system

Abstract

The system according to this embodiment aims to enable travelers to use cashless payment regardless of their destination. [Solution] The system according to the embodiment comprises a selection unit, a management unit, a guidance unit, a resolution unit, and a provision unit. The selection unit automatically selects the most suitable payment method for the traveler's destination. The management unit centrally manages the payment methods selected by the selection unit. The guidance unit provides real-time information on stores that accept local currency or cashless payments based on the payment methods managed by the management unit. The resolution unit quickly provides solutions in the event of payment errors or fraudulent use. The provision unit provides an integrated API for companies to develop a payment environment for tourists.

Description

Technical Field

[0004] ,

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[0003] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is cumbersome to prepare different cashless means for each country or region that a traveler visits, and there is a risk of anxiety and trouble regarding payments at the destination.

[0005] The system according to the embodiment aims to enable a traveler to use cashless payment without worrying about the destination.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, a management unit, a guidance unit, a resolution unit, and a provision unit. The selection unit automatically selects the most suitable payment method for the traveler's destination. The management unit centrally manages the payment methods selected by the selection unit. The guidance unit provides real-time information on stores that accept local currency or cashless payments based on the payment methods managed by the management unit. The resolution unit quickly provides solutions in the event of payment errors or fraudulent use. The provision unit provides an integrated API for companies to develop a payment environment for tourists. [Effects of the Invention]

[0007] The system according to this embodiment can enable travelers to use cashless payment regardless of their destination. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). <00000​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The cashless payment management system according to an embodiment of the present invention is a system that seamlessly manages and optimizes cashless payments for travelers. This cashless payment management system aims to eliminate the hassle of travelers researching and preparing different cashless payment methods for each country or region they visit, and to provide the most suitable payment method locally. Specifically, first, the AI ​​automatically selects the cashless payment method for the country or region the traveler is visiting and centrally manages the necessary apps and virtual cards. Next, it aims to reduce costs by selecting the optimal exchange rate and fees. It also provides real-time guidance on stores that accept local currency and cashless payments, and the AI ​​quickly provides solutions in the event of payment errors or fraudulent use. Furthermore, it provides an integrated API for companies to develop a payment environment for tourists using GlobalPay AI. This mechanism allows travelers to use cashless payments without worrying about their destination, maximizing the freedom and convenience of travel. First, the AI ​​automatically selects the cashless payment method for the country or region the traveler is visiting. In this process, the AI ​​collects cashless payment information for each country and proposes the most suitable payment method for the traveler. For example, if a traveler is going to Japan, the AI ​​will list the cashless payment methods popular in Japan and automatically install the necessary apps or virtual cards. This makes it easy for travelers to prepare for cashless payments locally. Next, it aims to reduce costs by selecting the optimal exchange rate and fees. The AI ​​compares the local currency with the exchange rate and fees in real time and presents the most cost-effective option. For example, if a traveler is going to the United States, the AI ​​will compare the dollar-yen exchange rate and suggest the payment method with the lowest fees. This allows travelers to use cashless payments while keeping costs down. Furthermore, it provides real-time information on stores that accept local currency and cashless payments. The AI ​​knows the traveler's current location and guides them to nearby stores that accept cashless payments. For example, if a traveler is in France, the AI ​​will guide them in real time to nearby restaurants and shops that accept cashless payments. This allows travelers to pay with peace of mind even if they are short on local currency. In addition, the AI ​​will quickly provide solutions in the event of payment errors or fraudulent use.When the AI ​​detects payment errors or fraudulent activity, it immediately notifies the traveler and guides them through the process, such as suspending the transaction or providing alternative methods. For example, if fraudulent activity is detected in a traveler's account in Italy, the AI ​​will immediately notify them and guide them through the process, such as suspending the transaction or providing alternative methods. This allows travelers to use cashless payments with peace of mind. Finally, GlobalPay provides an integrated API that enables businesses to leverage AI to develop payment environments for tourists. Businesses can use this API to develop cashless payment environments for tourists. For example, travel agencies and airlines can use this API to provide cashless payment services to tourists. This improves the payment environment for tourists and enhances convenience for travelers. As a result, cashless payment management systems can seamlessly manage and optimize cashless payments for travelers.

[0029] The cashless payment management system according to this embodiment comprises a selection unit, a management unit, a guidance unit, a solution unit, and a provision unit. The selection unit automatically selects the most suitable payment method for the traveler's destination. The selection unit, for example, collects cashless information from various countries and proposes the most suitable payment method for the traveler. For example, if the traveler is going to Japan, the selection unit lists the cashless payment methods prevalent in Japan and automatically installs the necessary apps and virtual cards. The management unit centrally manages the payment methods selected by the selection unit. For example, the management unit centrally manages the necessary apps and virtual cards. For example, the management unit centrally manages information on apps and virtual cards used by travelers and makes them easily accessible to travelers. The guidance unit provides real-time information on stores that accept local currency or cashless payments based on the payment methods managed by the management unit. For example, the guidance unit determines the traveler's current location and guides them to nearby stores that accept cashless payments. For example, if the traveler is in France, the guidance unit provides real-time information on nearby restaurants and shops that accept cashless payments. The resolution unit provides rapid solutions in the event of payment errors or fraudulent use. For example, if the resolution unit detects a payment error or fraudulent use, it immediately notifies the traveler and guides them through the process of suspending use or providing alternative methods. For example, if fraudulent use is detected on a traveler in Italy, the resolution unit immediately notifies them and guides them through the process of suspending use or providing alternative methods. The provision unit provides an integrated API for companies to develop a payment environment for tourists. For example, the provision unit provides an integrated API for companies to develop a payment environment for tourists using GlobalPay AI. For example, travel agencies and airlines can use this API to provide cashless payment services for tourists. As a result, the cashless payment management system according to the embodiment can seamlessly manage and optimize cashless payments for travelers.

[0030] The selection unit automatically selects the most suitable payment method for the traveler's destination. For example, it collects cashless payment information from various countries and proposes the most suitable payment method to the traveler. Specifically, the selection unit collects information such as the prevalence of cashless payment methods in each country, available applications, fees, and security levels. This information is obtained from publicly available databases on the internet and the official websites of financial institutions in each country. The selection unit selects the most suitable payment method according to the traveler's destination, length of stay, and purpose of travel (business, tourism, etc.). For example, if a traveler is going to Japan, it lists the cashless payment methods prevalent in Japan and automatically installs the necessary apps or virtual cards. The selection unit checks the applications installed on the traveler's smartphone and automatically installs any necessary apps that are not installed. The virtual card issuance process is also automated, allowing travelers to prepare the necessary payment method without any hassle. The selection unit also considers the traveler's past usage history and preferences to propose the most suitable payment method. For example, it prioritizes suggesting apps to travelers who have previously used a particular app. Furthermore, the selection department collects traveler feedback and continuously improves the accuracy of its recommendations. This allows the selection department to provide travelers with the most suitable cashless payment methods and enhance the convenience of their travels.

[0031] The Management Department centrally manages the payment methods selected by the Selection Department. For example, the Management Department centrally manages necessary apps and virtual cards. Specifically, the Management Department centrally manages information on apps and virtual cards used by travelers, making them easily accessible to travelers. The Management Department stores information on applications and virtual cards installed on travelers' smartphones in the cloud, allowing travelers to access it when needed. This allows travelers to centrally manage multiple apps and virtual cards, improving convenience. The Management Department monitors travelers' usage in real time and updates or reissues apps and virtual cards as needed. For example, if a traveler moves to a new country, the system automatically updates apps and virtual cards available in that country. The Management Department also analyzes travelers' usage history and prioritizes displaying frequently used apps and virtual cards. This allows travelers to quickly access necessary information. Furthermore, the Management Department strengthens security measures, safely protecting travelers' personal and payment information. For example, the Management Department uses encryption technology to protect data and prevent unauthorized access. The Management Department also collects traveler feedback to improve the system. This allows the management department to provide travelers with a user-friendly and secure cashless payment environment.

[0032] The information department provides real-time information on stores that accept local currency or cashless payments, based on payment methods managed by the administration department. For example, the information department can determine a traveler's current location and guide them to nearby stores that accept cashless payments. Specifically, the information department uses the GPS function of the traveler's smartphone to pinpoint their current location and searches for stores in the surrounding area that accept cashless payments. The information department retrieves information on each store from a database and suggests the most suitable store for the traveler. For example, if a traveler is in France, it will provide real-time information on nearby restaurants and shops that accept cashless payments. The information department also considers the traveler's preferences and past usage history to suggest the most suitable stores. For example, it will prioritize suggesting restaurants to travelers who have previously used a particular restaurant. The information department also collects traveler feedback and continuously improves the accuracy of its suggestions. This allows the information department to provide travelers with the most suitable stores that accept cashless payments, improving the convenience of their trip. Furthermore, the information department also provides information such as the congestion level and opening hours of the stores that travelers visit. This allows travelers to avoid crowds and enjoy shopping and dining efficiently. Furthermore, the information department provides reviews and ratings of shops that travelers visit, helping them choose shops they can use with confidence. In this way, the information department can provide travelers with reliable information and improve their travel satisfaction.

