System

Abstract

To provide a system that integrates ticket purchasing and payment across a plurality of transportation operators, and improves convenience for users.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a payment unit. The reception unit receives input of a departure place and a destination. The generation unit uses generative AI to generate directions from a current location to the destination based on information received by the reception unit. The provision unit provides the user with ticket information generated by the generation unit. The payment unit manages monthly fees from users and platform usage fees from transportation operators.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] Conventional technology has the problem that ticket purchases and payments from multiple transportation operators are distributed, making it cumbersome for users.

[0005] The system according to the embodiment aims to improve convenience for users by integrating ticket purchases and payments from multiple transportation operators. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a payment unit. The reception unit receives input of a departure point and a destination. The generation unit uses a generation AI to generate directions from the current location to the destination based on the information received by the reception unit. The provision unit provides the user with the ticket information generated by the generation unit. The payment unit manages monthly fees from users and platform usage fees from transportation operators. [Effects of the Invention]

[0007] The system according to the embodiment can integrate ticket purchases and payments from multiple transportation operators, thereby improving convenience for users. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A transportation service system according to an embodiment of the present invention allows users to ride public transportation services, such as trains, buses, and taxis, within a city, as much as they like, simply by pressing a button within the app. In this system, a generation AI embeds prompts within the app and instantly purchases tickets from various transportation operators on the user's behalf. This allows for the pseudo-integration of fares, which previously were paid individually to transportation operators. For example, when a user presses a button within the app, the generation AI generates a prompt and purchases tickets from various transportation operators. For example, if a user intends to use both a train and a bus, the generation AI purchases a combined train ticket and a bus ticket and provides them to the user. The generation AI then displays the purchased ticket information within the app, and the user boards the train or bus based on that information. For example, users can board a train or bus by scanning a two-dimensional code (e.g., a QR code (registered trademark)) displayed within the app. Furthermore, the user pays a monthly fee to the service provider, and each transportation operator pays a platform usage fee to the service provider. This allows users to use multiple transportation services through a single app, while also consolidating payments. This service improves convenience by allowing users to easily use multiple modes of transport and centralizing payments. It also increases revenue for transport operators by collecting platform usage fees. This allows the transport service system to allow users to use multiple modes of transport with the push of a button in the app and centralize payments.

[0029] A transportation service system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a payment unit. The reception unit accepts input of a departure point and a destination from a user. For example, the user can enter the departure point and the destination within an app. The reception unit can also acquire a current location using, for example, GPS information or Wi-Fi location information. The generation unit uses a generation AI to generate directions from the current location to the destination based on the information accepted by the reception unit. The generation AI calculates an optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. The provision unit provides the user with ticket information generated by the generation unit. For example, the provision unit displays the ticket information within an app, allowing the user to board by scanning a two-dimensional code. The provision unit can generate, for example, a two-dimensional code or a barcode and provide it to the user. The payment unit manages monthly fees from users and platform usage fees from transportation operators. The payment unit can manage payment methods, for example, credit cards, debit cards, and electronic money. The payment unit can, for example, automatically deduct the user's monthly fee and pay the platform usage fee to the transportation company. This allows the transportation service system according to the embodiment to allow the user to use multiple transportation services with a single click of a button in the app, and also allows for centralized payment.

[0030] The reception unit accepts the user's input of a departure point and destination. For example, the user can enter the departure point and destination within the app. The reception unit can also obtain the user's current location using, for example, GPS information or Wi-Fi location information. Specifically, when the user launches the app, fields for entering the departure point and destination are displayed on the interface. The user can enter text in these fields or select the departure point and destination on a map. The reception unit also has a function for automatically obtaining the user's current location, using GPS or Wi-Fi location information to determine the user's exact current location. This saves the user the trouble of manually entering the departure point. The reception unit processes the information entered by the user in real time and transmits it to the generation unit. For example, when the user enters the departure point and destination, the reception unit instantly analyzes the information and forwards it to the generation unit. The reception unit also stores the user's past usage history and favorite places and can suggest possible departure points and destinations based on this information. This allows the user to quickly and easily set their destination. The reception unit also has a function for detecting input errors by the user and suggesting appropriate corrections. For example, if a user mistakenly enters a place name that does not exist, the reception unit will suggest the nearest candidate and ask the user for confirmation, allowing the user to enter accurate information and improving the accuracy of the entire system.

[0031] The generation unit uses a generation AI to generate directions from the current location to the destination based on the information received by the reception unit. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. Specifically, the generation AI receives information about the user's current location and destination as input and generates multiple route options. This takes into account real-time data such as traffic conditions, road congestion, and public transportation schedules. The generation AI analyzes this data and identifies the most efficient route. For example, the generation AI can not only calculate the shortest route from the current location to the destination, but also suggest the cheapest route to minimize transportation costs. Furthermore, the generation AI can learn the user's past travel history and preferences and provide individually optimized routes. For example, if the user has preferred a particular mode of transportation in the past, the generation AI can take that information into account when proposing a route. The generation AI can also consider the user's desired travel time and arrival time and suggest optimal departure times and transfer points. This allows the generation unit to provide the user with the most efficient and comfortable travel experience. Furthermore, the generation unit can update the generated route information in real time to respond to changes in traffic conditions. For example, if a traffic jam or accident occurs, the generation unit will immediately calculate a new route and notify the user. This allows the user to always travel based on the latest information, allowing for a stress-free travel experience.

[0032] The providing unit provides the user with the ticket information generated by the generating unit. For example, the providing unit may display the ticket information within the app and allow the user to board by scanning a 2D code. The providing unit may generate, for example, a 2D code or barcode and provide it to the user. Specifically, the providing unit generates tickets for the transportation means the user will use based on the route information received from the generating unit. This includes tickets for trains, buses, taxis, etc. The providing unit displays this ticket information within the app so that the user can easily access it. For example, when the user opens the app, a 2D code or barcode is displayed on the screen, and the user can board the transportation means by holding it over a reader at the transportation means. Furthermore, the providing unit can also send the ticket information to the user via email or SMS. This allows the user to check the ticket information without opening the app. The providing unit also clearly displays the ticket expiration date and terms of use to prevent the user from accidentally using an invalid ticket. For example, when the ticket expiration date is approaching, a notification may be displayed within the app to alert the user. This allows the user to use the ticket with peace of mind. Furthermore, the service provider can collect user feedback and continuously improve the ticket provision process. For example, when users provide opinions on how tickets are displayed and used, the service provider can use this information to improve the interface and functions and create a more user-friendly system. This allows the service provider to provide users with ticket information quickly and accurately, supporting a comfortable travel experience.

