System

Abstract

An object of a system according to an embodiment is to optimize seat use in a train and promote a culture in which seats are given up.SOLUTION: A system includes a scheduled getting-off station input part, a seat prediction part, and a point giving part. The planned get-off station input part inputs a planned get-off station of the passenger. The seat prediction part predicts the vacancy situation of the seat based on the information of the getting-off scheduled station input by the getting-off scheduled station input part. The point awarding unit awards points to the act of yielding the seat.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology had the problem that seat utilization on trains was not optimized and the culture of sharing seats was not sufficiently promoted.

[0005] The system according to the embodiment aims to optimize seat utilization on trains and promote a culture of sharing seats. [Means for solving the problem]

[0006] The system according to the embodiment includes a planned disembarkation station input unit, a seat prediction unit, and a point granting unit. The planned disembarkation station input unit inputs the planned disembarkation station of the passenger. The seat prediction unit predicts the availability of seats based on the information on the planned disembarkation station input by the planned disembarkation station input unit. The point granting unit grants points for giving up a seat. [Effects of the Invention]

[0007] The system according to the embodiment can optimize seat utilization on trains and promote a culture of sharing seats. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In the seat utilization optimization system according to an embodiment of the present invention, passengers input their planned disembarkation station through an app, and the generation AI notifies them of upcoming vacant seats in real time based on its predictions. Furthermore, seat-giving-up behavior is rewarded in the form of seat-giving-up points, which can be used for discounts on train fares and affiliated services. This allows the seat utilization optimization system to streamline seat utilization on trains and promote a culture of seat-giving-up-together behavior.

[0029] A seat utilization optimization system according to an embodiment includes a planned disembarkation station input unit, a seat prediction unit, and a point assignment unit. The planned disembarkation station input unit inputs the planned disembarkation station of a passenger. For example, a passenger inputs their planned disembarkation station through an app. The seat prediction unit predicts seat availability based on the information about the planned disembarkation station input by the planned disembarkation station input unit. For example, the generation AI analyzes past data and current passenger disembarkation plans to predict which seats will be available at the next station. The point assignment unit assigns points for giving up a seat. For example, when a passenger gives up their seat to another passenger, the action is recorded in the app and points are assigned for giving up a seat. This allows the seat utilization optimization system to streamline seat utilization on trains and promote a culture of sharing seats with others. For example, by inputting the planned disembarkation station, passengers can know in advance which seats will be available at the next station, and giving up their seat is rewarded with points, thereby encouraging cooperation among passengers. In addition, by collaborating with partner services, the scope of use of sharing points will expand, increasing the benefits for passengers.

[0030] The planned alighting station input unit can predict and suggest planned alighting stations based on the passenger's past boarding history. For example, the planned alighting station input unit analyzes the passenger's past boarding history, and the generation AI automatically predicts the next planned alighting station. For example, the next alighting station can be suggested based on stations where passengers have frequently alighted in the past. This reduces the effort required for input by predicting and suggesting planned alighting stations based on the passenger's past boarding history.

[0031] The planned alighting station input unit can automatically detect the planned alighting station using smartphone location information, simplifying input. For example, the planned alighting station input unit obtains the location information of the passenger's smartphone in real time, and the generation AI automatically detects the planned alighting station. For example, when a passenger approaches a specific station, the AI ​​suggests that station as the planned alighting station. This allows the planned alighting station to be automatically detected using smartphone location information, simplifying input.

[0032] The planned alighting station input unit can input the planned alighting station using voice recognition technology, enabling hands-free operation. The planned alighting station input unit can use voice recognition technology to build a system that allows passengers to input the planned alighting station by voice. For example, the planned alighting station can be input simply by the passenger saying, "I'll get off at the next station." This allows the planned alighting station to be input using voice recognition technology, enabling hands-free operation.

[0033] The planned alighting station input unit can add a chat function to promote communication with other passengers. The planned alighting station input unit adds a chat function to communicate with other passengers, for example, when inputting a planned alighting station. For example, passengers can share information about their planned alighting stations. This allows the addition of a chat function to promote communication with other passengers.

[0034] The seat prediction unit can predict seat availability by taking into account external data such as weather or event information. The seat prediction unit builds a system that predicts seat availability by taking into account, for example, weather data. For example, since passenger numbers tend to increase on rainy days, weather data is used when predicting seat availability. This allows for improved prediction accuracy by taking into account external data such as weather and event information when predicting seat availability.

[0035] The seat prediction unit can analyze the congestion status inside the vehicle in real time and predict the availability of seats. For example, the seat prediction unit constructs a system in which a generation AI analyzes the congestion status inside the vehicle in real time and predicts the availability of seats. For example, it analyzes camera footage inside the vehicle to evaluate the degree of congestion. This allows the congestion status inside the vehicle to be analyzed in real time and predicts the availability of seats, improving the accuracy of predictions.

[0036] The seat prediction unit can predict seat availability by taking into account the operation status of other means of transportation. The seat prediction unit, for example, builds a system that predicts seat availability by taking into account the operation status of other means of transportation. For example, the system proposes the optimal means of transportation based on bus and taxi operation information. This makes it possible to improve prediction accuracy by predicting seat availability by taking into account the operation status of other means of transportation.

[0037] The seat prediction unit can give priority to suggesting seats in consideration of the health condition of passengers. The seat prediction unit, for example, builds a system that predicts seat availability in consideration of the health condition of passengers. For example, it gives priority to suggesting seats to passengers who have difficulty walking. In this way, passenger comfort can be improved by giving priority to suggesting seats in consideration of the health condition of passengers.

[0038] The point allocating unit can award not only seat-giving points but also a special badge or title for the act of giving up one's seat. The point allocating unit, for example, builds a system that awards not only seat-giving points but also a special badge or title for the act of giving up one's seat. For example, a passenger who gives up their seat a certain number of times or more can be awarded the title of "seat-giving master." In this way, passengers can be motivated by being awarded not only seat-giving points but also a special badge or title for the act of giving up one's seat.