[0033] The resolution unit provides swift solutions in the event of payment errors or fraudulent use. For example, upon detecting a payment error or fraudulent use, the resolution unit immediately notifies the traveler and guides them through the process of suspending the transaction or providing alternative options. Specifically, the resolution unit monitors the traveler's payment history in real time and detects unusual transactions or signs of fraudulent use. For example, if a traveler makes a large transaction at an unusual location or time, the resolution unit immediately detects the transaction and notifies the traveler. Notifications are sent via smartphone push notifications, email, SMS, etc. Upon receiving the notification, the traveler can check the transaction details and immediately suspend the transaction if fraudulent use is suspected. The resolution unit also automates the suspension process, allowing travelers to respond quickly and easily. Furthermore, the resolution unit guides travelers through alternative options to ensure they can continue making payments without inconvenience. For example, if fraudulent use is detected in Italy, the traveler is immediately notified and guided through the process of suspending the transaction or providing alternative options. Alternative options may include using another virtual card or withdrawing cash. Furthermore, the solutions unit collects traveler feedback and uses it to improve the system. This allows the solutions unit to provide travelers with a cashless payment environment they can use with peace of mind.

[0034] The service provider offers an integrated API to help businesses develop payment environments for tourists. Specifically, the service provider provides APIs for companies such as travel agencies, airlines, and hotels to offer cashless payment services for tourists. This allows companies to easily integrate cashless payment functionality into their services. For example, when a travel agency offers tours for tourists, integrating cashless payment functionality allows tourists to easily book and pay for tours. Similarly, when an airline integrates cashless payment functionality for flight bookings and payments, tourists can purchase tickets smoothly. Furthermore, when a hotel integrates cashless payment functionality for accommodation payments, tourists can pay smoothly during check-in and check-out. The service provider also provides technical support for these companies to provide cashless payment services for tourists. For example, they provide support on how to use the API and troubleshooting, enabling companies to smoothly implement cashless payment functionality. In this way, the service provider can provide a convenient cashless payment environment for tourists and contribute to improving the services of businesses.

[0035] The selection unit can collect cashless payment information from various countries and propose the most suitable payment method for travelers. For example, the selection unit can collect information on the penetration rate of cashless payments and available payment methods in each country and propose the most suitable payment method for travelers. For example, if a traveler is going to Japan, the selection unit can list the cashless payment methods that are popular in Japan and automatically install the necessary apps or virtual cards. For example, if a traveler is going to the United States, the selection unit can list the cashless payment methods that are popular in the United States and propose the most suitable payment method. In this way, the selection unit improves the convenience of cashless payments by proposing the most suitable payment method for travelers. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input cashless payment information from various countries into a generative AI and have the generative AI propose the most suitable payment method.

[0036] The management unit can centrally manage necessary apps and virtual cards. For example, the management unit can centrally manage information on apps and virtual cards used by travelers, making them easily accessible to travelers. For example, if a traveler goes to Japan, the management unit can centrally manage apps and virtual cards that can be used in Japan. For example, if a traveler goes to the United States, the management unit can centrally manage apps and virtual cards that can be used in the United States. This improves the convenience for travelers by allowing the management unit to centrally manage necessary apps and virtual cards. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information on apps and virtual cards used by travelers into a generative AI and have the generative AI perform the centralized management.

[0037] The information unit can determine the traveler's current location and provide real-time information on nearby stores that accept cashless payments. For example, the information unit can determine the traveler's current location using GPS and provide information on nearby stores that accept cashless payments. For example, if the traveler is in France, the information unit can provide real-time information on nearby restaurants and shops that accept cashless payments. For example, if the traveler is in Italy, the information unit can provide real-time information on nearby stores that accept cashless payments. This allows the information unit to ensure that travelers can make payments with peace of mind even if they lack local currency. Some or all of the above processing in the information unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information unit can input the traveler's current location information into a generative AI and have the generative AI provide information on nearby stores that accept cashless payments.

[0038] The resolution unit can immediately notify travelers upon detecting payment errors or fraudulent use, and guide them through the process of suspending use or providing alternative methods. For example, the resolution unit can detect payment errors or fraudulent use in real time and notify travelers. For example, if a traveler is detected to have committed fraud in Italy, the resolution unit will immediately notify them and guide them through the process of suspending use or providing alternative methods. For example, if a traveler is detected to have committed a payment error in France, the resolution unit will immediately notify them and guide them through the process of suspending use or providing alternative methods. This allows travelers to use cashless payments with peace of mind. Some or all of the above-described processes in the resolution unit may be performed using, for example, a generating AI, or without a generating AI. For example, the resolution unit can input the detection of payment errors or fraudulent use into a generating AI and have the generating AI perform the task of suggesting solutions.

[0039] The service provider can provide an integrated API for businesses to develop payment environments for tourists. For example, the service provider can provide an integrated API for businesses to develop payment environments for tourists using GlobalPay AI. For example, travel agencies and airlines can use this API to provide cashless payment services for tourists. For example, shops in tourist areas can use this API to develop cashless payment environments for tourists. This allows the service provider to enable businesses to develop cashless payment environments for tourists. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input a company's request into a generative AI and have the generative AI execute the optimal method for providing the API.

[0040] The selection unit can analyze the traveler's past travel history during the selection process and propose the most suitable payment method. For example, the selection unit can propose the most suitable payment method based on the payment methods the traveler has used in the past. For example, the selection unit can propose payment methods available in a specific region based on the traveler's past travel history. For example, the selection unit can analyze the traveler's past spending patterns and propose the most suitable payment method. In this way, the selection unit can propose the most suitable payment method by analyzing the traveler's past travel history. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the traveler's past travel history data into a generative AI and have the generative AI propose the most suitable payment method.

[0041] The selection unit can customize payment methods based on the traveler's current financial situation and spending habits during the selection process. For example, the selection unit may suggest the optimal payment method by considering the traveler's current bank balance. For example, the selection unit may analyze the traveler's spending habits and suggest the most cost-effective payment method. For example, the selection unit may suggest available payment methods based on the traveler's credit score. In this way, the selection unit can provide the optimal payment method by customizing it based on the traveler's current financial situation and spending habits. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit may input data on the traveler's current financial situation and spending habits into a generative AI and have the generative AI perform the customization of payment methods.

[0042] The selection unit can prioritize selecting payment methods that are highly relevant, taking into account the traveler's geographical location information during the selection process. For example, if the traveler is in a specific region, the selection unit will prioritize suggesting payment methods available in that region. For example, if the traveler is in a tourist destination, the selection unit will prioritize suggesting payment methods available in that tourist destination. For example, if the traveler is in an urban area, the selection unit will prioritize suggesting payment methods available in urban areas. In this way, the selection unit can provide highly relevant payment methods by taking into account the traveler's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the selection unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the selection of highly relevant payment methods.

[0043] The selection unit can analyze the traveler's social media activity during the selection process and propose relevant payment methods. For example, the selection unit can propose the most suitable payment method based on information shared by the traveler on social media. For example, the selection unit can propose payment methods available in a specific region based on the traveler's social media activity. For example, the selection unit can analyze the traveler's social media activity and propose the most suitable payment method. In this way, the selection unit can provide relevant payment methods by analyzing the traveler's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the traveler's social media activity data into a generative AI and have the generative AI propose relevant payment methods.

[0044] The management department can analyze a traveler's past usage history during management and select the optimal management method. For example, the management department can propose the optimal management method based on apps or virtual cards that the traveler has used in the past. For example, the management department can propose a specific management method based on the traveler's past usage history. For example, the management department can analyze the traveler's past usage history and propose the most efficient management method. In this way, the management department can provide the optimal management method by analyzing the traveler's past usage history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the traveler's past usage history data into a generative AI and have the generative AI select the optimal management method.

[0045] The management unit can customize the management means based on the traveler's current device information during management. For example, if the traveler is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the traveler is using a tablet, the management unit provides a management method optimized for tablets. For example, if the traveler is using a smartwatch, the management unit provides a management method optimized for smartwatches. In this way, the management unit can provide the optimal management method by customizing the management means based on the traveler's current device information. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the traveler's current device information into a generative AI and have the generative AI perform the customization of the management means.