[0033] The payment unit manages monthly fees from users and platform usage fees from transportation operators. The payment unit can manage payment methods, such as credit cards, debit cards, and electronic money. For example, the payment unit can automatically debit users' monthly fees and pay platform usage fees to transportation operators. Specifically, the payment unit securely manages payment information registered by users within the app and automatically debits monthly fees. This includes credit cards, debit cards, electronic money, and even debits from bank accounts. The payment unit centrally manages these payment methods and ensures smooth payment via the method selected by the user. Furthermore, the payment unit records detailed payment history and allows users to view it at any time. For example, users can view their past payment history within the app and check each month's usage details. This makes it easier for users to understand their spending and improves transparency. The payment unit also has the function of appropriately distributing platform usage fees to transportation operators. For example, the payment unit calculates platform usage fees based on each transportation operator's usage status and makes periodic payments. This allows transportation operators to ensure stable revenue. Furthermore, the Payment Department also has support functions to quickly respond to payment-related problems and inquiries. For example, when a user reports a payment problem, the Payment Department immediately investigates and takes appropriate action. This allows users to use the service with peace of mind. Through these functions, the Payment Department provides reliable payment management for both users and transportation operators, ensuring smooth operation of the entire system.

[0034] The generation unit can generate prompts using the generation AI and combine the best tickets. For example, the generation unit can generate prompts using the generation AI and combine the best tickets. For example, the generation AI can select the best ticket based on information input by a user. For example, the generation AI can select the best means of transportation based on the departure and destination input by the user. For example, the generation AI can combine train tickets and bus tickets and provide them to the user. The generation AI can select the best ticket based on criteria such as price, convenience, and travel time. The generation AI can select the best ticket using, for example, a machine learning model or a deep learning algorithm. This enables the generation unit to combine the best tickets using the generation AI.

[0035] The providing unit may display the generated ticket information within the app, allowing the user to board by scanning the two-dimensional code. The providing unit may, for example, display the generated ticket information within the app. The providing unit may, for example, generate a two-dimensional code or a barcode and provide it to the user. The user may, for example, board a train or bus by scanning a two-dimensional code displayed within the app. The providing unit may, for example, clarify how to generate and use the two-dimensional code. For example, the two-dimensional code may encode specific information, and the information may be obtained by scanning it. The providing unit may, for example, use an algorithm for generating the two-dimensional code. The providing unit may, for example, provide a device for scanning the two-dimensional code. In this way, the providing unit may allow the user to easily board a train or bus by scanning the two-dimensional code.

[0036] The payment unit manages monthly fees from users and can manage platform usage fees from transportation operators. The payment unit, for example, manages monthly fees from users. The payment unit can manage payment methods such as credit cards, debit cards, and electronic money. The payment unit, for example, can automatically debit the user's monthly fee. The payment unit, for example, manages platform usage fees from transportation operators. The payment unit, for example, can pay the platform usage fee to the transportation operator. The payment unit, for example, can manage fees and monthly fees from transportation operators. This allows the payment unit to centralize fare management between users and transportation operators.

[0037] The reception unit can receive information on the user's current location and destination. For example, the reception unit allows the user to input the information on the current location and destination within the app. For example, the reception unit can acquire the current location using GPS information or Wi-Fi location information. For example, the reception unit can acquire the destination information based on an address or landmark input by the user. For example, the reception unit allows the user to input the information on the current location and destination using voice input. This allows the reception unit to accurately receive the information on the user's current location and destination.

[0038] The generation unit can calculate the best route based on information about the user's current location and destination, and generate a ticket along that route. The generation unit, for example, uses a generation AI to calculate the best route based on information about the user's current location and destination. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. The generation unit, for example, selects the optimal means of transportation based on the user's current location and destination. The generation unit can provide the user with a combination of train tickets and bus tickets. The generation unit calculates the best route based on criteria such as price, convenience, and travel time. The generation unit can calculate the optimal route using, for example, a machine learning model or a deep learning algorithm. This allows the generation unit to provide the optimal route based on the user's current location and destination.

[0039] The reception unit can analyze the user's past travel history and present the best candidates for departure and destination. The reception unit, for example, analyzes the user's past travel history. The reception unit, for example, automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit, for example, predicts places that the user will visit on specific days of the week or during specific time periods and proposes them as candidate locations. The reception unit, for example, analyzes the user's past travel patterns and proposes the best candidate locations. The reception unit can acquire the past travel history based on, for example, GPS logs or transportation usage history. This allows the reception unit to present the best candidate locations based on the user's past travel history.

[0040] When inputting a departure point and a destination, the reception unit can complement the input by taking into account the user's current traffic conditions and weather information. The reception unit complements the input by taking into account, for example, the user's current traffic conditions and weather information. The reception unit suggests an optimal departure point and destination based on, for example, current traffic congestion information. The reception unit prioritizes and suggests covered routes and underground passages by taking into account, for example, current weather information. The reception unit suggests an optimal departure point and destination by taking into account, for example, the current operating status of public transportation. The reception unit can acquire traffic conditions based on, for example, real-time traffic information and congestion information. The reception unit can acquire weather information based on, for example, meteorological data and weather forecasts. This allows the reception unit to provide optimal input by taking into account the current traffic conditions and weather information.

[0041] When a departure point and a destination are input, the reception unit can prioritize presenting highly relevant candidates taking into consideration the user's geographical location information. For example, the reception unit prioritizes presenting highly relevant candidates taking into consideration the user's geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. For example, the reception unit prioritizes displaying highly relevant candidate locations based on the user's past travel history. For example, the reception unit prioritizes displaying optimal candidate locations taking into consideration the user's current traffic conditions. The reception unit can acquire geographical location information based on, for example, GPS information or Wi-Fi location information. This allows the reception unit to present optimal candidate locations based on the user's geographical location information.

[0042] The reception unit can analyze the user's social media activity when the departure point and destination are input, and present relevant candidate locations. The reception unit, for example, analyzes the user's social media activity. The reception unit suggests relevant candidate locations based on the user's check-in information on social media, for example. The reception unit, for example, analyzes the content of the user's social media posts and presents relevant candidate locations. The reception unit, for example, refers to the activity of the user's friends on social media and presents relevant candidate locations. The reception unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the reception unit to present optimal candidate locations based on the user's social media activity.