[0039] The point allocating unit can collect feedback from other passengers, evaluate the act of giving up one's seat, and reflect the evaluation in points. For example, the point allocating unit builds a system for collecting feedback from other passengers and reflecting the feedback in points in order to evaluate the act of giving up one's seat. For example, a passenger who has been given up a seat provides feedback, and the evaluation is reflected in points. In this way, feedback from other passengers is collected, the act of giving up one's seat is evaluated, and the evaluation is reflected in points, thereby making it possible to increase the fairness of evaluation.

[0040] The point allocating unit can provide not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. The point allocating unit builds a system that provides not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat, for example. For example, free tickets are provided to passengers who have earned a certain number of points. In this way, by providing not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat, it is possible to increase the motivation of passengers.

[0041] The point allocating unit can introduce a ranking system in which passengers compete with each other for points. The point allocating unit introduces a ranking system in which passengers compete with each other for points, for example, in order to encourage the act of giving up one's seat. For example, the act of giving up one's seat is evaluated, and a benefit is provided to passengers with high rankings. In this way, by introducing a ranking system in which passengers compete with each other for points, it is possible to encourage the act of giving up one's seat.

[0042] The generation AI can analyze passenger behavior patterns and make seat-sharing suggestions at the optimal timing. The generation AI can, for example, build a system that analyzes passenger behavior patterns and makes seat-sharing suggestions at the optimal timing. For example, the system can make a seat-sharing suggestion when a passenger approaches the station where they plan to disembark. In this way, by using the generation AI to analyze passenger behavior patterns and make seat-sharing suggestions at the optimal timing, it is possible to encourage seat-sharing.

[0043] The generation AI can analyze passengers' past history of giving up their seats and customize rewards. For example, the generation AI can build a system that analyzes passengers' past history of giving up their seats and customize rewards. For example, a special reward can be offered to passengers who have given up many seats in the past. In this way, passengers' motivation can be increased by using the generation AI to analyze passengers' past history of giving up their seats and customize rewards.

[0044] The platform allows passengers to share stories of seat-sharing among themselves. The platform, for example, builds a system for passengers to share stories of seat-sharing among themselves. For example, passengers can post stories of when they gave up their seats and share them with other passengers. This provides a platform for passengers to share stories of seat-sharing among themselves, thereby promoting a culture of seat-sharing.

[0045] Generative AI can link with passengers' social media accounts to share their seat-sharing behavior. For example, generative AI can link with passengers' social media accounts to build a system for sharing seat-sharing behavior. For example, the act of giving up one's seat can be automatically posted on social media. In this way, generative AI can link with passengers' social media accounts to share their seat-sharing behavior, thereby spreading the culture of seat-sharing.

[0046] The generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points. For example, the generation AI can build a system that suggests the most suitable services to passengers based on their usage history of affiliated services. For example, it can suggest related services based on services that have been used in the past. In this way, the generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points, thereby increasing passenger satisfaction.

[0047] Discount coupons for affiliated services can be customized to suit the lifestyles and preferences of passengers. A system can be constructed to customize discount coupons for affiliated services to suit the lifestyles and preferences of passengers. For example, a discount coupon for a fitness gym can be provided to a health-conscious passenger. In this way, passenger satisfaction can be increased by customizing discount coupons for affiliated services to suit the lifestyles and preferences of passengers.

[0048] The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. For example, a system can be built that allows passengers to use their points on online shopping sites. This will expand the range of affiliated services to include online shopping sites and digital content where passengers can use their points, thereby increasing passenger satisfaction.

[0049] It is possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing. It is possible to build a system that monitors the usage of affiliated services in real time and, for example, provides discount coupons to passengers at the optimal timing. For example, a discount coupon is provided just before a passenger uses an affiliated service. In this way, it is possible to increase passenger satisfaction by monitoring the usage of affiliated services in real time and providing discount coupons to passengers at the optimal timing.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The planned alighting station input unit can predict and suggest a planned alighting station based on the passenger's past riding history. For example, it can suggest the next alighting station based on the station where the passenger has frequently alighted in the past. This reduces the effort required for input by predicting and suggesting a planned alighting station based on the passenger's past riding history. It can also analyze data on the time periods and days of the week that passengers have used the service in the past and prioritize suggesting the most frequently used stations. Furthermore, it can record the stations that passengers have used to attend specific events or occasions and suggest those stations when a similar event is approaching.

[0052] The planned alighting station input unit can automatically detect the planned alighting station using the smartphone's location information, simplifying input. For example, when a passenger approaches a specific station, that station is suggested as the planned alighting station. This allows the planned alighting station to be automatically detected using the smartphone's location information, simplifying input. Also, if a passenger frequently uses a specific route, stations on that route can be suggested preferentially. Furthermore, it is possible to calculate the time it will take for the passenger to reach their destination in real time and suggest the optimal alighting station.

[0053] The planned alighting station input unit can input the planned alighting station using voice recognition technology, enabling hands-free operation. For example, the planned alighting station can be input simply by a passenger saying, "I'll get off at the next station." This allows the planned alighting station to be input using voice recognition technology, enabling hands-free operation. In addition, passengers can perform multiple operations at once by speaking specific phrases. Furthermore, voice recognition technology can also be used to simplify the operation when passengers change their planned alighting station.

[0054] The planned disembarking station input unit can add a chat function to promote communication with other passengers. For example, when a planned disembarking station is input, a chat function for communicating with other passengers can be added. This allows the addition of a chat function to promote communication with other passengers. Passengers can also share information about their planned disembarking stations. Furthermore, passengers can also share information about stations they will use to participate in specific events or activities.

[0055] The seat prediction unit can predict seat availability by taking into account external data such as weather or event information. For example, a system can be constructed that predicts seat availability by taking into account weather data. This allows for improved prediction accuracy by taking into account external data such as weather and event information when predicting seat availability. Furthermore, when a specific event is held, the number of passengers attending the event can be predicted and seat availability can be adjusted. Furthermore, it is possible to obtain weather and event information in real time and update the prediction.