[0046] The management unit can prioritize the management of highly relevant apps and virtual cards by considering the traveler's geographical location during management. For example, if the traveler is in a specific region, the management unit will prioritize apps and virtual cards available in that region. For example, if the traveler is in a tourist destination, the management unit will prioritize apps and virtual cards available in that tourist destination. For example, if the traveler is in an urban area, the management unit will prioritize apps and virtual cards available in urban areas. In this way, the management unit can provide highly relevant apps and virtual cards by considering the traveler's geographical location. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the management of highly relevant apps and virtual cards.

[0047] The management department can analyze travelers' social media activity and manage relevant apps and virtual cards during management. For example, the management department can manage the most suitable apps and virtual cards based on information shared by travelers on social media. For example, the management department can manage apps and virtual cards available in a specific region based on travelers' social media activity. For example, the management department can analyze travelers' social media activity and manage the most suitable apps and virtual cards. In this way, the management department can provide relevant apps and virtual cards by analyzing travelers' social media activity. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input travelers' social media activity data into generative AI and have the generative AI perform the management of relevant apps and virtual cards.

[0048] The guidance unit can analyze a traveler's past visit history and guide them to the most suitable store. For example, the guidance unit guides travelers to the most suitable store based on stores they have visited in the past. For example, the guidance unit guides travelers to stores available in a specific area based on their past visit history. For example, the guidance unit analyzes a traveler's past visit history and guides them to the most suitable store. In this way, the guidance unit can guide travelers to the most suitable store by analyzing their past visit history. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the traveler's past visit history data into a generative AI and have the generative AI perform the task of guiding travelers to the most suitable store.

[0049] The guidance system can customize the guidance content based on the traveler's current interests and preferences. For example, the guidance system may prioritize guiding travelers to stores in categories that the traveler is currently interested in. For example, the guidance system may guide travelers to relevant stores based on their current interests. For example, the guidance system may analyze the traveler's current interests and preferences and guide them to the most suitable stores. In this way, the guidance system can guide travelers to the most suitable stores by customizing the guidance content based on the traveler's current interests and preferences. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or not using a generative AI. For example, the guidance system may input the traveler's current interests and preferences data into a generative AI and have the generative AI perform the customization of the guidance content.

[0050] The guidance system can prioritize recommending highly relevant stores by considering the traveler's geographical location. For example, if the traveler is in a specific region, the guidance system will prioritize recommending stores available in that region. For example, if the traveler is in a tourist area, the guidance system will prioritize recommending stores available in that tourist area. For example, if the traveler is in an urban area, the guidance system will prioritize recommending stores available in the urban area. In this way, the guidance system can recommend highly relevant stores by considering the traveler's geographical location. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the traveler's geographical location information into a generative AI and have the generative AI perform the task of recommending highly relevant stores.

[0051] The guidance unit can analyze a traveler's social media activity during guidance and guide them to relevant stores. For example, the guidance unit can guide travelers to the most suitable stores based on information they have shared on social media. For example, the guidance unit can guide travelers to stores available in a specific area based on their social media activity. For example, the guidance unit can analyze a traveler's social media activity and guide them to the most suitable stores. In this way, the guidance unit can guide travelers to relevant stores by analyzing their social media activity. Some or all of the above processing in the guidance unit may be performed using, for example, generative AI, or not using generative AI. For example, the guidance unit can input the traveler's social media activity data into a generative AI and have the generative AI perform the task of guiding travelers to relevant stores.

[0052] The resolution unit can analyze past payment errors and fraudulent use history during the resolution process and propose the optimal solution. For example, the resolution unit may propose the optimal solution based on payment errors the traveler has experienced in the past. For example, the resolution unit may analyze the traveler's past fraudulent use history and propose the most effective solution. For example, the resolution unit may quickly propose a solution based on the traveler's past payment troubles. In this way, the resolution unit can provide the optimal solution by analyzing past payment errors and fraudulent use history. Some or all of the above processing in the resolution unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the resolution unit can input past payment error and fraudulent use history data into a generating AI and have the generating AI execute the task of proposing the optimal solution.

[0053] The solution unit can customize the solution based on the traveler's current situation at the time of resolution. For example, the solution unit may present the optimal solution based on the traveler's current location information. For example, the solution unit may present the most effective solution considering the traveler's current economic situation. For example, the solution unit may present a solution that can be quickly implemented based on the traveler's current device information. In this way, the solution unit can provide the optimal solution by customizing the solution based on the traveler's current situation. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit may input the traveler's current situation data into a generative AI and have the generative AI perform the customization of the solution.

[0054] The solution unit can prioritize presenting highly relevant solutions by considering the traveler's geographical location information during the resolution process. For example, if the traveler is in a specific region, the solution unit will prioritize presenting solutions available in that region. For example, if the traveler is in a tourist destination, the solution unit will prioritize presenting solutions available in that tourist destination. For example, if the traveler is in an urban area, the solution unit will prioritize presenting solutions available in the urban area. In this way, the solution unit can provide highly relevant solutions by considering the traveler's geographical location information. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the task of presenting highly relevant solutions.

[0055] The solution unit can analyze the traveler's social media activity and present relevant solutions during the resolution process. For example, the solution unit can present the optimal solution based on information shared by the traveler on social media. For example, the solution unit can present solutions available in a specific region based on the traveler's social media activity. For example, the solution unit can analyze the traveler's social media activity and present the most suitable solution. In this way, the solution unit can provide relevant solutions by analyzing the traveler's social media activity. Some or all of the above processing in the solution unit may be performed using, for example, generative AI, or without generative AI. For example, the solution unit can input the traveler's social media activity data into a generative AI and have the generative AI perform the task of presenting relevant solutions.

[0056] The service provider can analyze a company's past usage history and select the optimal service delivery method at the time of delivery. For example, the service provider can propose the optimal service delivery method based on the API delivery methods the company has used in the past. For example, the service provider can propose a specific service delivery method based on the company's past usage history. For example, the service provider can analyze the company's past usage history and propose the most efficient service delivery method. In this way, the service provider can provide the optimal service delivery method by analyzing the company's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the company's past usage history data into a generative AI and have the generative AI select the optimal service delivery method.

[0057] The service provider can customize the service content based on the company's current needs at the time of delivery. For example, the service provider can propose the optimal API delivery method considering the company's current business needs. For example, the service provider can propose the most suitable API delivery method based on the company's current technology stack. For example, the service provider can propose the most effective API delivery method considering the company's current market conditions. In this way, the service provider can provide the optimal delivery method by customizing the service content based on the company's current needs. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the company's current needs data into a generative AI and have the generative AI perform the customization of the service content.

[0058] The service provider can prioritize providing highly relevant APIs by considering the geographical location of the company at the time of provision. For example, if the company is in a specific region, the service provider will prioritize providing APIs available in that region. For example, if the company is in a tourist area, the service provider will prioritize providing APIs available in the tourist area. For example, if the company is in an urban area, the service provider will prioritize providing APIs available in the urban area. In this way, the service provider can provide highly relevant APIs by considering the geographical location of the company. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the geographical location of the company into a generative AI and have the generative AI perform the task of providing highly relevant APIs.

[0059] The service provider can analyze a company's social media activities and provide relevant APIs at the time of provision. For example, the service provider can provide the most suitable API based on information shared by a company on social media. For example, the service provider can provide APIs available in a specific region based on a company's social media activities. For example, the service provider can analyze a company's social media activities and provide the most suitable API. In this way, the service provider can provide relevant APIs by analyzing a company's social media activities. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input a company's social media activity data into a generative AI and have the generative AI perform the provision of relevant APIs.

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

[0061] The selection unit can choose the most suitable payment method considering the traveler's health condition. For example, if a traveler has health problems, the selection unit can link with a health management app to suggest a payment method appropriate to the traveler's health condition. If a traveler has diabetes, the selection unit can link with a health management app to suggest a payment method that offers benefits for diabetic patients. If a traveler has allergies, the selection unit can consider allergy information and suggest a payment method that can be used at allergy-friendly stores. In this way, the selection unit can improve the convenience and safety of travelers by providing the most suitable payment method according to their health condition.

[0062] The management department can learn travelers' preferences and customize how apps and virtual cards are managed based on those preferences. For example, if a traveler prefers a particular brand or service, the management department can prioritize the management of apps and virtual cards related to that brand or service. If a traveler frequently uses stores in a particular category, the management department can prioritize the management of apps and virtual cards related to that category. If a traveler frequently visits a particular region, the management department can prioritize the management of apps and virtual cards available in that region. In this way, the management department can improve the convenience of travelers by customizing how apps and virtual cards are managed based on their preferences.