[0043] At the time of generation, the generation unit can predict the best route by referring to past movement data. The generation unit, for example, predicts the optimal route by referring to past movement data. The generation unit, for example, predicts the optimal route based on routes that the user has used in the past. The generation unit, for example, predicts a route that avoids congestion based on the user's past movement history. The generation unit, for example, analyzes the user's past movement history and predicts the most efficient route. The generation unit can acquire past movement data based on, for example, GPS logs or public transportation usage history. This allows the generation unit to provide the optimal route based on the past movement data.

[0044] The generation unit can optimize the route by reflecting traffic conditions and weather information in real time when generating the route. The generation unit, for example, optimizes the route by reflecting traffic conditions and weather information in real time. The generation unit, for example, proposes an optimal route based on real-time traffic congestion information. The generation unit, for example, proposes an optimal route taking into account the real-time operation status of public transportation. The generation unit, for example, proposes an optimal route based on real-time weather information. The generation unit, for example, can acquire traffic conditions based on real-time traffic information and congestion information. The generation unit, for example, can acquire weather information based on meteorological data and weather forecasts. This allows the generation unit to provide an optimal route based on real-time information.

[0045] The generation unit can generate the best route by taking into account the user's geographical location information at the time of generation. The generation unit generates the optimal route by taking into account, for example, the user's geographical location information. The generation unit generates the optimal route based on, for example, the user's current location. The generation unit generates the optimal route from, for example, the user's past movement history. The generation unit generates the optimal route by taking into account, for example, the user's current traffic conditions. The generation unit can acquire geographical location information based on, for example, GPS information or Wi-Fi location information. This allows the generation unit to provide the optimal route based on the user's geographical location information.

[0046] At the time of generation, the generation unit can analyze the user's social media activity and generate a related route. The generation unit, for example, analyzes the user's social media activity. The generation unit generates a related route based on the user's check-in information on social media, for example. The generation unit, for example, analyzes the content of the user's posts on social media and generates a related route. The generation unit, for example, generates a related route by referring to the activity of the user's friends on social media. The generation unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the generation unit to provide an optimal route based on the user's social media activity.

[0047] At the time of providing the display information, the providing unit can select the best display method by referring to the user's past usage history. The providing unit, for example, selects the best display method by referring to the user's past usage history. The providing unit, for example, selects the best display method based on display methods used by the user in the past. The providing unit, for example, selects a display method with high visibility from the user's past usage history. The providing unit, for example, analyzes the user's past usage history and selects the most efficient display method. The providing unit can acquire the past usage history based on, for example, app usage history or transportation usage history. This allows the providing unit to provide the best display method based on the user's past usage history.

[0048] The providing unit can complement the display content when providing the display content by taking into account the user's current traffic conditions and weather information. The providing unit complements the display content by taking into account, for example, the user's current traffic conditions and weather information. The providing unit provides optimal display content based on, for example, current traffic congestion information. The providing unit provides optimal display content by taking into account, for example, current weather information. The providing unit provides optimal display content by taking into account, for example, the current operation status of public transportation. The providing unit can acquire traffic conditions based on, for example, real-time traffic information and congestion information. The providing unit can acquire weather information based on, for example, weather data and weather forecasts. This allows the providing unit to provide optimal display content based on the current traffic conditions and weather information.

[0049] When providing the information, the providing unit can prioritize displaying the best ticket information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying highly relevant ticket information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying ticket information close to the user's current location. For example, the providing unit prioritizes displaying highly relevant ticket information based on the user's past movement history. For example, the providing unit prioritizes displaying optimal ticket information by taking into account the user's current traffic conditions. For example, the providing unit can acquire geographical location information based on GPS information or Wi-Fi location information. This allows the providing unit to provide optimal ticket information based on the user's geographical location information.

[0050] The providing unit can analyze the user's social media activity and display related ticket information at the time of providing. The providing unit, for example, analyzes the user's social media activity. The providing unit displays related ticket information based on the user's check-in information on social media, for example. The providing unit, for example, analyzes the content of the user's posts on social media and displays related ticket information. The providing unit displays related ticket information based on the activity of the user's friends on social media, for example. The providing unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the providing unit to provide optimal ticket information based on the user's social media activity.

[0051] At the time of payment, the payment unit can select the best payment method by referring to the user's past payment history. The payment unit, for example, selects the optimal payment method by referring to the user's past payment history. The payment unit, for example, suggests the optimal payment method based on payment methods used by the user in the past. The payment unit, for example, prioritizes and displays the most frequently used payment method from the user's past payment history. The payment unit, for example, analyzes the user's past payment history and suggests the most efficient payment method. The payment unit can acquire past payment history based on, for example, credit card usage history or electronic money usage history. This allows the payment unit to provide the optimal payment method based on the user's past payment history.

[0052] At the time of payment, the payment unit can propose the best payment plan taking into account the user's current economic situation. The payment unit proposes a payment plan taking into account, for example, the user's current economic situation. The payment unit proposes the best payment plan based on, for example, the user's current bank balance. The payment unit proposes the best payment plan taking into account, for example, the user's credit card usage. The payment unit proposes the best payment plan based on, for example, the balance between the user's income and expenses. The payment unit can acquire the user's economic situation based on, for example, income information, expense information, and credit score. This allows the payment unit to provide the best payment plan according to the user's economic situation.

[0053] The payment unit can propose the best payment method at the time of payment, taking into account the user's geographical location information. The payment unit proposes the best payment method, for example, taking into account the user's geographical location information. The payment unit proposes the best payment method, for example, based on the user's current location. The payment unit proposes the best payment method, for example, based on the user's past payment history. The payment unit proposes the best payment method, for example, taking into account the user's current economic situation. The payment unit can acquire geographical location information, for example, based on GPS information or Wi-Fi location information. This allows the payment unit to provide the best payment method based on the user's geographical location information.

[0054] The payment unit can analyze the user's social media activity at the time of payment and suggest a relevant payment method. The payment unit, for example, analyzes the user's social media activity. The payment unit can suggest a relevant payment method based on the user's social media check-in information, for example. The payment unit can analyze the content of the user's social media posts and suggest a relevant payment method. The payment unit can suggest a relevant payment method based on the activity of the user's friends on social media, for example. The payment unit can acquire social media activity based on the content of posts, the number of likes, the number of followers, etc. This allows the payment unit to provide the optimal payment method based on the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can also monitor the user's health condition and adjust the input method for the departure point and destination based on the health condition. For example, if the user is tired, a simple interface is provided to minimize input steps. If the user is in good health, detailed input options are provided and a customizable input method is suggested. If the user is in a hurry, voice input is prioritized to enable the user to quickly input the departure point and destination. In this way, the reception unit can provide an input method according to the user's health condition.