[0056] The seat prediction unit can analyze the congestion status inside the vehicle in real time and predict seat availability. For example, a system can be constructed in which the generation AI analyzes the congestion status inside the vehicle in real time and predicts seat availability. This allows for real-time analysis of the congestion status inside the vehicle and prediction of seat availability, improving prediction accuracy. It can also analyze camera footage inside the vehicle to evaluate the degree of congestion. Furthermore, it is possible to analyze passenger movement patterns and identify time periods and sections where congestion is expected.

[0057] The seat prediction unit can predict seat availability by taking into account the operation status of other means of transport. For example, a system can be constructed that predicts seat availability by taking into account the operation status of other means of transport. This allows for improved prediction accuracy by predicting seat availability by taking into account the operation status of other means of transport. It can also suggest the optimal means of transportation based on bus and taxi operation information. Furthermore, it is possible to obtain the operation status of other means of transport in real time and update the prediction.

[0058] The seat prediction unit can give priority to seat suggestions, taking into account the health condition of passengers. For example, a system can be constructed that predicts seat availability, taking into account the health condition of passengers. This makes it possible to improve passenger comfort by giving priority to seat suggestions, taking into account the health condition of passengers. It is also possible to give priority to seat suggestions for passengers who have difficulty walking. Furthermore, it is possible to monitor the health condition of passengers in real time and update seat suggestions as necessary.

[0059] The point allocating unit can award not only seat-giving points but also a special badge or title for the act of giving up one's seat. For example, a system can be constructed that awards not only seat-giving points but also a special badge or title for the act of giving up one's seat. This makes it possible to increase passenger motivation by awarding not only seat-giving points but also a special badge or title for the act of giving up one's seat. It is also possible to award the title of "seat-giving master" to passengers who give up their seats a certain number of times. Furthermore, it is also possible to provide additional benefits to passengers who have special badges or titles.

[0060] The point allocating unit can collect feedback from other passengers, evaluate the act of giving up one's seat, and reflect the results in points. For example, a system can be constructed to collect feedback from other passengers and reflect the feedback in points in order to evaluate the act of giving up one's seat. This allows the fairness of evaluation to be improved by collecting feedback from other passengers, evaluating the act of giving up one's seat, and reflecting the feedback in points. It is also possible to provide advice to improve the quality of the act of giving up one's seat based on the feedback. Furthermore, by collecting feedback in real time and reflecting the feedback in points, rapid evaluation is possible.

[0061] The point allocating unit can provide not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. For example, a system can be constructed that provides not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. This can increase passenger motivation by providing not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. It is also possible to provide free tickets to passengers who have earned a certain number of points. Furthermore, it is possible to increase the types of affiliated services and widen the range of benefits that passengers can choose from.

[0062] The point allocating unit can introduce a ranking system in which passengers compete with each other for points. For example, in order to encourage passengers to give up their seats, a ranking system in which passengers compete with each other for points is introduced. By introducing a ranking system in which passengers compete with each other for points, it is possible to encourage passengers to give up their seats. It is also possible to provide benefits to passengers who rank highly. Furthermore, it is also possible to update the ranking system in real time so that passengers can always check their ranking.

[0063] Generative AI can analyze passenger behavior patterns and make seat-sharing suggestions at the optimal time. For example, we will build a system that analyzes passenger behavior patterns and makes seat-sharing suggestions at the optimal time. This will allow generative AI to analyze passenger behavior patterns and make seat-sharing suggestions at the optimal time, thereby encouraging seat-sharing. It will also be possible to make suggestions to passengers about giving up their seats when they approach their planned disembarkation station. Furthermore, by recording passenger behavior patterns and analyzing them later, it will be possible to make better suggestions.

[0064] The generation AI can analyze passengers' past history of giving up their seats and customize rewards. For example, we can build a system that analyzes passengers' past history of giving up their seats and customize rewards. This allows us to use the generation AI to analyze passengers' past history of giving up their seats and customize rewards, thereby increasing passenger motivation. It can also provide special rewards to passengers who have given up many seats in the past. It is also possible to increase the variety of rewards and broaden the range of benefits that passengers can choose from.

[0065] The platform allows passengers to share their experiences of giving up their seats with each other. For example, a system could be built to share experiences of passengers giving up their seats with each other. This would promote a culture of giving up seats by providing a platform for passengers to share their experiences. Passengers could also post stories of giving up their seats and share them with other passengers. It would also be possible to rate the stories and offer rewards to outstanding stories.

[0066] The generative AI can link with passengers' social media accounts to share their seat-sharing actions. For example, we can build a system that links with passengers' social media accounts to share their seat-sharing actions. This will allow us to spread the culture of seat-sharing by using generative AI to link with passengers' social media accounts and share their seat-sharing actions. It can also automatically post the act of giving up your seat on social media. It can also analyze reactions on social media and suggest more effective ways to share.

[0067] The generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points. For example, we will build a system that suggests the most suitable services to passengers based on their usage history of affiliated services. This will allow the generation AI to suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points, thereby increasing passenger satisfaction. It can also suggest related services based on services used in the past. Furthermore, it is possible to analyze the usage history of affiliated services in real time and suggest the most suitable services.

[0068] Discount coupons for affiliated services can be customized to suit passengers' lifestyles and preferences. For example, a system can be built that customizes discount coupons to suit passengers' lifestyles and preferences. This allows discount coupons for affiliated services to be customized to suit passengers' lifestyles and preferences, thereby increasing passenger satisfaction. Health-conscious passengers can also be offered discount coupons for fitness gyms. Furthermore, passenger preferences can be analyzed in real time to provide the most suitable discount coupons.

[0069] The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. For example, a system can be built that also supports online shopping sites and digital content where passengers can use their points. This will expand the range of affiliated services to include online shopping sites and digital content where passengers can use their points, thereby increasing passenger satisfaction. It will also be possible to allow passengers to use their points on online shopping sites. Furthermore, it will be possible to increase the variety of digital content and broaden the range of benefits that passengers can choose from.