[0063] The guidance department can analyze travelers' past reviews and ratings to recommend the most suitable establishments. For example, it can recommend similar establishments based on places travelers have given high ratings to in the past. It can also advise travelers to avoid places they have given low ratings to in the past. The guidance department can analyze reviews of establishments travelers have visited in the past to recommend the most suitable ones. In this way, the guidance department can recommend the most suitable establishments by analyzing travelers' past reviews and ratings.

[0064] The solution unit can present the optimal solution by considering the traveler's current network connection status. For example, if the traveler has an unstable network connection, the solution unit can present a solution that can be used offline. If the traveler has a stable network connection, the solution unit can present a solution that can be quickly implemented online. If the traveler's network connection is interrupted, the solution unit can present a solution that can be implemented after reconnection. In this way, the solution unit can provide the optimal solution according to the traveler's network connection status.

[0065] The service provider can customize how integrated APIs are delivered based on a company's business model. For example, if a company uses a subscription model, the service provider can propose an API delivery method optimized for subscriptions. If a company provides on-demand services, the service provider can propose an API delivery method optimized for on-demand services. If a company uses an advertising revenue model, the service provider can propose an API delivery method optimized for advertising revenue. In this way, the service provider can improve convenience for companies by customizing how integrated APIs are delivered based on their business model.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The selection unit automatically chooses the most suitable payment method for the traveler's destination. For example, it collects cashless payment information for each country and suggests the most suitable payment method for the traveler. If the traveler is going to Japan, it lists the cashless payment methods that are popular in Japan and automatically installs the necessary apps or virtual cards. Step 2: The management department centrally manages the payment methods selected by the selection department. For example, it centrally manages necessary apps and virtual cards, centrally manages information on apps and virtual cards used by travelers, and makes them easily accessible to travelers. Step 3: The information desk provides real-time information on stores that accept local currency or cashless payments, based on payment methods managed by the administration department. For example, it can determine the traveler's current location and guide them to nearby stores that accept cashless payments. If the traveler is in France, it will provide real-time information on nearby restaurants and shops that accept cashless payments. Step 4: The resolution unit provides quick solutions in the event of payment errors or fraudulent use. For example, upon detecting a payment error or fraudulent use, it immediately notifies the traveler and guides them through the process of suspending the transaction or providing alternative options. If fraudulent use is detected in Italy, the traveler is immediately notified and guided through the process of suspending the transaction or providing alternative options. Step 5: The service provider will provide an integrated API to enable businesses to develop payment environments for tourists. For example, it will provide an integrated API that enables businesses to develop payment environments for tourists using GlobalPay AI. Travel agencies and airlines can then use this API to provide cashless payment services for tourists.

[0068] (Example of form 2) The cashless payment management system according to an embodiment of the present invention is a system that seamlessly manages and optimizes cashless payments for travelers. This cashless payment management system aims to eliminate the hassle of travelers researching and preparing different cashless payment methods for each country or region they visit, and to provide the most suitable payment method locally. Specifically, first, the AI ​​automatically selects the cashless payment method for the country or region the traveler is visiting and centrally manages the necessary apps and virtual cards. Next, it aims to reduce costs by selecting the optimal exchange rate and fees. It also provides real-time guidance on stores that accept local currency and cashless payments, and the AI ​​quickly provides solutions in the event of payment errors or fraudulent use. Furthermore, it provides an integrated API for companies to develop a payment environment for tourists using GlobalPay AI. This mechanism allows travelers to use cashless payments without worrying about their destination, maximizing the freedom and convenience of travel. First, the AI ​​automatically selects the cashless payment method for the country or region the traveler is visiting. In this process, the AI ​​collects cashless payment information for each country and proposes the most suitable payment method for the traveler. For example, if a traveler is going to Japan, the AI ​​will list the cashless payment methods popular in Japan and automatically install the necessary apps or virtual cards. This makes it easy for travelers to prepare for cashless payments locally. Next, it aims to reduce costs by selecting the optimal exchange rate and fees. The AI ​​compares the local currency with the exchange rate and fees in real time and presents the most cost-effective option. For example, if a traveler is going to the United States, the AI ​​will compare the dollar-yen exchange rate and suggest the payment method with the lowest fees. This allows travelers to use cashless payments while keeping costs down. Furthermore, it provides real-time information on stores that accept local currency and cashless payments. The AI ​​knows the traveler's current location and guides them to nearby stores that accept cashless payments. For example, if a traveler is in France, the AI ​​will guide them in real time to nearby restaurants and shops that accept cashless payments. This allows travelers to pay with peace of mind even if they are short on local currency. In addition, the AI ​​will quickly provide solutions in the event of payment errors or fraudulent use.When the AI ​​detects payment errors or fraudulent activity, it immediately notifies the traveler and guides them through the process, such as suspending the transaction or providing alternative methods. For example, if fraudulent activity is detected in a traveler's account in Italy, the AI ​​will immediately notify them and guide them through the process, such as suspending the transaction or providing alternative methods. This allows travelers to use cashless payments with peace of mind. Finally, GlobalPay provides an integrated API that enables businesses to leverage AI to develop payment environments for tourists. Businesses can use this API to develop cashless payment environments for tourists. For example, travel agencies and airlines can use this API to provide cashless payment services to tourists. This improves the payment environment for tourists and enhances convenience for travelers. As a result, cashless payment management systems can seamlessly manage and optimize cashless payments for travelers.

[0069] The cashless payment management system according to this embodiment comprises a selection unit, a management unit, a guidance unit, a solution unit, and a provision unit. The selection unit automatically selects the most suitable payment method for the traveler's destination. The selection unit, for example, collects cashless information from various countries and proposes the most suitable payment method for the traveler. For example, if the traveler is going to Japan, the selection unit lists the cashless payment methods prevalent in Japan and automatically installs the necessary apps and virtual cards. The management unit centrally manages the payment methods selected by the selection unit. For example, the management unit centrally manages the necessary apps and virtual cards. For example, the management unit centrally manages information on apps and virtual cards used by travelers and makes them easily accessible to travelers. The guidance unit provides real-time information on stores that accept local currency or cashless payments based on the payment methods managed by the management unit. For example, the guidance unit determines the traveler's current location and guides them to nearby stores that accept cashless payments. For example, if the traveler is in France, the guidance unit provides real-time information on nearby restaurants and shops that accept cashless payments. The resolution unit provides rapid solutions in the event of payment errors or fraudulent use. For example, if the resolution unit detects a payment error or fraudulent use, it immediately notifies the traveler and guides them through the process of suspending use or providing alternative methods. For example, if fraudulent use is detected on a traveler in Italy, the resolution unit immediately notifies them and guides them through the process of suspending use or providing alternative methods. The provision unit provides an integrated API for companies to develop a payment environment for tourists. For example, the provision unit provides an integrated API for companies to develop a payment environment for tourists using GlobalPay AI. For example, travel agencies and airlines can use this API to provide cashless payment services for tourists. As a result, the cashless payment management system according to the embodiment can seamlessly manage and optimize cashless payments for travelers.

[0070] The selection unit automatically selects the most suitable payment method for the traveler's destination. For example, it collects cashless payment information from various countries and proposes the most suitable payment method to the traveler. Specifically, the selection unit collects information such as the prevalence of cashless payment methods in each country, available applications, fees, and security levels. This information is obtained from publicly available databases on the internet and the official websites of financial institutions in each country. The selection unit selects the most suitable payment method according to the traveler's destination, length of stay, and purpose of travel (business, tourism, etc.). For example, if a traveler is going to Japan, it lists the cashless payment methods prevalent in Japan and automatically installs the necessary apps or virtual cards. The selection unit checks the applications installed on the traveler's smartphone and automatically installs any necessary apps that are not installed. The virtual card issuance process is also automated, allowing travelers to prepare the necessary payment method without any hassle. The selection unit also considers the traveler's past usage history and preferences to propose the most suitable payment method. For example, it prioritizes suggesting apps to travelers who have previously used a particular app. Furthermore, the selection department collects traveler feedback and continuously improves the accuracy of its recommendations. This allows the selection department to provide travelers with the most suitable cashless payment methods and enhance the convenience of their travels.

[0071] The Management Department centrally manages the payment methods selected by the Selection Department. For example, the Management Department centrally manages necessary apps and virtual cards. Specifically, the Management Department centrally manages information on apps and virtual cards used by travelers, making them easily accessible to travelers. The Management Department stores information on applications and virtual cards installed on travelers' smartphones in the cloud, allowing travelers to access it when needed. This allows travelers to centrally manage multiple apps and virtual cards, improving convenience. The Management Department monitors travelers' usage in real time and updates or reissues apps and virtual cards as needed. For example, if a traveler moves to a new country, the system automatically updates apps and virtual cards available in that country. The Management Department also analyzes travelers' usage history and prioritizes displaying frequently used apps and virtual cards. This allows travelers to quickly access necessary information. Furthermore, the Management Department strengthens security measures, safely protecting travelers' personal and payment information. For example, the Management Department uses encryption technology to protect data and prevent unauthorized access. The Management Department also collects traveler feedback to improve the system. This allows the management department to provide travelers with a user-friendly and secure cashless payment environment.