[0057] The generation unit can also predict the optimal route based on the user's past movement history. For example, it can prioritize and suggest routes that the user has frequently used in the past. It can predict routes that the user will use on specific days of the week or time periods and present them as candidates. It can analyze the user's past movement patterns and suggest the most efficient route. This allows the generation unit to provide the optimal route based on the user's past movement history.

[0058] The providing unit can also adjust the display method of ticket information taking into account the user's current traffic conditions and weather information. For example, the providing unit provides optimal display content based on current traffic congestion information. The providing unit provides optimal display content taking into account current weather information. The providing unit provides optimal display content taking into account the current operation status of public transportation. This allows the providing unit to provide optimal display content based on current traffic conditions and weather information.

[0059] The payment unit can also propose an optimal payment plan taking into account the user's current financial situation. For example, the payment unit can propose an optimal payment plan based on the user's current bank balance, or the payment unit can propose an optimal payment plan taking into account the user's credit card usage, or the payment unit can propose an optimal payment plan based on the balance between the user's income and expenses. In this way, the payment unit can provide the optimal payment plan according to the user's financial situation.

[0060] The reception unit can also analyze the user's social media activity and present related candidate locations. For example, the reception unit can suggest related candidate locations based on the user's check-in information on social media. The reception unit can suggest related candidate locations by analyzing the content of the user's social media posts. The reception unit can suggest related candidate locations based on the activity of the user's friends on social media. This allows the reception unit to present optimal candidate locations based on the user's social media activity.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives input of the user's departure point and destination. For example, the user can input the departure point and destination within the app. The reception unit can also obtain the user's current location using, for example, GPS information or Wi-Fi location information. Step 2: The generation unit uses a generation AI to generate directions from the current location to the destination based on the information received by the reception unit. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. Step 3: The providing unit provides the ticket information generated by the generating unit to the user. For example, the providing unit displays the ticket information in the app and allows the user to board the train by scanning the two-dimensional code. For example, the providing unit can generate a two-dimensional code or a barcode and provide it to the user. Step 4: The payment unit manages the monthly fee from the user and the platform usage fee from the transportation company. The payment unit can manage payment methods such as credit cards, debit cards, and electronic money. For example, the payment unit can automatically debit the monthly fee from the user and pay the platform usage fee to the transportation company.

[0063] (Example 2) A transportation service system according to an embodiment of the present invention allows users to ride public transportation services, such as trains, buses, and taxis, within a city, as much as they like, simply by pressing a button within the app. In this system, a generation AI embeds prompts within the app and instantly purchases tickets from various transportation operators on the user's behalf. This allows for the pseudo-integration of fares, which previously were paid individually to transportation operators. For example, when a user presses a button within the app, the generation AI generates a prompt and purchases tickets from various transportation operators. For example, if a user intends to use both a train and a bus, the generation AI purchases a combined train ticket and a bus ticket and provides them to the user. The generation AI then displays the purchased ticket information within the app, and the user boards the train or bus based on that information. For example, users can board a train or bus by scanning a two-dimensional code (e.g., a QR code) displayed within the app. Furthermore, the user pays a monthly fee to the service provider, and each transportation operator pays a platform usage fee to the service provider. This allows users to use multiple transportation services through a single app, while also consolidating payments. This service improves convenience by allowing users to easily use multiple modes of transport and centralizing payments. It also increases revenue for transport operators by collecting platform usage fees. This allows the transport service system to allow users to use multiple modes of transport with the push of a button in the app and centralize payments.

[0064] A transportation service system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a payment unit. The reception unit accepts input of a departure point and a destination from a user. For example, the user can enter the departure point and the destination within an app. The reception unit can also acquire a current location using, for example, GPS information or Wi-Fi location information. The generation unit uses a generation AI to generate directions from the current location to the destination based on the information accepted by the reception unit. The generation AI calculates an optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. The provision unit provides the user with ticket information generated by the generation unit. For example, the provision unit displays the ticket information within an app, allowing the user to board by scanning a two-dimensional code. The provision unit can generate, for example, a two-dimensional code or a barcode and provide it to the user. The payment unit manages monthly fees from users and platform usage fees from transportation operators. The payment unit can manage payment methods, for example, credit cards, debit cards, and electronic money. The payment unit can, for example, automatically deduct the user's monthly fee and pay the platform usage fee to the transportation company. This allows the transportation service system according to the embodiment to allow the user to use multiple transportation services with a single click of a button in the app, and also allows for centralized payment.

[0065] The reception unit accepts the user's input of a departure point and destination. For example, the user can enter the departure point and destination within the app. The reception unit can also obtain the user's current location using, for example, GPS information or Wi-Fi location information. Specifically, when the user launches the app, fields for entering the departure point and destination are displayed on the interface. The user can enter text in these fields or select the departure point and destination on a map. The reception unit also has a function for automatically obtaining the user's current location, using GPS or Wi-Fi location information to determine the user's exact current location. This saves the user the trouble of manually entering the departure point. The reception unit processes the information entered by the user in real time and transmits it to the generation unit. For example, when the user enters the departure point and destination, the reception unit instantly analyzes the information and forwards it to the generation unit. The reception unit also stores the user's past usage history and favorite places and can suggest possible departure points and destinations based on this information. This allows the user to quickly and easily set their destination. The reception unit also has a function for detecting input errors by the user and suggesting appropriate corrections. For example, if a user mistakenly enters a place name that does not exist, the reception unit will suggest the nearest candidate and ask the user for confirmation, allowing the user to enter accurate information and improving the accuracy of the entire system.

[0066] The generation unit uses a generation AI to generate directions from the current location to the destination based on the information received by the reception unit. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. Specifically, the generation AI receives information about the user's current location and destination as input and generates multiple route options. This takes into account real-time data such as traffic conditions, road congestion, and public transportation schedules. The generation AI analyzes this data and identifies the most efficient route. For example, the generation AI can not only calculate the shortest route from the current location to the destination, but also suggest the cheapest route to minimize transportation costs. Furthermore, the generation AI can learn the user's past travel history and preferences and provide individually optimized routes. For example, if the user has preferred a particular mode of transportation in the past, the generation AI can take that information into account when proposing a route. The generation AI can also consider the user's desired travel time and arrival time and suggest optimal departure times and transfer points. This allows the generation unit to provide the user with the most efficient and comfortable travel experience. Furthermore, the generation unit can update the generated route information in real time to respond to changes in traffic conditions. For example, if a traffic jam or accident occurs, the generation unit will immediately calculate a new route and notify the user. This allows the user to always travel based on the latest information, allowing for a stress-free travel experience.