[0070] It is possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing. For example, a system can be built that monitors the usage of affiliated services in real time and provides discount coupons to passengers at the optimal timing. This makes it possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing, thereby increasing passenger satisfaction. It is also possible to provide discount coupons just before passengers use affiliated services. It is also possible to increase the types of discount coupons and broaden the range of benefits that passengers can choose from.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The planned alighting station input unit inputs the planned alighting station of the passenger. For example, the passenger inputs the planned alighting station through the app. Step 2: The seat prediction unit predicts seat availability based on the information about the planned disembarkation station entered by the planned disembarkation station input unit. For example, the generation AI analyzes past data and current passenger disembarkation plans to predict which seats will be available at the next station. Step 3: The point-assigning unit assigns points for the act of giving up one's seat. For example, if a passenger gives up their seat to another passenger, the action is recorded in the app and points are assigned to the passenger.

[0073] (Example 2) In the seat utilization optimization system according to an embodiment of the present invention, passengers input their planned disembarkation station through an app, and the generation AI notifies them of upcoming vacant seats in real time based on its predictions. Furthermore, seat-giving-up behavior is rewarded in the form of seat-giving-up points, which can be used for discounts on train fares and affiliated services. This allows the seat utilization optimization system to streamline seat utilization on trains and promote a culture of seat-giving-up-together behavior.

[0074] A seat utilization optimization system according to an embodiment includes a planned disembarkation station input unit, a seat prediction unit, and a point assignment unit. The planned disembarkation station input unit inputs the planned disembarkation station of a passenger. For example, a passenger inputs their planned disembarkation station through an app. The seat prediction unit predicts seat availability based on the information about the planned disembarkation station input by the planned disembarkation station input unit. For example, the generation AI analyzes past data and current passenger disembarkation plans to predict which seats will be available at the next station. The point assignment unit assigns points for giving up a seat. For example, when a passenger gives up their seat to another passenger, the action is recorded in the app and points are assigned for giving up a seat. This allows the seat utilization optimization system to streamline seat utilization on trains and promote a culture of sharing seats with others. For example, by inputting the planned disembarkation station, passengers can know in advance which seats will be available at the next station, and giving up their seat is rewarded with points, thereby encouraging cooperation among passengers. In addition, by collaborating with partner services, the scope of use of sharing points will expand, increasing the benefits for passengers.

[0075] The planned alighting station input unit can predict and suggest planned alighting stations based on the passenger's past boarding history. For example, the planned alighting station input unit analyzes the passenger's past boarding history, and the generation AI automatically predicts the next planned alighting station. For example, the next alighting station can be suggested based on stations where passengers have frequently alighted in the past. This reduces the effort required for input by predicting and suggesting planned alighting stations based on the passenger's past boarding history.

[0076] The planned alighting station input unit can automatically detect the planned alighting station using smartphone location information, simplifying input. For example, the planned alighting station input unit obtains the location information of the passenger's smartphone in real time, and the generation AI automatically detects the planned alighting station. For example, when a passenger approaches a specific station, the AI ​​suggests that station as the planned alighting station. This allows the planned alighting station to be automatically detected using smartphone location information, simplifying input.

[0077] The planned alighting station input unit can use the emotion estimation function to measure the stress level of a passenger, and if the stress level is high, send a reminder prompting the passenger to input the planned alighting station. The planned alighting station input unit can, for example, use the emotion estimation function to measure the stress level of a passenger in real time, and if the stress level is high, send a reminder prompting the passenger to input the planned alighting station. For example, the stress level can be evaluated by analyzing the passenger's facial expression and voice. This makes it possible to measure the passenger's stress level using the emotion estimation function, and if the stress level is high, send a reminder prompting the passenger to input the planned alighting station.

[0078] The planned alighting station input unit can input the planned alighting station using voice recognition technology, enabling hands-free operation. The planned alighting station input unit can use voice recognition technology to build a system that allows passengers to input the planned alighting station by voice. For example, the planned alighting station can be input simply by the passenger saying, "I'll get off at the next station." This allows the planned alighting station to be input using voice recognition technology, enabling hands-free operation.

[0079] The planned alighting station input unit can add a chat function to promote communication with other passengers. The planned alighting station input unit adds a chat function to communicate with other passengers, for example, when inputting a planned alighting station. For example, passengers can share information about their planned alighting stations. This allows the addition of a chat function to promote communication with other passengers.

[0080] The planned alighting station input unit can use the emotion estimation function to analyze the emotion of the passenger when entering the planned alighting station and provide positive feedback. The planned alighting station input unit can, for example, use the emotion estimation function to analyze the emotion of the passenger when entering the planned alighting station in real time and provide positive feedback. For example, if the passenger is feeling stressed, an encouraging message can be sent. In this way, the emotion estimation function can be used to analyze the emotion of the passenger when entering the planned alighting station and provide positive feedback.

[0081] The seat prediction unit can predict seat availability by taking into account external data such as weather or event information. The seat prediction unit builds a system that predicts seat availability by taking into account, for example, weather data. For example, since passenger numbers tend to increase on rainy days, weather data is used when predicting seat availability. This allows for improved prediction accuracy by taking into account external data such as weather and event information when predicting seat availability.

[0082] The seat prediction unit can analyze the congestion status inside the vehicle in real time and predict the availability of seats. For example, the seat prediction unit constructs a system in which a generation AI analyzes the congestion status inside the vehicle in real time and predicts the availability of seats. For example, it analyzes camera footage inside the vehicle to evaluate the degree of congestion. This allows the congestion status inside the vehicle to be analyzed in real time and predicts the availability of seats, improving the accuracy of predictions.

[0083] The seat prediction unit can use the emotion estimation function to consider the emotional state of the passenger and preferentially suggest seats that are less stressful. The seat prediction unit, for example, uses the emotion estimation function to analyze the emotional state of the passenger in real time and build a system that preferentially suggests seats that are less stressful. For example, it suggests seats where the passenger can relax. In this way, the emotion estimation function can be used to consider the emotional state of the passenger and preferentially suggest seats that are less stressful.

