment and adjusts how seat availability information is displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying seat availability information, the system selects the most suitable display method by referring to the passenger's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying seat availability information, customize the displayed content based on the passenger's current status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is The system estimates passenger sentiment and adjusts the display order of available seats based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying seat availability information, the system selects the optimal display method considering the passenger's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying seat availability information, the system analyzes passengers' social media activity to customize the displayed content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned compensation unit is, The system estimates passenger emotions and adjusts the point awarding criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned compensation unit is, When awarding points, the system analyzes the passenger's past behavioral history to select the most suitable reward method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned compensation unit is, When awarding points, the reward content will be customized based on the passenger's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned compensation unit is, The system estimates passenger emotions and determines the priority for awarding points based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned compensation unit is, When awarding points, the system selects the optimal reward method by considering the passenger's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned compensation unit is, When awarding points, the rewards are customized based on an analysis of passengers' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned utilization unit is, The system estimates passenger emotions and adjusts how points are used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned utilization unit is, When using points, the system analyzes the passenger's past usage history to select the most suitable method of use. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned utilization unit is, When using points, the usage details are customized based on the passenger's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned utilization unit is, The system estimates passenger emotions and determines the priority of point redemption based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned utilization unit is, When using points, the system selects the optimal usage method by considering the passenger's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned utilization unit is, When using points, the system analyzes passengers' social media activity to customize the usage. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned utilization unit is, When using points, the system will select the most appropriate usage method, taking into account the passenger's health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where passengers enter their intended disembarking station, A prediction unit predicts available seats based on the information entered by the reception unit, A display unit that displays the vacancy information predicted by the prediction unit, The reward system rewards the act of giving up a seat in the form of points, The system includes a usage unit that utilizes points awarded by the aforementioned reward unit. A system characterized by the following features.

2. The prediction unit, By analyzing past boarding and alighting data and real-time congestion levels, we predict available seats. The system according to feature 1.

3. The aforementioned display unit is Display seat availability in real time. The system according to feature 1.

4. The aforementioned compensation unit is, Points will be awarded for the act of giving up one's seat. The system according to feature 1.

5. The aforementioned utilization unit is, The points awarded can be used for train fares and at participating stores. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the passenger's emotions and adjusts the input method for the intended disembarking station based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the passenger's past disembarkation history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When passengers enter their intended disembarking station, the system filters the results based on their current schedule and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the passenger's emotions and determines the priority of the intended disembarking stations based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When entering the intended disembarking station, the system prioritizes inputting stations that are highly relevant, taking into account the passenger's geographical location. The system according to feature 1.

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