[0067] The providing unit provides the user with the ticket information generated by the generating unit. For example, the providing unit may display the ticket information within the app and allow the user to board by scanning a 2D code. The providing unit may generate, for example, a 2D code or barcode and provide it to the user. Specifically, the providing unit generates tickets for the transportation means the user will use based on the route information received from the generating unit. This includes tickets for trains, buses, taxis, etc. The providing unit displays this ticket information within the app so that the user can easily access it. For example, when the user opens the app, a 2D code or barcode is displayed on the screen, and the user can board the transportation means by holding it over a reader at the transportation means. Furthermore, the providing unit can also send the ticket information to the user via email or SMS. This allows the user to check the ticket information without opening the app. The providing unit also clearly displays the ticket expiration date and terms of use to prevent the user from accidentally using an invalid ticket. For example, when the ticket expiration date is approaching, a notification may be displayed within the app to alert the user. This allows the user to use the ticket with peace of mind. Furthermore, the service provider can collect user feedback and continuously improve the ticket provision process. For example, when users provide opinions on how tickets are displayed and used, the service provider can use this information to improve the interface and functions and create a more user-friendly system. This allows the service provider to provide users with ticket information quickly and accurately, supporting a comfortable travel experience.

[0068] The payment unit manages monthly fees from users and platform usage fees from transportation operators. The payment unit can manage payment methods, such as credit cards, debit cards, and electronic money. For example, the payment unit can automatically debit users' monthly fees and pay platform usage fees to transportation operators. Specifically, the payment unit securely manages payment information registered by users within the app and automatically debits monthly fees. This includes credit cards, debit cards, electronic money, and even debits from bank accounts. The payment unit centrally manages these payment methods and ensures smooth payment via the method selected by the user. Furthermore, the payment unit records detailed payment history and allows users to view it at any time. For example, users can view their past payment history within the app and check each month's usage details. This makes it easier for users to understand their spending and improves transparency. The payment unit also has the function of appropriately distributing platform usage fees to transportation operators. For example, the payment unit calculates platform usage fees based on each transportation operator's usage status and makes periodic payments. This allows transportation operators to ensure stable revenue. Furthermore, the Payment Department also has support functions to quickly respond to payment-related problems and inquiries. For example, when a user reports a payment problem, the Payment Department immediately investigates and takes appropriate action. This allows users to use the service with peace of mind. Through these functions, the Payment Department provides reliable payment management for both users and transportation operators, ensuring smooth operation of the entire system.

[0069] The generation unit can generate prompts using the generation AI and combine the best tickets. For example, the generation unit can generate prompts using the generation AI and combine the best tickets. For example, the generation AI can select the best ticket based on information input by a user. For example, the generation AI can select the best means of transportation based on the departure and destination input by the user. For example, the generation AI can combine train tickets and bus tickets and provide them to the user. The generation AI can select the best ticket based on criteria such as price, convenience, and travel time. The generation AI can select the best ticket using, for example, a machine learning model or a deep learning algorithm. This enables the generation unit to combine the best tickets using the generation AI.

[0070] The providing unit may display the generated ticket information within the app, allowing the user to board by scanning the two-dimensional code. The providing unit may, for example, display the generated ticket information within the app. The providing unit may, for example, generate a two-dimensional code or a barcode and provide it to the user. The user may, for example, board a train or bus by scanning a two-dimensional code displayed within the app. The providing unit may, for example, clarify how to generate and use the two-dimensional code. For example, the two-dimensional code may encode specific information, and the information may be obtained by scanning it. The providing unit may, for example, use an algorithm for generating the two-dimensional code. The providing unit may, for example, provide a device for scanning the two-dimensional code. In this way, the providing unit may allow the user to easily board a train or bus by scanning the two-dimensional code.

[0071] The payment unit manages monthly fees from users and can manage platform usage fees from transportation operators. The payment unit, for example, manages monthly fees from users. The payment unit can manage payment methods such as credit cards, debit cards, and electronic money. The payment unit, for example, can automatically debit the user's monthly fee. The payment unit, for example, manages platform usage fees from transportation operators. The payment unit, for example, can pay the platform usage fee to the transportation operator. The payment unit, for example, can manage fees and monthly fees from transportation operators. This allows the payment unit to centralize fare management between users and transportation operators.

[0072] The reception unit can receive information on the user's current location and destination. For example, the reception unit allows the user to input the information on the current location and destination within the app. For example, the reception unit can acquire the current location using GPS information or Wi-Fi location information. For example, the reception unit can acquire the destination information based on an address or landmark input by the user. For example, the reception unit allows the user to input the information on the current location and destination using voice input. This allows the reception unit to accurately receive the information on the user's current location and destination.

[0073] The generation unit can calculate the best route based on information about the user's current location and destination, and generate a ticket along that route. The generation unit, for example, uses a generation AI to calculate the best route based on information about the user's current location and destination. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. The generation unit, for example, selects the optimal means of transportation based on the user's current location and destination. The generation unit can provide the user with a combination of train tickets and bus tickets. The generation unit calculates the best route based on criteria such as price, convenience, and travel time. The generation unit can calculate the optimal route using, for example, a machine learning model or a deep learning algorithm. This allows the generation unit to provide the optimal route based on the user's current location and destination.

[0074] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly input the departure point and destination. This allows the reception unit to provide an input method that corresponds to the user's emotions.

[0075] The reception unit can analyze the user's past travel history and present the best candidates for departure and destination. The reception unit, for example, analyzes the user's past travel history. The reception unit, for example, automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit, for example, predicts places that the user will visit on specific days of the week or during specific time periods and proposes them as candidate locations. The reception unit, for example, analyzes the user's past travel patterns and proposes the best candidate locations. The reception unit can acquire the past travel history based on, for example, GPS logs or transportation usage history. This allows the reception unit to present the best candidate locations based on the user's past travel history.

[0076] When inputting a departure point and a destination, the reception unit can complement the input by taking into account the user's current traffic conditions and weather information. The reception unit complements the input by taking into account, for example, the user's current traffic conditions and weather information. The reception unit suggests an optimal departure point and destination based on, for example, current traffic congestion information. The reception unit prioritizes and suggests covered routes and underground passages by taking into account, for example, current weather information. The reception unit suggests an optimal departure point and destination by taking into account, for example, the current operating status of public transportation. The reception unit can acquire traffic conditions based on, for example, real-time traffic information and congestion information. The reception unit can acquire weather information based on, for example, meteorological data and weather forecasts. This allows the reception unit to provide optimal input by taking into account the current traffic conditions and weather information.