[0084] The seat prediction unit can predict seat availability by taking into account the operation status of other means of transportation. The seat prediction unit, for example, builds a system that predicts seat availability by taking into account the operation status of other means of transportation. For example, the system proposes the optimal means of transportation based on bus and taxi operation information. This makes it possible to improve prediction accuracy by predicting seat availability by taking into account the operation status of other means of transportation.

[0085] The seat prediction unit can give priority to suggesting seats in consideration of the health condition of passengers. The seat prediction unit, for example, builds a system that predicts seat availability in consideration of the health condition of passengers. For example, it gives priority to suggesting seats to passengers who have difficulty walking. In this way, passenger comfort can be improved by giving priority to suggesting seats in consideration of the health condition of passengers.

[0086] The seat prediction unit can use the emotion estimation function to analyze the emotions of passengers when checking seat availability and provide an interface that elicits positive emotions. The seat prediction unit, for example, uses the emotion estimation function to analyze the emotions of passengers when checking seat availability in real time and provide an interface that elicits positive emotions. For example, if a passenger is feeling stressed, an interface that helps them relax can be provided. This makes it possible to use the emotion estimation function to analyze the emotions of passengers when checking seat availability and provide an interface that elicits positive emotions.

[0087] The point allocating unit can award not only seat-giving points but also a special badge or title for the act of giving up one's seat. The point allocating unit, for example, builds a system that awards not only seat-giving points but also a special badge or title for the act of giving up one's seat. For example, a passenger who gives up their seat a certain number of times or more can be awarded the title of "seat-giving master." In this way, passengers can be motivated by being awarded not only seat-giving points but also a special badge or title for the act of giving up one's seat.

[0088] The point allocating unit can collect feedback from other passengers, evaluate the act of giving up one's seat, and reflect the evaluation in points. For example, the point allocating unit builds a system for collecting feedback from other passengers and reflecting the feedback in points in order to evaluate the act of giving up one's seat. For example, a passenger who has been given up a seat provides feedback, and the evaluation is reflected in points. In this way, feedback from other passengers is collected, the act of giving up one's seat is evaluated, and the evaluation is reflected in points, thereby making it possible to increase the fairness of evaluation.

[0089] The point assigning unit can use the emotion estimation function to evaluate the positive emotions that the act of giving up one's seat inspires in other passengers and reflect the evaluation in points. The point assigning unit, for example, uses the emotion estimation function to build a system that evaluates the positive emotions that the act of giving up one's seat inspires in other passengers and reflects the evaluation in points. For example, the emotion of the passenger who has been given up his or her seat is analyzed, and points are added if the positive emotions are high. In this way, passenger satisfaction can be increased by using the emotion estimation function to evaluate the positive emotions that the act of giving up one's seat inspires in other passengers and reflecting the evaluation in points.

[0090] The point allocating unit can provide not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. The point allocating unit builds a system that provides not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat, for example. For example, free tickets are provided to passengers who have earned a certain number of points. In this way, by providing not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat, it is possible to increase the motivation of passengers.

[0091] The point allocating unit can introduce a ranking system in which passengers compete with each other for points. The point allocating unit introduces a ranking system in which passengers compete with each other for points, for example, in order to encourage the act of giving up one's seat. For example, the act of giving up one's seat is evaluated, and a benefit is provided to passengers with high rankings. In this way, by introducing a ranking system in which passengers compete with each other for points, it is possible to encourage the act of giving up one's seat.

[0092] The point assigning unit can use the emotion estimation function to analyze the emotional impact that the act of giving up one's seat has on other passengers and generate a thank-you message. The point assigning unit, for example, uses the emotion estimation function to build a system that analyzes the emotional impact that the act of giving up one's seat has on other passengers and automatically generates a thank-you message. For example, the point assigning unit analyzes the emotions of the passenger who has been given up his or her seat and sends a thank-you message. In this way, passenger satisfaction can be increased by using the emotion estimation function to analyze the emotional impact that the act of giving up one's seat has on other passengers and automatically generating a thank-you message.

[0093] The generation AI can analyze passenger behavior patterns and make seat-sharing suggestions at the optimal timing. The generation AI can, for example, build a system that analyzes passenger behavior patterns and makes seat-sharing suggestions at the optimal timing. For example, the system can make a seat-sharing suggestion when a passenger approaches the station where they plan to disembark. In this way, by using the generation AI to analyze passenger behavior patterns and make seat-sharing suggestions at the optimal timing, it is possible to encourage seat-sharing.

[0094] The generation AI can analyze passengers' past history of giving up their seats and customize rewards. For example, the generation AI can build a system that analyzes passengers' past history of giving up their seats and customize rewards. For example, a special reward can be offered to passengers who have given up many seats in the past. In this way, passengers' motivation can be increased by using the generation AI to analyze passengers' past history of giving up their seats and customize rewards.

[0095] The emotion estimation function can monitor the emotional state of passengers in real time and send messages that elicit positive emotions. The emotion estimation function can, for example, build a system that monitors the emotional state of passengers in real time and sends messages that elicit positive emotions. For example, if a passenger is feeling stressed, an encouraging message can be sent. In this way, passenger satisfaction can be increased by using the emotion estimation function to monitor the emotional state of passengers in real time and sending messages that elicit positive emotions.

[0096] The platform allows passengers to share stories of seat-sharing among themselves. The platform, for example, builds a system for passengers to share stories of seat-sharing among themselves. For example, passengers can post stories of when they gave up their seats and share them with other passengers. This provides a platform for passengers to share stories of seat-sharing among themselves, thereby promoting a culture of seat-sharing.

[0097] Generative AI can link with passengers' social media accounts to share their seat-sharing behavior. For example, generative AI can link with passengers' social media accounts to build a system for sharing seat-sharing behavior. For example, the act of giving up one's seat can be automatically posted on social media. In this way, generative AI can link with passengers' social media accounts to share their seat-sharing behavior, thereby spreading the culture of seat-sharing.

[0098] The emotion estimation function can analyze the emotions felt by passengers when they give up their seats and provide a digital badge expressing gratitude. The emotion estimation function can, for example, build a system that analyzes the emotions felt by passengers when they give up their seats in real time and provides a digital badge expressing gratitude. For example, a digital badge of gratitude can be given to passengers who give up their seats. In this way, passenger satisfaction can be increased by using the emotion estimation function to analyze the emotions felt by passengers when they give up their seats and providing a digital badge expressing gratitude.