[0072] The information department provides real-time information on stores that accept local currency or cashless payments, based on payment methods managed by the administration department. For example, the information department can determine a traveler's current location and guide them to nearby stores that accept cashless payments. Specifically, the information department uses the GPS function of the traveler's smartphone to pinpoint their current location and searches for stores in the surrounding area that accept cashless payments. The information department retrieves information on each store from a database and suggests the most suitable store for the traveler. For example, if a traveler is in France, it will provide real-time information on nearby restaurants and shops that accept cashless payments. The information department also considers the traveler's preferences and past usage history to suggest the most suitable stores. For example, it will prioritize suggesting restaurants to travelers who have previously used a particular restaurant. The information department also collects traveler feedback and continuously improves the accuracy of its suggestions. This allows the information department to provide travelers with the most suitable stores that accept cashless payments, improving the convenience of their trip. Furthermore, the information department also provides information such as the congestion level and opening hours of the stores that travelers visit. This allows travelers to avoid crowds and enjoy shopping and dining efficiently. Furthermore, the information department provides reviews and ratings of shops that travelers visit, helping them choose shops they can use with confidence. In this way, the information department can provide travelers with reliable information and improve their travel satisfaction.

[0073] The resolution unit provides swift solutions in the event of payment errors or fraudulent use. For example, upon detecting a payment error or fraudulent use, the resolution unit immediately notifies the traveler and guides them through the process of suspending the transaction or providing alternative options. Specifically, the resolution unit monitors the traveler's payment history in real time and detects unusual transactions or signs of fraudulent use. For example, if a traveler makes a large transaction at an unusual location or time, the resolution unit immediately detects the transaction and notifies the traveler. Notifications are sent via smartphone push notifications, email, SMS, etc. Upon receiving the notification, the traveler can check the transaction details and immediately suspend the transaction if fraudulent use is suspected. The resolution unit also automates the suspension process, allowing travelers to respond quickly and easily. Furthermore, the resolution unit guides travelers through alternative options to ensure they can continue making payments without inconvenience. For example, if fraudulent use is detected in Italy, the traveler is immediately notified and guided through the process of suspending the transaction or providing alternative options. Alternative options may include using another virtual card or withdrawing cash. Furthermore, the solutions unit collects traveler feedback and uses it to improve the system. This allows the solutions unit to provide travelers with a cashless payment environment they can use with peace of mind.

[0074] The service provider offers an integrated API to help businesses develop payment environments for tourists. Specifically, the service provider provides APIs for companies such as travel agencies, airlines, and hotels to offer cashless payment services for tourists. This allows companies to easily integrate cashless payment functionality into their services. For example, when a travel agency offers tours for tourists, integrating cashless payment functionality allows tourists to easily book and pay for tours. Similarly, when an airline integrates cashless payment functionality for flight bookings and payments, tourists can purchase tickets smoothly. Furthermore, when a hotel integrates cashless payment functionality for accommodation payments, tourists can pay smoothly during check-in and check-out. The service provider also provides technical support for these companies to provide cashless payment services for tourists. For example, they provide support on how to use the API and troubleshooting, enabling companies to smoothly implement cashless payment functionality. In this way, the service provider can provide a convenient cashless payment environment for tourists and contribute to improving the services of businesses.

[0075] The selection unit can collect cashless payment information from various countries and propose the most suitable payment method for travelers. For example, the selection unit can collect information on the penetration rate of cashless payments and available payment methods in each country and propose the most suitable payment method for travelers. For example, if a traveler is going to Japan, the selection unit can list the cashless payment methods that are popular in Japan and automatically install the necessary apps or virtual cards. For example, if a traveler is going to the United States, the selection unit can list the cashless payment methods that are popular in the United States and propose the most suitable payment method. In this way, the selection unit improves the convenience of cashless payments by proposing the most suitable payment method for travelers. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input cashless payment information from various countries into a generative AI and have the generative AI propose the most suitable payment method.

[0076] The management unit can centrally manage necessary apps and virtual cards. For example, the management unit can centrally manage information on apps and virtual cards used by travelers, making them easily accessible to travelers. For example, if a traveler goes to Japan, the management unit can centrally manage apps and virtual cards that can be used in Japan. For example, if a traveler goes to the United States, the management unit can centrally manage apps and virtual cards that can be used in the United States. This improves the convenience for travelers by allowing the management unit to centrally manage necessary apps and virtual cards. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information on apps and virtual cards used by travelers into a generative AI and have the generative AI perform the centralized management.

[0077] The information unit can determine the traveler's current location and provide real-time information on nearby stores that accept cashless payments. For example, the information unit can determine the traveler's current location using GPS and provide information on nearby stores that accept cashless payments. For example, if the traveler is in France, the information unit can provide real-time information on nearby restaurants and shops that accept cashless payments. For example, if the traveler is in Italy, the information unit can provide real-time information on nearby stores that accept cashless payments. This allows the information unit to ensure that travelers can make payments with peace of mind even if they lack local currency. Some or all of the above processing in the information unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information unit can input the traveler's current location information into a generative AI and have the generative AI provide information on nearby stores that accept cashless payments.

[0078] The resolution unit can immediately notify travelers upon detecting payment errors or fraudulent use, and guide them through the process of suspending use or providing alternative methods. For example, the resolution unit can detect payment errors or fraudulent use in real time and notify travelers. For example, if a traveler is detected to have committed fraud in Italy, the resolution unit will immediately notify them and guide them through the process of suspending use or providing alternative methods. For example, if a traveler is detected to have committed a payment error in France, the resolution unit will immediately notify them and guide them through the process of suspending use or providing alternative methods. This allows travelers to use cashless payments with peace of mind. Some or all of the above-described processes in the resolution unit may be performed using, for example, a generating AI, or without a generating AI. For example, the resolution unit can input the detection of payment errors or fraudulent use into a generating AI and have the generating AI perform the task of suggesting solutions.

[0079] The service provider can provide an integrated API for businesses to develop payment environments for tourists. For example, the service provider can provide an integrated API for businesses to develop payment environments for tourists using GlobalPay AI. For example, travel agencies and airlines can use this API to provide cashless payment services for tourists. For example, shops in tourist areas can use this API to develop cashless payment environments for tourists. This allows the service provider to enable businesses to develop cashless payment environments for tourists. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input a company's request into a generative AI and have the generative AI execute the optimal method for providing the API.

[0080] The selection unit can estimate the traveler's emotions and adjust the selection criteria for the optimal payment method based on the estimated emotions. For example, if the traveler is feeling anxious, the selection unit will prioritize suggesting a reliable payment method. For example, if the traveler is relaxed, the selection unit will prioritize suggesting a payment method with low fees. For example, if the traveler is in a hurry, the selection unit will prioritize suggesting a payment method that can be used quickly. In this way, the selection unit improves traveler satisfaction by selecting the optimal payment method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using a generative AI, or not using a generative AI. For example, the selection unit can input traveler emotion data into a generative AI and have the generative AI adjust the selection criteria for the optimal payment method.

[0081] The selection unit can analyze the traveler's past travel history during the selection process and propose the most suitable payment method. For example, the selection unit can propose the most suitable payment method based on the payment methods the traveler has used in the past. For example, the selection unit can propose payment methods available in a specific region based on the traveler's past travel history. For example, the selection unit can analyze the traveler's past spending patterns and propose the most suitable payment method. In this way, the selection unit can propose the most suitable payment method by analyzing the traveler's past travel history. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the traveler's past travel history data into a generative AI and have the generative AI propose the most suitable payment method.

[0082] The selection unit can customize payment methods based on the traveler's current financial situation and spending habits during the selection process. For example, the selection unit may suggest the optimal payment method by considering the traveler's current bank balance. For example, the selection unit may analyze the traveler's spending habits and suggest the most cost-effective payment method. For example, the selection unit may suggest available payment methods based on the traveler's credit score. In this way, the selection unit can provide the optimal payment method by customizing it based on the traveler's current financial situation and spending habits. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit may input data on the traveler's current financial situation and spending habits into a generative AI and have the generative AI perform the customization of payment methods.