[0077] The reception unit can estimate the user's emotions and determine the best input priority based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is nervous, the reception unit prioritizes displaying important input items. For example, if the user is relaxed, the reception unit provides detailed input options. For example, if the user is in a hurry, the reception unit displays the most important input items first. This allows the reception unit to provide input priority according to the user's emotions.

[0078] When a departure point and a destination are input, the reception unit can prioritize presenting highly relevant candidates taking into consideration the user's geographical location information. For example, the reception unit prioritizes presenting highly relevant candidates taking into consideration the user's geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. For example, the reception unit prioritizes displaying highly relevant candidate locations based on the user's past travel history. For example, the reception unit prioritizes displaying optimal candidate locations taking into consideration the user's current traffic conditions. The reception unit can acquire geographical location information based on, for example, GPS information or Wi-Fi location information. This allows the reception unit to present optimal candidate locations based on the user's geographical location information.

[0079] The reception unit can analyze the user's social media activity when the departure point and destination are input, and present relevant candidate locations. The reception unit, for example, analyzes the user's social media activity. The reception unit suggests relevant candidate locations based on the user's check-in information on social media, for example. The reception unit, for example, analyzes the content of the user's social media posts and presents relevant candidate locations. The reception unit, for example, refers to the activity of the user's friends on social media and presents relevant candidate locations. The reception unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the reception unit to present optimal candidate locations based on the user's social media activity.

[0080] The generation unit can estimate the user's emotions and adjust the optimal route generation method based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is relaxed, the generation unit generates a scenic route. For example, if the user is in a hurry, the generation unit generates the shortest route. For example, if the user is excited, the generation unit generates a visually stimulating route. This allows the generation unit to provide an optimal route according to the user's emotions.

[0081] At the time of generation, the generation unit can predict the best route by referring to past movement data. The generation unit, for example, predicts the optimal route by referring to past movement data. The generation unit, for example, predicts the optimal route based on routes that the user has used in the past. The generation unit, for example, predicts a route that avoids congestion based on the user's past movement history. The generation unit, for example, analyzes the user's past movement history and predicts the most efficient route. The generation unit can acquire past movement data based on, for example, GPS logs or public transportation usage history. This allows the generation unit to provide the optimal route based on the past movement data.

[0082] The generation unit can optimize the route by reflecting traffic conditions and weather information in real time when generating the route. The generation unit, for example, optimizes the route by reflecting traffic conditions and weather information in real time. The generation unit, for example, proposes an optimal route based on real-time traffic congestion information. The generation unit, for example, proposes an optimal route taking into account the real-time operation status of public transportation. The generation unit, for example, proposes an optimal route based on real-time weather information. The generation unit, for example, can acquire traffic conditions based on real-time traffic information and congestion information. The generation unit, for example, can acquire weather information based on meteorological data and weather forecasts. This allows the generation unit to provide an optimal route based on real-time information.

[0083] The generation unit can estimate the user's emotions and determine route priorities based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is nervous, the generation unit prioritizes the safest route. For example, if the user is relaxed, the generation unit prioritizes a scenic route. For example, if the user is in a hurry, the generation unit prioritizes the shortest route. This allows the generation unit to provide route priorities according to the user's emotions.

[0084] The generation unit can generate the best route by taking into account the user's geographical location information at the time of generation. The generation unit generates the optimal route by taking into account, for example, the user's geographical location information. The generation unit generates the optimal route based on, for example, the user's current location. The generation unit generates the optimal route from, for example, the user's past movement history. The generation unit generates the optimal route by taking into account, for example, the user's current traffic conditions. The generation unit can acquire geographical location information based on, for example, GPS information or Wi-Fi location information. This allows the generation unit to provide the optimal route based on the user's geographical location information.

[0085] At the time of generation, the generation unit can analyze the user's social media activity and generate a related route. The generation unit, for example, analyzes the user's social media activity. The generation unit generates a related route based on the user's check-in information on social media, for example. The generation unit, for example, analyzes the content of the user's posts on social media and generates a related route. The generation unit, for example, generates a related route by referring to the activity of the user's friends on social media. The generation unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the generation unit to provide an optimal route based on the user's social media activity.

[0086] The providing unit can estimate the user's emotions and adjust the display method of ticket information based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, if the user is relaxed, the providing unit provides a display method including detailed information. For example, if the user is in a hurry, the providing unit provides a display method that focuses on the main points. This allows the providing unit to provide a display method that corresponds to the user's emotions.

[0087] At the time of providing the display information, the providing unit can select the best display method by referring to the user's past usage history. The providing unit, for example, selects the best display method by referring to the user's past usage history. The providing unit, for example, selects the best display method based on display methods used by the user in the past. The providing unit, for example, selects a display method with high visibility from the user's past usage history. The providing unit, for example, analyzes the user's past usage history and selects the most efficient display method. The providing unit can acquire the past usage history based on, for example, app usage history or transportation usage history. This allows the providing unit to provide the best display method based on the user's past usage history.

[0088] The providing unit can complement the display content when providing the display content by taking into account the user's current traffic conditions and weather information. The providing unit complements the display content by taking into account, for example, the user's current traffic conditions and weather information. The providing unit provides optimal display content based on, for example, current traffic congestion information. The providing unit provides optimal display content by taking into account, for example, current weather information. The providing unit provides optimal display content by taking into account, for example, the current operation status of public transportation. The providing unit can acquire traffic conditions based on, for example, real-time traffic information and congestion information. The providing unit can acquire weather information based on, for example, weather data and weather forecasts. This allows the providing unit to provide optimal display content based on the current traffic conditions and weather information.

[0089] The providing unit can estimate the user's emotions and determine the priority of ticket information based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is nervous, the providing unit prioritizes displaying important ticket information. For example, if the user is relaxed, the providing unit provides detailed ticket information. For example, if the user is in a hurry, the providing unit displays the most important ticket information first. This allows the providing unit to provide priority of ticket information according to the user's emotions.

[0090] When providing the information, the providing unit can prioritize displaying the best ticket information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying highly relevant ticket information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying ticket information close to the user's current location. For example, the providing unit prioritizes displaying highly relevant ticket information based on the user's past movement history. For example, the providing unit prioritizes displaying optimal ticket information by taking into account the user's current traffic conditions. For example, the providing unit can acquire geographical location information based on GPS information or Wi-Fi location information. This allows the providing unit to provide optimal ticket information based on the user's geographical location information.