[0099] The generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points. For example, the generation AI can build a system that suggests the most suitable services to passengers based on their usage history of affiliated services. For example, it can suggest related services based on services that have been used in the past. In this way, the generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points, thereby increasing passenger satisfaction.

[0100] Discount coupons for affiliated services can be customized to suit the lifestyles and preferences of passengers. A system can be constructed to customize discount coupons for affiliated services to suit the lifestyles and preferences of passengers. For example, a discount coupon for a fitness gym can be provided to a health-conscious passenger. In this way, passenger satisfaction can be increased by customizing discount coupons for affiliated services to suit the lifestyles and preferences of passengers.

[0101] The emotion estimation function can analyze the emotions passengers feel when using affiliated services and prioritize the suggestion of services that will provide a positive experience. For example, the emotion estimation function can analyze the emotions passengers feel when using affiliated services in real time and build a system that prioritizes the suggestion of services that will provide a positive experience. For example, it can suggest services that will help passengers relax. In this way, passenger satisfaction can be increased by using the emotion estimation function to analyze the emotions passengers feel when using affiliated services and prioritize the suggestion of services that will provide a positive experience.

[0102] The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. For example, a system can be built that allows passengers to use their points on online shopping sites. This will expand the range of affiliated services to include online shopping sites and digital content where passengers can use their points, thereby increasing passenger satisfaction.

[0103] It is possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing. It is possible to build a system that monitors the usage of affiliated services in real time and, for example, provides discount coupons to passengers at the optimal timing. For example, a discount coupon is provided just before a passenger uses an affiliated service. In this way, it is possible to increase passenger satisfaction by monitoring the usage of affiliated services in real time and providing discount coupons to passengers at the optimal timing.

[0104] The emotion estimation function can monitor the emotions of passengers when using affiliated services in real time and provide positive feedback. The emotion estimation function can, for example, build a system that monitors the emotions of passengers when using affiliated services in real time and provides positive feedback. For example, it can suggest services that will help passengers relax. In this way, passenger satisfaction can be increased by using the emotion estimation function to monitor the emotions of passengers when using affiliated services in real time and providing positive feedback.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The planned alighting station input unit can predict and suggest a planned alighting station based on the passenger's past riding history. For example, it can suggest the next alighting station based on the station where the passenger has frequently alighted in the past. This reduces the effort required for input by predicting and suggesting a planned alighting station based on the passenger's past riding history. It can also analyze data on the time periods and days of the week that passengers have used the service in the past and prioritize suggesting the most frequently used stations. Furthermore, it can record the stations that passengers have used to attend specific events or occasions and suggest those stations when a similar event is approaching.

[0107] The planned alighting station input unit can automatically detect the planned alighting station using the smartphone's location information, simplifying input. For example, when a passenger approaches a specific station, that station is suggested as the planned alighting station. This allows the planned alighting station to be automatically detected using the smartphone's location information, simplifying input. Also, if a passenger frequently uses a specific route, stations on that route can be suggested preferentially. Furthermore, it is possible to calculate the time it will take for the passenger to reach their destination in real time and suggest the optimal alighting station.

[0108] The planned alighting station input unit can use an emotion estimation function to measure a passenger's stress level and, if stress levels are high, send a reminder prompting the passenger to enter their planned alighting station. For example, the stress level can be assessed by analyzing the passenger's facial expressions and voice. This makes it possible to measure a passenger's stress level using the emotion estimation function and, if stress levels are high, send a reminder prompting the passenger to enter their planned alighting station. In addition, if a passenger feels stressed, it can also provide relaxing music or messages. It can also suggest activities or exercises that passengers can do to reduce stress.

[0109] The planned alighting station input unit can input the planned alighting station using voice recognition technology, enabling hands-free operation. For example, the planned alighting station can be input simply by a passenger saying, "I'll get off at the next station." This allows the planned alighting station to be input using voice recognition technology, enabling hands-free operation. In addition, passengers can perform multiple operations at once by speaking specific phrases. Furthermore, voice recognition technology can also be used to simplify the operation when passengers change their planned alighting station.

[0110] The planned disembarking station input unit can add a chat function to promote communication with other passengers. For example, when a planned disembarking station is input, a chat function for communicating with other passengers can be added. This allows the addition of a chat function to promote communication with other passengers. Passengers can also share information about their planned disembarking stations. Furthermore, passengers can also share information about stations they will use to participate in specific events or activities.

[0111] The planned alighting station input unit can use the emotion estimation function to analyze the emotions of passengers when they input their planned alighting station and provide positive feedback. For example, if a passenger is feeling stressed, an encouraging message can be sent. This allows the emotion estimation function to analyze the emotions of passengers when they input their planned alighting station and provide positive feedback. It can also provide messages or music that help passengers relax. Furthermore, it is possible to provide better service by recording the emotions of passengers when they input their planned alighting station and analyzing them later.

[0112] The seat prediction unit can predict seat availability by taking into account external data such as weather or event information. For example, a system can be constructed that predicts seat availability by taking into account weather data. This allows for improved prediction accuracy by taking into account external data such as weather and event information when predicting seat availability. Furthermore, when a specific event is held, the number of passengers attending the event can be predicted and seat availability can be adjusted. Furthermore, it is possible to obtain weather and event information in real time and update the prediction.

[0113] The seat prediction unit can analyze the congestion status inside the vehicle in real time and predict seat availability. For example, a system can be constructed in which the generation AI analyzes the congestion status inside the vehicle in real time and predicts seat availability. This allows for real-time analysis of the congestion status inside the vehicle and prediction of seat availability, improving prediction accuracy. It can also analyze camera footage inside the vehicle to evaluate the degree of congestion. Furthermore, it is possible to analyze passenger movement patterns and identify time periods and sections where congestion is expected.