[0083] The selection unit can estimate the traveler's emotions and determine the priority of payment methods based on the estimated emotions. For example, if the traveler is stressed, the selection unit will prioritize suggesting payment methods with low fees. For example, if the traveler is having fun, the selection unit will prioritize suggesting payment methods that are convenient. For example, if the traveler is in a hurry, the selection unit will prioritize suggesting payment methods that can be used quickly. In this way, the selection unit improves traveler satisfaction by prioritizing payment methods according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using, for example, generative AI, or not using generative AI. For example, the selection unit can input traveler emotion data into a generative AI and have the generative AI perform the determination of payment method priorities.

[0084] The selection unit can prioritize selecting payment methods that are highly relevant, taking into account the traveler's geographical location information during the selection process. For example, if the traveler is in a specific region, the selection unit will prioritize suggesting payment methods available in that region. For example, if the traveler is in a tourist destination, the selection unit will prioritize suggesting payment methods available in that tourist destination. For example, if the traveler is in an urban area, the selection unit will prioritize suggesting payment methods available in urban areas. In this way, the selection unit can provide highly relevant payment methods by taking into account the traveler's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the selection unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the selection of highly relevant payment methods.

[0085] The selection unit can analyze the traveler's social media activity during the selection process and propose relevant payment methods. For example, the selection unit can propose the most suitable payment method based on information shared by the traveler on social media. For example, the selection unit can propose payment methods available in a specific region based on the traveler's social media activity. For example, the selection unit can analyze the traveler's social media activity and propose the most suitable payment method. In this way, the selection unit can provide relevant payment methods by analyzing the traveler's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the traveler's social media activity data into a generative AI and have the generative AI propose relevant payment methods.

[0086] The management unit can estimate the traveler's emotions and adjust the management methods of the app and virtual cards based on the estimated emotions. For example, if the traveler is stressed, the management unit provides a simple management interface. For example, if the traveler is relaxed, the management unit provides detailed management options. For example, if the traveler is in a hurry, the management unit provides a management method that can be accessed quickly. In this way, the management unit improves the traveler's convenience by adjusting the management method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input traveler emotion data into a generative AI and have the generative AI perform the adjustment of the management method.

[0087] The management department can analyze a traveler's past usage history during management and select the optimal management method. For example, the management department can propose the optimal management method based on apps or virtual cards that the traveler has used in the past. For example, the management department can propose a specific management method based on the traveler's past usage history. For example, the management department can analyze the traveler's past usage history and propose the most efficient management method. In this way, the management department can provide the optimal management method by analyzing the traveler's past usage history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the traveler's past usage history data into a generative AI and have the generative AI select the optimal management method.

[0088] The management unit can customize the management means based on the traveler's current device information during management. For example, if the traveler is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the traveler is using a tablet, the management unit provides a management method optimized for tablets. For example, if the traveler is using a smartwatch, the management unit provides a management method optimized for smartwatches. In this way, the management unit can provide the optimal management method by customizing the management means based on the traveler's current device information. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the traveler's current device information into a generative AI and have the generative AI perform the customization of the management means.

[0089] The management unit can estimate the traveler's emotions and determine the priority of apps and virtual cards to manage based on the estimated emotions. For example, if the traveler is stressed, the management unit will prioritize the most frequently used apps and virtual cards. For example, if the traveler is relaxed, the management unit will provide detailed management options. For example, if the traveler is in a hurry, the management unit will prioritize apps and virtual cards that can be accessed quickly. This improves the traveler's convenience by allowing the management unit to prioritize apps and virtual cards according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input traveler emotion data into a generative AI and have the generative AI determine the priority of apps and virtual cards.

[0090] The management unit can prioritize the management of highly relevant apps and virtual cards by considering the traveler's geographical location during management. For example, if the traveler is in a specific region, the management unit will prioritize apps and virtual cards available in that region. For example, if the traveler is in a tourist destination, the management unit will prioritize apps and virtual cards available in that tourist destination. For example, if the traveler is in an urban area, the management unit will prioritize apps and virtual cards available in urban areas. In this way, the management unit can provide highly relevant apps and virtual cards by considering the traveler's geographical location. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the management of highly relevant apps and virtual cards.

[0091] The management department can analyze travelers' social media activity and manage relevant apps and virtual cards during management. For example, the management department can manage the most suitable apps and virtual cards based on information shared by travelers on social media. For example, the management department can manage apps and virtual cards available in a specific region based on travelers' social media activity. For example, the management department can analyze travelers' social media activity and manage the most suitable apps and virtual cards. In this way, the management department can provide relevant apps and virtual cards by analyzing travelers' social media activity. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input travelers' social media activity data into generative AI and have the generative AI perform the management of relevant apps and virtual cards.

[0092] The information unit can estimate the traveler's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the traveler is nervous, the information unit provides a simple and highly visible display method. For example, if the traveler is relaxed, the information unit provides a display method that includes detailed information. For example, if the traveler is in a hurry, the information unit provides a display method that gets straight to the point. In this way, the information unit improves the traveler's convenience by adjusting the way the information is displayed according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information unit may be performed using a generative AI, or not using a generative AI. For example, the information unit can input traveler emotion data into a generative AI and have the generative AI adjust the way the information is displayed.

[0093] The guidance unit can analyze a traveler's past visit history and guide them to the most suitable store. For example, the guidance unit guides travelers to the most suitable store based on stores they have visited in the past. For example, the guidance unit guides travelers to stores available in a specific area based on their past visit history. For example, the guidance unit analyzes a traveler's past visit history and guides them to the most suitable store. In this way, the guidance unit can guide travelers to the most suitable store by analyzing their past visit history. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the traveler's past visit history data into a generative AI and have the generative AI perform the task of guiding travelers to the most suitable store.

[0094] The guidance system can customize the guidance content based on the traveler's current interests and preferences. For example, the guidance system may prioritize guiding travelers to stores in categories that the traveler is currently interested in. For example, the guidance system may guide travelers to relevant stores based on their current interests. For example, the guidance system may analyze the traveler's current interests and preferences and guide them to the most suitable stores. In this way, the guidance system can guide travelers to the most suitable stores by customizing the guidance content based on the traveler's current interests and preferences. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or not using a generative AI. For example, the guidance system may input the traveler's current interests and preferences data into a generative AI and have the generative AI perform the customization of the guidance content.

[0095] The guidance system can estimate the traveler's emotions and determine the priority of the shops to guide them to based on the estimated emotions. For example, if the traveler is stressed, the guidance system will prioritize shops that offer relaxation. For example, if the traveler is having fun, the guidance system will prioritize shops that offer high entertainment value. For example, if the traveler is in a hurry, the guidance system will prioritize shops that can provide quick service. In this way, the guidance system improves traveler satisfaction by determining the priority of shops to guide them to according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using generative AI, or not using generative AI. For example, the guidance system can input traveler emotion data into a generative AI and have the generative AI perform the determination of shop priorities.

[0096] The guidance system can prioritize recommending highly relevant stores by considering the traveler's geographical location. For example, if the traveler is in a specific region, the guidance system will prioritize recommending stores available in that region. For example, if the traveler is in a tourist area, the guidance system will prioritize recommending stores available in that tourist area. For example, if the traveler is in an urban area, the guidance system will prioritize recommending stores available in the urban area. In this way, the guidance system can recommend highly relevant stores by considering the traveler's geographical location. Some or all of the above processing in the guidance system may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance system can input the traveler's geographical location information into a generative AI and have the generative AI perform the task of recommending highly relevant stores.

[0097] The guidance unit can analyze a traveler's social media activity during guidance and guide them to relevant stores. For example, the guidance unit can guide travelers to the most suitable stores based on information they have shared on social media. For example, the guidance unit can guide travelers to stores available in a specific area based on their social media activity. For example, the guidance unit can analyze a traveler's social media activity and guide them to the most suitable stores. In this way, the guidance unit can guide travelers to relevant stores by analyzing their social media activity. Some or all of the above processing in the guidance unit may be performed using, for example, generative AI, or not using generative AI. For example, the guidance unit can input the traveler's social media activity data into a generative AI and have the generative AI perform the task of guiding travelers to relevant stores.

[0098] The solution unit can estimate the traveler's emotions and adjust how it presents solutions based on the estimated emotions. For example, if the traveler is stressed, the solution unit will present a simple and easily understandable solution. If the traveler is relaxed, the solution unit will present a solution that includes detailed information. If the traveler is in a hurry, the solution unit will present a solution that can be implemented quickly. In this way, the solution unit improves the traveler's convenience by adjusting how it presents solutions according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using a generative AI, or not using a generative AI. For example, the solution unit can input the traveler's emotion data into a generative AI and have the generative AI adjust how it presents solutions.