[0091] The providing unit can analyze the user's social media activity and display related ticket information at the time of providing. The providing unit, for example, analyzes the user's social media activity. The providing unit displays related ticket information based on the user's check-in information on social media, for example. The providing unit, for example, analyzes the content of the user's posts on social media and displays related ticket information. The providing unit displays related ticket information based on the activity of the user's friends on social media, for example. The providing unit can acquire social media activity based on, for example, the content of posts, the number of likes, the number of followers, etc. This allows the providing unit to provide optimal ticket information based on the user's social media activity.

[0092] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated user's emotions. The payment unit, for example, estimates the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is feeling stressed, the payment unit provides a simple payment interface. For example, if the user is relaxed, the payment unit provides detailed payment options. For example, if the user is in a hurry, the payment unit provides a method for quickly completing payment. This allows the payment unit to provide a payment method according to the user's emotions.

[0093] At the time of payment, the payment unit can select the best payment method by referring to the user's past payment history. The payment unit, for example, selects the optimal payment method by referring to the user's past payment history. The payment unit, for example, suggests the optimal payment method based on payment methods used by the user in the past. The payment unit, for example, prioritizes and displays the most frequently used payment method from the user's past payment history. The payment unit, for example, analyzes the user's past payment history and suggests the most efficient payment method. The payment unit can acquire past payment history based on, for example, credit card usage history or electronic money usage history. This allows the payment unit to provide the optimal payment method based on the user's past payment history.

[0094] At the time of payment, the payment unit can propose the best payment plan taking into account the user's current economic situation. The payment unit proposes a payment plan taking into account, for example, the user's current economic situation. The payment unit proposes the best payment plan based on, for example, the user's current bank balance. The payment unit proposes the best payment plan taking into account, for example, the user's credit card usage. The payment unit proposes the best payment plan based on, for example, the balance between the user's income and expenses. The payment unit can acquire the user's economic situation based on, for example, income information, expense information, and credit score. This allows the payment unit to provide the best payment plan according to the user's economic situation.

[0095] The payment unit can estimate the user's emotions and determine payment priorities based on the estimated user emotions. The payment unit, for example, estimates the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is nervous, the payment unit prioritizes displaying important payment items. For example, if the user is relaxed, the payment unit provides detailed payment options. For example, if the user is in a hurry, the payment unit displays the most important payment items first. This allows the payment unit to provide payment priorities according to the user's emotions.

[0096] The payment unit can propose the best payment method at the time of payment, taking into account the user's geographical location information. The payment unit proposes the best payment method, for example, taking into account the user's geographical location information. The payment unit proposes the best payment method, for example, based on the user's current location. The payment unit proposes the best payment method, for example, based on the user's past payment history. The payment unit proposes the best payment method, for example, taking into account the user's current economic situation. The payment unit can acquire geographical location information, for example, based on GPS information or Wi-Fi location information. This allows the payment unit to provide the best payment method based on the user's geographical location information.

[0097] The payment unit can analyze the user's social media activity at the time of payment and suggest a relevant payment method. The payment unit, for example, analyzes the user's social media activity. The payment unit can suggest a relevant payment method based on the user's social media check-in information, for example. The payment unit can analyze the content of the user's social media posts and suggest a relevant payment method. The payment unit can suggest a relevant payment method based on the activity of the user's friends on social media, for example. The payment unit can acquire social media activity based on the content of posts, the number of likes, the number of followers, etc. This allows the payment unit to provide the optimal payment method based on the user's social media activity.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can also monitor the user's health condition and adjust the input method for the departure point and destination based on the health condition. For example, if the user is tired, a simple interface is provided to minimize input steps. If the user is in good health, detailed input options are provided and a customizable input method is suggested. If the user is in a hurry, voice input is prioritized to enable the user to quickly input the departure point and destination. In this way, the reception unit can provide an input method according to the user's health condition.

[0100] The generation unit can also predict the optimal route based on the user's past movement history. For example, it can prioritize and suggest routes that the user has frequently used in the past. It can predict routes that the user will use on specific days of the week or time periods and present them as candidates. It can analyze the user's past movement patterns and suggest the most efficient route. This allows the generation unit to provide the optimal route based on the user's past movement history.

[0101] The providing unit can also adjust the display method of ticket information taking into account the user's current traffic conditions and weather information. For example, the providing unit provides optimal display content based on current traffic congestion information. The providing unit provides optimal display content taking into account current weather information. The providing unit provides optimal display content taking into account the current operation status of public transportation. This allows the providing unit to provide optimal display content based on current traffic conditions and weather information.

[0102] The payment unit can also propose an optimal payment plan taking into account the user's current financial situation. For example, the payment unit can propose an optimal payment plan based on the user's current bank balance, or the payment unit can propose an optimal payment plan taking into account the user's credit card usage, or the payment unit can propose an optimal payment plan based on the balance between the user's income and expenses. In this way, the payment unit can provide the optimal payment plan according to the user's financial situation.

[0103] The reception unit can also analyze the user's social media activity and present related candidate locations. For example, the reception unit can suggest related candidate locations based on the user's check-in information on social media. The reception unit can suggest related candidate locations by analyzing the content of the user's social media posts. The reception unit can suggest related candidate locations based on the activity of the user's friends on social media. This allows the reception unit to present optimal candidate locations based on the user's social media activity.

[0104] The generation unit can also estimate the user's emotions and adjust the method for generating an optimal route based on the estimated user's emotions. For example, if the user is relaxed, a scenic route is generated. If the user is in a hurry, the shortest route is generated. If the user is excited, a visually stimulating route is generated. In this way, the generation unit can provide an optimal route according to the user's emotions.

[0105] The providing unit can also estimate the user's emotions and adjust the display method of ticket information based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that focuses on the main points is provided. In this way, the providing unit can provide a display method that corresponds to the user's emotions.

[0106] The payment unit can also estimate the user's emotions and adjust the payment method based on the estimated user's emotions. For example, if the user is stressed, a simple payment interface is provided; if the user is relaxed, detailed payment options are provided; if the user is in a hurry, a method for quickly completing payment is provided. In this way, the payment unit can provide a payment method according to the user's emotions.

[0107] The generation unit can also estimate the user's emotions and determine the priority of routes based on the estimated user's emotions. For example, if the user is nervous, the safest route is prioritized. If the user is relaxed, the route with the best scenery is prioritized. If the user is in a hurry, the shortest route is prioritized. In this way, the generation unit can provide route priorities according to the user's emotions.