[0114] The seat prediction unit can use the emotion estimation function to consider the emotional state of passengers and preferentially suggest seats that are less stressful. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of passengers in real time and preferentially suggest seats that are less stressful. This makes it possible to consider the emotional state of passengers using the emotion estimation function and preferentially suggest seats that are less stressful. It can also suggest seats that allow passengers to relax. Furthermore, it is possible to provide better service by recording the emotional state of passengers and analyzing it later.

[0115] The seat prediction unit can predict seat availability by taking into account the operation status of other means of transport. For example, a system can be constructed that predicts seat availability by taking into account the operation status of other means of transport. This allows for improved prediction accuracy by predicting seat availability by taking into account the operation status of other means of transport. It can also suggest the optimal means of transportation based on bus and taxi operation information. Furthermore, it is possible to obtain the operation status of other means of transport in real time and update the prediction.

[0116] The seat prediction unit can give priority to seat suggestions, taking into account the health condition of passengers. For example, a system can be constructed that predicts seat availability, taking into account the health condition of passengers. This makes it possible to improve passenger comfort by giving priority to seat suggestions, taking into account the health condition of passengers. It is also possible to give priority to seat suggestions for passengers who have difficulty walking. Furthermore, it is possible to monitor the health condition of passengers in real time and update seat suggestions as necessary.

[0117] The seat prediction unit can use the emotion estimation function to analyze the emotions of passengers when checking seat availability and provide an interface that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions of passengers when checking seat availability in real time and provide an interface that elicits positive emotions. This makes it possible to use the emotion estimation function to analyze the emotions of passengers when checking seat availability and provide an interface that elicits positive emotions. It is also possible to provide an interface that allows passengers to relax. Furthermore, it is possible to provide better service by recording the emotional state of passengers and analyzing it later.

[0118] The point allocating unit can award not only seat-giving points but also a special badge or title for the act of giving up one's seat. For example, a system can be constructed that awards not only seat-giving points but also a special badge or title for the act of giving up one's seat. This makes it possible to increase passenger motivation by awarding not only seat-giving points but also a special badge or title for the act of giving up one's seat. It is also possible to award the title of "seat-giving master" to passengers who give up their seats a certain number of times. Furthermore, it is also possible to provide additional benefits to passengers who have special badges or titles.

[0119] The point allocating unit can collect feedback from other passengers, evaluate the act of giving up one's seat, and reflect the results in points. For example, a system can be constructed to collect feedback from other passengers and reflect the feedback in points in order to evaluate the act of giving up one's seat. This allows the fairness of evaluation to be improved by collecting feedback from other passengers, evaluating the act of giving up one's seat, and reflecting the feedback in points. It is also possible to provide advice to improve the quality of the act of giving up one's seat based on the feedback. Furthermore, by collecting feedback in real time and reflecting the feedback in points, rapid evaluation is possible.

[0120] The point-assigning unit can use the emotion estimation function to evaluate the positive emotions that the act of giving up one's seat inspires in other passengers and reflect the evaluation in points. For example, a system can be constructed that uses the emotion estimation function to evaluate the positive emotions that the act of giving up one's seat inspires in other passengers and reflects the evaluation in points. This can increase passenger satisfaction by using the emotion estimation function to evaluate the positive emotions that the act of giving up one's seat inspires in other passengers and reflecting the evaluation in points. It is also possible to analyze the emotions of passengers who have been given their seats and add points if the positive emotions are high. Furthermore, it is also possible to use the emotion estimation function to provide advice for improving the quality of seat-giving behavior.

[0121] The point allocating unit can provide not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. For example, a system can be constructed that provides not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. This can increase passenger motivation by providing not only points but also free tickets to affiliated fitness gyms or movie theaters for giving up one's seat. It is also possible to provide free tickets to passengers who have earned a certain number of points. Furthermore, it is possible to increase the types of affiliated services and widen the range of benefits that passengers can choose from.

[0122] The point allocating unit can introduce a ranking system in which passengers compete with each other for points. For example, in order to encourage passengers to give up their seats, a ranking system in which passengers compete with each other for points is introduced. By introducing a ranking system in which passengers compete with each other for points, it is possible to encourage passengers to give up their seats. It is also possible to provide benefits to passengers who rank highly. Furthermore, it is also possible to update the ranking system in real time so that passengers can always check their ranking.

[0123] The point-granting unit can use the emotion estimation function to analyze the emotional impact that the act of giving up one's seat has on other passengers and generate a thank-you message. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional impact that the act of giving up one's seat has on other passengers and automatically generate a thank-you message. This makes it possible to increase passenger satisfaction by using the emotion estimation function to analyze the emotional impact that the act of giving up one's seat has on other passengers and automatically generating a thank-you message. The thank-you message can also be personalized to make it more moving. Furthermore, it is possible to spread awareness of the importance of giving up one's seat by sharing the thank-you message with other passengers.

[0124] Generative AI can analyze passenger behavior patterns and make seat-sharing suggestions at the optimal time. For example, we will build a system that analyzes passenger behavior patterns and makes seat-sharing suggestions at the optimal time. This will allow generative AI to analyze passenger behavior patterns and make seat-sharing suggestions at the optimal time, thereby encouraging seat-sharing. It will also be possible to make suggestions to passengers about giving up their seats when they approach their planned disembarkation station. Furthermore, by recording passenger behavior patterns and analyzing them later, it will be possible to make better suggestions.

[0125] The generation AI can analyze passengers' past history of giving up their seats and customize rewards. For example, we can build a system that analyzes passengers' past history of giving up their seats and customize rewards. This allows us to use the generation AI to analyze passengers' past history of giving up their seats and customize rewards, thereby increasing passenger motivation. It can also provide special rewards to passengers who have given up many seats in the past. It is also possible to increase the variety of rewards and broaden the range of benefits that passengers can choose from.

[0126] The emotion estimation function can monitor passengers' emotional states in real time and send messages that elicit positive emotions. For example, we will build a system that monitors passengers' emotional states in real time and sends messages that elicit positive emotions. This will increase passenger satisfaction by using the emotion estimation function to monitor passengers' emotional states in real time and sending messages that elicit positive emotions. It will also be possible to send encouraging messages to passengers if they are feeling stressed. Furthermore, passengers' emotional states can be recorded and later analyzed to provide better service.