[0099] The resolution unit can analyze past payment errors and fraudulent use history during the resolution process and propose the optimal solution. For example, the resolution unit may propose the optimal solution based on payment errors the traveler has experienced in the past. For example, the resolution unit may analyze the traveler's past fraudulent use history and propose the most effective solution. For example, the resolution unit may quickly propose a solution based on the traveler's past payment troubles. In this way, the resolution unit can provide the optimal solution by analyzing past payment errors and fraudulent use history. Some or all of the above processing in the resolution unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the resolution unit can input past payment error and fraudulent use history data into a generating AI and have the generating AI execute the task of proposing the optimal solution.

[0100] The solution unit can customize the solution based on the traveler's current situation at the time of resolution. For example, the solution unit may present the optimal solution based on the traveler's current location information. For example, the solution unit may present the most effective solution considering the traveler's current economic situation. For example, the solution unit may present a solution that can be quickly implemented based on the traveler's current device information. In this way, the solution unit can provide the optimal solution by customizing the solution based on the traveler's current situation. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit may input the traveler's current situation data into a generative AI and have the generative AI perform the customization of the solution.

[0101] The solution unit can estimate the traveler's emotions and prioritize solutions based on those emotions. For example, if the traveler is stressed, the solution unit will prioritize the quickest possible solution. If the traveler is relaxed, the solution unit will prioritize solutions that include detailed information. If the traveler is in a hurry, the solution unit will prioritize the most effective solution. By prioritizing solutions according to the traveler's emotions, the solution unit improves the traveler's satisfaction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using or without a generative AI. For example, the solution unit can input traveler emotion data into a generative AI and have the generative AI determine the priority of solutions.

[0102] The solution unit can prioritize presenting highly relevant solutions by considering the traveler's geographical location information during the resolution process. For example, if the traveler is in a specific region, the solution unit will prioritize presenting solutions available in that region. For example, if the traveler is in a tourist destination, the solution unit will prioritize presenting solutions available in that tourist destination. For example, if the traveler is in an urban area, the solution unit will prioritize presenting solutions available in the urban area. In this way, the solution unit can provide highly relevant solutions by considering the traveler's geographical location information. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the traveler's geographical location information into a generative AI and have the generative AI perform the task of presenting highly relevant solutions.

[0103] The solution unit can analyze the traveler's social media activity and present relevant solutions during the resolution process. For example, the solution unit can present the optimal solution based on information shared by the traveler on social media. For example, the solution unit can present solutions available in a specific region based on the traveler's social media activity. For example, the solution unit can analyze the traveler's social media activity and present the most suitable solution. In this way, the solution unit can provide relevant solutions by analyzing the traveler's social media activity. Some or all of the above processing in the solution unit may be performed using, for example, generative AI, or without generative AI. For example, the solution unit can input the traveler's social media activity data into a generative AI and have the generative AI perform the task of presenting relevant solutions.

[0104] The service provider can estimate the traveler's emotions and adjust how the integrated API is provided based on the estimated emotions. For example, if the traveler is stressed, the service provider might suggest a simple API delivery method. If the traveler is relaxed, the service provider might suggest a more detailed API delivery method. If the traveler is in a hurry, the service provider might suggest a quickly accessible API delivery method. This improves the traveler's convenience by adjusting how the integrated API is provided according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input traveler emotion data into a generative AI and have the generative AI adjust how the integrated API is provided.

[0105] The service provider can analyze a company's past usage history and select the optimal service delivery method at the time of delivery. For example, the service provider can propose the optimal service delivery method based on the API delivery methods the company has used in the past. For example, the service provider can propose a specific service delivery method based on the company's past usage history. For example, the service provider can analyze the company's past usage history and propose the most efficient service delivery method. In this way, the service provider can provide the optimal service delivery method by analyzing the company's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the company's past usage history data into a generative AI and have the generative AI select the optimal service delivery method.

[0106] The service provider can customize the service content based on the company's current needs at the time of delivery. For example, the service provider can propose the optimal API delivery method considering the company's current business needs. For example, the service provider can propose the most suitable API delivery method based on the company's current technology stack. For example, the service provider can propose the most effective API delivery method considering the company's current market conditions. In this way, the service provider can provide the optimal delivery method by customizing the service content based on the company's current needs. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the company's current needs data into a generative AI and have the generative AI perform the customization of the service content.

[0107] The service provider can estimate the traveler's emotions and determine the priority of providing integrated APIs based on the estimated emotions. For example, if the traveler is stressed, the service provider will prioritize providing the most readily available APIs. If the traveler is relaxed, the service provider will prioritize providing APIs containing detailed information. If the traveler is in a hurry, the service provider will prioritize providing the most effective APIs. This improves the traveler's convenience by allowing the service provider to prioritize integrated APIs according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using or without a generative AI. For example, the service provider can input traveler emotion data into a generative AI and have the generative AI determine the priority of providing integrated APIs.

[0108] The service provider can prioritize providing highly relevant APIs by considering the geographical location of the company at the time of provision. For example, if the company is in a specific region, the service provider will prioritize providing APIs available in that region. For example, if the company is in a tourist area, the service provider will prioritize providing APIs available in the tourist area. For example, if the company is in an urban area, the service provider will prioritize providing APIs available in the urban area. In this way, the service provider can provide highly relevant APIs by considering the geographical location of the company. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the geographical location of the company into a generative AI and have the generative AI perform the task of providing highly relevant APIs.

[0109] The service provider can analyze a company's social media activities and provide relevant APIs at the time of provision. For example, the service provider can provide the most suitable API based on information shared by a company on social media. For example, the service provider can provide APIs available in a specific region based on a company's social media activities. For example, the service provider can analyze a company's social media activities and provide the most suitable API. In this way, the service provider can provide relevant APIs by analyzing a company's social media activities. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input a company's social media activity data into a generative AI and have the generative AI perform the provision of relevant APIs.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The selection unit can choose the most suitable payment method considering the traveler's health condition. For example, if a traveler has health problems, the selection unit can link with a health management app to suggest a payment method appropriate to the traveler's health condition. If a traveler has diabetes, the selection unit can link with a health management app to suggest a payment method that offers benefits for diabetic patients. If a traveler has allergies, the selection unit can consider allergy information and suggest a payment method that can be used at allergy-friendly stores. In this way, the selection unit can improve the convenience and safety of travelers by providing the most suitable payment method according to their health condition.

[0112] The management department can learn travelers' preferences and customize how apps and virtual cards are managed based on those preferences. For example, if a traveler prefers a particular brand or service, the management department can prioritize the management of apps and virtual cards related to that brand or service. If a traveler frequently uses stores in a particular category, the management department can prioritize the management of apps and virtual cards related to that category. If a traveler frequently visits a particular region, the management department can prioritize the management of apps and virtual cards available in that region. In this way, the management department can improve the convenience of travelers by customizing how apps and virtual cards are managed based on their preferences.

[0113] The guidance department can analyze travelers' past reviews and ratings to recommend the most suitable establishments. For example, it can recommend similar establishments based on places travelers have given high ratings to in the past. It can also advise travelers to avoid places they have given low ratings to in the past. The guidance department can analyze reviews of establishments travelers have visited in the past to recommend the most suitable ones. In this way, the guidance department can recommend the most suitable establishments by analyzing travelers' past reviews and ratings.

[0114] The solution unit can present the optimal solution by considering the traveler's current network connection status. For example, if the traveler has an unstable network connection, the solution unit can present a solution that can be used offline. If the traveler has a stable network connection, the solution unit can present a solution that can be quickly implemented online. If the traveler's network connection is interrupted, the solution unit can present a solution that can be implemented after reconnection. In this way, the solution unit can provide the optimal solution according to the traveler's network connection status.

[0115] The service provider can customize how integrated APIs are delivered based on a company's business model. For example, if a company uses a subscription model, the service provider can propose an API delivery method optimized for subscriptions. If a company provides on-demand services, the service provider can propose an API delivery method optimized for on-demand services. If a company uses an advertising revenue model, the service provider can propose an API delivery method optimized for advertising revenue. In this way, the service provider can improve convenience for companies by customizing how integrated APIs are delivered based on their business model.

[0116] The selection system can estimate the traveler's emotions and adjust the payment method selection criteria based on those emotions. For example, if a traveler is feeling anxious, the system can prioritize suggesting reliable payment methods. If a traveler is relaxed, the system can prioritize suggesting payment methods with low fees. If a traveler is in a hurry, the system can prioritize suggesting payment methods that can be used quickly. In this way, the system can improve traveler satisfaction by selecting the most suitable payment method according to the traveler's emotions.

[0117] The management team can estimate the traveler's emotions and adjust the app and virtual card management methods based on those estimates. For example, if the traveler is stressed, the management team can provide a simple management interface. If the traveler is relaxed, the management team can provide detailed management options. If the traveler is in a hurry, the management team can provide a management method that can be accessed quickly. In this way, the management team can improve the traveler's convenience by adjusting the management methods according to the traveler's emotions.