[0108] The providing unit can also estimate the user's emotions and determine the priority of ticket information based on the estimated user's emotions. For example, if the user is nervous, important ticket information is displayed with priority. If the user is relaxed, detailed ticket information is provided. If the user is in a hurry, the most important ticket information is displayed first. In this way, the providing unit can provide the priority of ticket information according to the user's emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives input of the user's departure point and destination. For example, the user can input the departure point and destination within the app. The reception unit can also obtain the user's current location using, for example, GPS information or Wi-Fi location information. Step 2: The generation unit uses a generation AI to generate directions from the current location to the destination based on the information received by the reception unit. The generation AI calculates the optimal route using, for example, a text generation AI (e.g., LLM). The generation unit can calculate, for example, the shortest route or the cheapest route. Step 3: The providing unit provides the ticket information generated by the generating unit to the user. For example, the providing unit displays the ticket information in the app and allows the user to board the train by scanning the two-dimensional code. For example, the providing unit can generate a two-dimensional code or a barcode and provide it to the user. Step 4: The payment unit manages the monthly fee from the user and the platform usage fee from the transportation company. The payment unit can manage payment methods such as credit cards, debit cards, and electronic money. For example, the payment unit can automatically debit the monthly fee from the user and pay the platform usage fee to the transportation company.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] 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.

[0114] Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, and payment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of the user's departure point and destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated ticket information to the user. The payment unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's monthly fee and the platform usage fee from the transportation company. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, and payment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of the user's departure point and destination. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated ticket information to the user. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's monthly fee and the platform usage fee from the transportation operator. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and payment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of the user's departure point and destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using a generation AI. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated ticket information to the user. The payment unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's monthly fee and the platform usage fee from the transportation operator. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, and payment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of the user's departure point and destination. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated ticket information to the user. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's monthly fee and the platform usage fee from the transportation company. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) a reception unit that receives input of a departure point and a destination; A generation unit that generates a route from the current location to the destination based on the information received by the reception unit using a generation AI; a providing unit that provides the ticket information generated by the generating unit to a user; A payment unit that manages monthly fees from users and platform usage fees from transportation operators. A system characterized by: (Appendix 2) The generation unit Generative AI generates prompts and matches the best tickets 2. The system of claim 1. (Appendix 3) The providing unit The generated ticket information is displayed within the app, and users can board by scanning the 2D code. 2. The system of claim 1. (Appendix 4) The payment unit Manage monthly fees from users and platform usage fees from transportation operators 2. The system of claim 1. (Appendix 5) The reception unit Accepts the user's current location and destination information 2. The system of claim 1. (Appendix 6) The generation unit Calculate the best route based on the user's current location and destination, and generate a ticket along that route 2. The system of claim 1. (Appendix 7) The reception unit Inferring user emotions and adjusting the input method for departure and destination based on the estimated user emotions 2. The system of claim 1. (Appendix 8) The reception unit Analyzes the user's past travel history and presents the best possible starting and destination locations 2. The system of claim 1. (Appendix 9) The reception unit When entering a departure point and destination, the system takes into account the user's current traffic and weather information to complete the input. 2. The system of claim 1. (Appendix 10) The reception unit Estimate the user's emotions and prioritize the best inputs based on the estimated user emotions. 2. The system of claim 1. (Appendix 11) The reception unit When entering origin and destination, the app takes into account the user's geographic location to prioritize relevant suggestions. 2. The system of claim 1. (Appendix 12) The reception unit Analyzes users' social media activity and provides relevant suggestions when entering origin and destination 2. The system of claim 1. (Appendix 13) The generation unit Estimate the user's emotions and adjust the optimal route generation method based on the estimated user emotions. 2. The system of claim 1. (Appendix 14) The generation unit When generating a route, it looks at past travel data to predict the best route. 2. The system of claim 1. (Appendix 15) The generation unit When generating a route, traffic and weather information is reflected in real time to optimize the route. 2. The system of claim 1. (Appendix 16) The generation unit Estimate user emotions and prioritize routes based on the estimated user emotions 2. The system of claim 1. (Appendix 17) The generation unit At generation time, the best route is generated taking into account the user's geographic location. 2. The system of claim 1. (Appendix 18) The generation unit At the time of generation, the system analyzes the user's social media activity and generates relevant routes. 2. The system of claim 1. (Appendix 19) The providing unit Infer user sentiment and adjust how ticket information is displayed based on the inferred sentiment 2. The system of claim 1. (Appendix 20) The providing unit When providing the information, the best display method is selected by referring to the user's past usage history. 2. The system of claim 1. (Appendix 21) The providing unit When providing information, the display content is supplemented by taking into account the user's current traffic conditions and weather information. 2. The system of claim 1. (Appendix 22) The providing unit Estimate user sentiment and prioritize ticket information based on the estimated user sentiment 2. The system of claim 1. (Appendix 23) The providing unit At the time of delivery, the best ticket information is prioritized based on the user's geographic location. 2. The system of claim 1. (Appendix 24) The providing unit When provided, analyze your social media activity and display relevant ticket information 2. The system of claim 1. (Appendix 25) The payment unit Inferring user sentiment and adjusting payment methods based on the estimated user sentiment 2. The system of claim 1. (Appendix 26) The payment unit At the time of payment, we look at the user's past payment history to select the best payment method. 2. The system of claim 1. (Appendix 27) The payment unit At checkout, we offer the best payment plan based on your current financial situation. 2. The system of claim 1. (Appendix 28) The payment unit Estimate user sentiment and prioritize payments based on the estimated sentiment 2. The system of claim 1. (Appendix 29) The payment unit When making a payment, it takes into account the user's geographic location to suggest the best payment method. 2. The system of claim 1. (Appendix 30) The payment unit When making a payment, analyze your social media activity and suggest relevant payment options 2. The system of claim 1. [Explanation of symbols]

[0183] 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 reception unit that receives input of a departure point and a destination; A generation unit that generates a route from a current location to a destination based on the information received by the reception unit; a providing unit that provides a user with the ticket information generated by the generating unit; A payment unit that manages monthly fees from users and platform usage fees from transportation companies. A system characterized by:

2. The generation unit is Generative AI generates prompts to match the best tickets 2. The system of claim 1 .

3. The providing unit is The generated ticket information is displayed within the app, and users can board the train by scanning the 2D code.

2. The system of claim 1 .

4. The payment unit, Manage monthly fees from users and platform usage fees from transportation operators 2. The system of claim 1 .

5. The reception unit is Accepts the user's current location and destination information 2. The system of claim 1 .

6. The generation unit is Calculate the best route based on the user's current location and destination, and generate a ticket along that route 2. The system of claim 1 .

7. The reception unit is Infer user emotions and adjust the input method for departure and destination based on the estimated user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past travel history and suggest the best starting and destination locations 2. The system of claim 1 .

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