[0127] The platform allows passengers to share their experiences of giving up their seats with each other. For example, a system could be built to share experiences of passengers giving up their seats with each other. This would promote a culture of giving up seats by providing a platform for passengers to share their experiences. Passengers could also post stories of giving up their seats and share them with other passengers. It would also be possible to rate the stories and offer rewards to outstanding stories.

[0128] The generative AI can link with passengers' social media accounts to share their seat-sharing actions. For example, we can build a system that links with passengers' social media accounts to share their seat-sharing actions. This will allow us to spread the culture of seat-sharing by using generative AI to link with passengers' social media accounts and share their seat-sharing actions. It can also automatically post the act of giving up your seat on social media. It can also analyze reactions on social media and suggest more effective ways to share.

[0129] The emotion estimation function can analyze the emotions felt by passengers when they give up their seats and provide them with a digital badge expressing gratitude. For example, we will build a system that analyzes the emotions felt by passengers when they give up their seats in real time and provides a digital badge expressing gratitude. This will increase passenger satisfaction by using the emotion estimation function to analyze the emotions felt by passengers when they give up their seats and providing a digital badge expressing gratitude. It will also be possible to award a digital badge of gratitude to passengers who give up their seats. Furthermore, by sharing the digital badge with other passengers, it will be possible to spread awareness of the importance of giving up your seat.

[0130] The generation AI can suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points. For example, we will build a system that suggests the most suitable services to passengers based on their usage history of affiliated services. This will allow the generation AI to suggest the most suitable services to passengers based on their usage history of affiliated services and encourage them to use points, thereby increasing passenger satisfaction. It can also suggest related services based on services used in the past. Furthermore, it is possible to analyze the usage history of affiliated services in real time and suggest the most suitable services.

[0131] Discount coupons for affiliated services can be customized to suit passengers' lifestyles and preferences. For example, a system can be built that customizes discount coupons to suit passengers' lifestyles and preferences. This allows discount coupons for affiliated services to be customized to suit passengers' lifestyles and preferences, thereby increasing passenger satisfaction. Health-conscious passengers can also be offered discount coupons for fitness gyms. Furthermore, passenger preferences can be analyzed in real time to provide the most suitable discount coupons.

[0132] The emotion estimation function can analyze the emotions passengers feel when using affiliated services and prioritize the suggestion of services that provide a positive experience. For example, a system can be built that analyzes the emotions passengers feel when using affiliated services in real time and prioritizes the suggestion of services that provide a positive experience. This can increase passenger satisfaction by using the emotion estimation function to analyze the emotions passengers feel when using affiliated services and prioritize the suggestion of services that provide a positive experience. It can also suggest services that help passengers relax. Furthermore, by recording passengers' emotional states and analyzing them later, it is possible to provide better services.

[0133] The range of affiliated services can be expanded to include online shopping sites and digital content where passengers can use their points. For example, a system can be built that also supports online shopping sites and digital content where passengers can use their points. This will expand the range of affiliated services to include online shopping sites and digital content where passengers can use their points, thereby increasing passenger satisfaction. It will also be possible to allow passengers to use their points on online shopping sites. Furthermore, it will be possible to increase the variety of digital content and broaden the range of benefits that passengers can choose from.

[0134] It is possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing. For example, a system can be built that monitors the usage of affiliated services in real time and provides discount coupons to passengers at the optimal timing. This makes it possible to monitor the usage of affiliated services in real time and provide discount coupons to passengers at the optimal timing, thereby increasing passenger satisfaction. It is also possible to provide discount coupons just before passengers use affiliated services. It is also possible to increase the types of discount coupons and broaden the range of benefits that passengers can choose from.

[0135] The emotion estimation function can monitor passengers' emotions in real time when using affiliated services and provide positive feedback. For example, a system can be built that monitors passengers' emotions in real time when using affiliated services and provides positive feedback. This can increase passenger satisfaction by using the emotion estimation function to monitor passengers' emotions in real time when using affiliated services and providing positive feedback. It can also suggest services that will help passengers relax. Furthermore, passengers' emotional states can be recorded and later analyzed to provide better services.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The planned alighting station input unit inputs the planned alighting station of the passenger. For example, the passenger inputs the planned alighting station through the app. Step 2: The seat prediction unit predicts seat availability based on the information about the planned disembarkation station entered by the planned disembarkation station input unit. For example, the generation AI analyzes past data and current passenger disembarkation plans to predict which seats will be available at the next station. Step 3: The point-assigning unit assigns points for the act of giving up one's seat. For example, if a passenger gives up their seat to another passenger, the action is recorded in the app and points are assigned to the passenger.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0158] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0166] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0167] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0182] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0183] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a planned alighting station input section for inputting a planned alighting station of a passenger; a seat prediction unit that predicts seat availability based on information about the planned alighting station input by the planned alighting station input unit; and a point granting unit that grants points for the act of giving up a seat. A system characterized by:

2. The planned alighting station input unit The system automatically detects the intended disembarking station using the location information of the passenger's smartphone, simplifying input.

2. The system of claim 1.

3. The seat prediction unit Taking into account external data such as weather or event information, predict the availability of said seats.

2. The system of claim 1.

4. The point giving unit In addition to the above-mentioned sharing points, a special badge or title is awarded for the act of giving up one's seat.

2. The system of claim 1.

5. The generating AI is Analyze the passengers' behavioral patterns and make the concession proposal at the optimal time.

2. The system of claim 1.

6. The planned alighting station input unit Measure the stress level of the passenger, and if the stress level is high, send a reminder to input the planned disembarkation station.

2. The system of claim 1.

7. The seat prediction unit Taking into account the emotional state of the passenger, preferentially recommend low-stress seats 2. The system of claim 1.

8. The point giving unit The positive feelings that the act of giving up one's seat gives to other passengers are evaluated and reflected in the points.

2. The system of claim 1.

Images