[0118] The information desk can estimate the traveler's emotions and adjust the way information is displayed based on those emotions. For example, if the traveler is nervous, the information desk can provide a simple and highly visible display. If the traveler is relaxed, the information desk can provide a display that includes detailed information. If the traveler is in a hurry, the information desk can provide a display that gets straight to the point. In this way, the information desk can improve the traveler's convenience by adjusting the way information is displayed according to the traveler's emotions.

[0119] The solution unit can estimate the traveler's emotions and adjust how it presents solutions based on those emotions. For example, if the traveler is stressed, the solution unit can present a simple and easily understandable solution. If the traveler is relaxed, the solution unit can present a solution that includes more detailed information. If the traveler is in a hurry, the solution unit can present a solution that can be implemented quickly. In this way, the solution unit can improve the traveler's convenience by adjusting how it presents solutions according to the traveler's emotions.

[0120] The service provider can estimate the traveler's emotions and adjust how the integrated API is delivered based on those emotions. For example, if the traveler is stressed, the service provider can suggest a simple API delivery method. If the traveler is relaxed, the service provider can suggest a more detailed API delivery method. If the traveler is in a hurry, the service provider can suggest a quickly accessible API delivery method. This allows the service provider to improve traveler convenience by adjusting how the integrated API is delivered according to the traveler's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The selection unit automatically chooses the most suitable payment method for the traveler's destination. For example, it collects cashless payment information for each country and suggests the most suitable payment method for the traveler. If the traveler is going to Japan, it lists the cashless payment methods that are popular in Japan and automatically installs the necessary apps or virtual cards. Step 2: The management department centrally manages the payment methods selected by the selection department. For example, it centrally manages necessary apps and virtual cards, centrally manages information on apps and virtual cards used by travelers, and makes them easily accessible to travelers. Step 3: The information desk provides real-time information on stores that accept local currency or cashless payments, based on payment methods managed by the administration department. For example, it can determine the traveler's current location and guide them to nearby stores that accept cashless payments. If the traveler is in France, it will provide real-time information on nearby restaurants and shops that accept cashless payments. Step 4: The resolution unit provides quick solutions in the event of payment errors or fraudulent use. For example, upon detecting a payment error or fraudulent use, it immediately notifies the traveler and guides them through the process of suspending the transaction or providing alternative options. If fraudulent use is detected in Italy, the traveler is immediately notified and guided through the process of suspending the transaction or providing alternative options. Step 5: The service provider will provide an integrated API to enable businesses to develop payment environments for tourists. For example, it will provide an integrated API that enables businesses to develop payment environments for tourists using GlobalPay AI. Travel agencies and airlines can then use this API to provide cashless payment services for tourists.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the selection unit, management unit, guidance unit, solution unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and automatically selects the most suitable payment method for the traveler's destination. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and centrally manages the selected payment method. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14 and determines the traveler's current location and guides them to nearby stores that accept cashless payments. The solution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and quickly presents solutions in the event of payment errors or fraudulent use. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an integrated API for companies to develop a payment environment for tourists. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the selection unit, management unit, guidance unit, solution unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214 and automatically selects the most suitable payment method for the traveler's destination. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and centrally manages the selected payment method. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214 and determines the traveler's current location and guides them to nearby stores that accept cashless payments. The solution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and quickly presents solutions in the event of payment errors or fraudulent use. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an integrated API for companies to develop a payment environment for tourists. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the selection unit, management unit, guidance unit, resolution unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314 and automatically selects the most suitable payment method for the traveler's destination. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the selected payment method. The guidance unit is implemented by, for example, the control unit 46A of the headset terminal 314 and determines the traveler's current location and guides them to nearby stores that accept cashless payments. The resolution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and quickly presents solutions in the event of payment errors or fraudulent use. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an integrated API for companies to develop a payment environment for tourists. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the selection unit, management unit, guidance unit, solution unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414 and automatically selects the most suitable payment method for the traveler's destination. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the selected payment method. The guidance unit is implemented by, for example, the control unit 46A of the robot 414 and determines the traveler's current location and guides them to nearby stores that accept cashless payments. The solution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and quickly presents solutions in the event of payment errors or fraudulent use. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an integrated API for companies to develop a payment environment for tourists. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A selection unit that automatically selects the most suitable payment method for the traveler's destination, A management unit that centrally manages the payment methods selected by the aforementioned selection unit, An information unit provides real-time information on stores that accept local currency or cashless payments based on payment methods managed by the aforementioned management unit. A resolution department that provides quick solutions in the event of payment errors or fraudulent use, It includes a provisioning unit that provides an integrated API for companies to develop a payment environment for tourists. A system characterized by the following features. (Note 2) The aforementioned selection unit is We collect cashless payment information from various countries and propose the best payment methods for travelers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Centralized management of necessary apps and virtual cards The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is It tracks the traveler's current location and provides real-time information on nearby stores that accept cashless payments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned solution unit is If a payment error or fraudulent activity is detected, the traveler will be immediately notified and guided to suspend the transaction or provide alternative options. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide an integrated API to help businesses develop payment environments for tourists. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is We estimate the traveler's sentiment and adjust the criteria for selecting the optimal payment method based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is During the selection process, we analyze the traveler's past travel history and propose the most suitable payment method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is During the selection process, payment methods are customized based on the traveler's current financial situation and spending habits. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is The system estimates the traveler's sentiment and determines the priority of payment methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is During the selection process, priority will be given to selecting payment methods that are highly relevant, taking into account the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is During the selection process, we analyze travelers' social media activity and propose relevant payment methods. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, It estimates the traveler's emotions and adjusts how the app and virtual cards are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, During management, the traveler's past usage history is analyzed to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, During management, customize management methods based on the traveler's current device information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, It estimates the traveler's emotions and prioritizes apps and virtual cards based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, During management, the system prioritizes the management of highly relevant apps and virtual cards, taking into account the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, During management, we analyze travelers' social media activity and manage relevant apps and virtual cards. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the traveler's emotions and adjusts the way information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is When providing guidance, the system analyzes the traveler's past visit history to recommend the most suitable stores. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is When providing a tour, customize the tour content based on the traveler's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is It estimates the traveler's emotions and determines the priority of recommended stores based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is When providing directions, the system prioritizes recommending highly relevant stores, taking into account the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is During the tour, we analyze the traveler's social media activity and recommend relevant shops. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned solution unit is We estimate the traveler's emotions and adjust how solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned solution unit is When resolving an issue, we analyze past payment errors and fraudulent activity history to propose the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned solution unit is When resolving an issue, the solution is customized based on the traveler's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned solution unit is Estimate the traveler's emotions and prioritize solutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned solution unit is When resolving an issue, the system prioritizes presenting the most relevant solutions by considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned solution unit is When resolving an issue, analyze the traveler's social media activity and propose relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate travelers' sentiments and adjust how the integrated API is delivered based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, we analyze the company's past usage history and select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, At the time of delivery, the service will be customized based on the company's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the sentiment of travelers and determines the priority of providing integrated APIs based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing APIs, we prioritize providing the most relevant APIs, taking into account the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the service, we analyze the company's social media activity and provide relevant APIs. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection unit that automatically selects the most suitable payment method for the traveler's destination, A management unit that centrally manages the payment methods selected by the aforementioned selection unit, An information unit provides real-time information on stores that accept local currency or cashless payments based on payment methods managed by the aforementioned management unit. A resolution department that provides quick solutions in the event of payment errors or fraudulent use, It includes a provisioning unit that provides an integrated API for companies to develop a payment environment for tourists. A system characterized by the following features.

2. The aforementioned selection unit is We collect cashless payment information from various countries and propose the best payment methods for travelers. The system according to feature 1.

3. The aforementioned management department, Centralized management of necessary apps and virtual cards The system according to feature 1.

4. The aforementioned guide section is It tracks the traveler's current location and provides real-time information on nearby stores that accept cashless payments. The system according to feature 1.

5. The aforementioned solution unit is If a payment error or fraudulent activity is detected, the traveler will be immediately notified and guided to suspend the transaction or provide alternative options. The system according to feature 1.

6. The aforementioned supply unit is, We provide an integrated API to help businesses develop payment environments for tourists. The system according to feature 1.

7. The aforementioned selection unit is We estimate the traveler's sentiment and adjust the criteria for selecting the optimal payment method based on the estimated traveler's sentiment. The system according to feature 1.

8. The aforementioned selection unit is During the selection process, we analyze the traveler's past travel history and propose the most suitable payment method. The system according to feature 1.

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