system

Abstract

The system according to this embodiment aims to provide flight information within the airport, facilitate efficient movement, and support language and currency conversion. [Solution] The system according to the embodiment comprises a collection unit, a provision unit, a route calculation unit, a guidance unit, a conversion unit, and a currency conversion unit. The collection unit collects flight information. The provision unit provides the flight information collected by the collection unit. The route calculation unit calculates an efficient route based on map data of the airport. The guidance unit provides guidance along the efficient route calculated by the route calculation unit. The conversion unit performs language conversion. The currency conversion unit performs currency conversion.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation about a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 difficult to grasp flight information, move efficiently, and convert languages and currencies within an airport, and there is room for improvement.

[0005] The system according to the embodiment aims to assist in providing flight information, efficient movement, and language and currency conversion within an airport.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a provision unit, a route calculation unit, a guidance unit, a conversion unit, and a currency conversion unit. The collection unit collects flight information. The provision unit provides the flight information collected by the collection unit. The route calculation unit calculates an efficient route based on airport map data. The guidance unit provides guidance along the efficient route calculated by the route calculation unit. The conversion unit performs language conversion. The currency conversion unit performs currency conversion. [Effects of the Invention]

[0007] The system according to this embodiment can provide flight information within the airport, facilitate efficient movement, and support language and currency conversion. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An airport guidance support system according to an embodiment of the present invention is an AI assistant that provides guidance support within the airport. This airport guidance support system has a function to provide real-time flight information. This allows users to instantly obtain the latest flight information. Next, it has a function to guide users with a map of the airport and the shortest route to their destination. This allows users to reach their destination without getting lost in the airport. Furthermore, it has a function to support language conversion and currency conversion. This allows users to communicate in different languages ​​and currencies. First, if a user wants to obtain flight information, they enter the flight number into the AI ​​assistant. The AI ​​assistant collects flight information in real time and provides it to the user. For example, it can instantly inform the user of flight delay information or gate change information. Next, if a user wants to go to a destination within the airport, they enter the destination into the AI ​​assistant. The AI ​​assistant calculates the shortest route based on the airport map data and guides the user. For example, it can display the route to destinations such as check-in counters, boarding gates, and lounges. Furthermore, if a user wants to communicate in different languages ​​or currencies, they request language conversion or currency conversion from the AI ​​assistant. The AI ​​assistant analyzes the input information and converts it to the specified language or converts the currency. For example, it can convert Japanese yen to US dollars or translate Japanese to English. In this way, the AI ​​assistant of the present invention can improve the user's convenience within the airport by providing real-time flight information, airport maps and guidance on the shortest route to the destination, and support for language and currency conversion. As a result, the airport guidance support system can improve the user's convenience within the airport.

[0029] The airport guidance support system according to this embodiment comprises a collection unit, a provision unit, a route calculation unit, a guidance unit, a conversion unit, and a conversion unit. The collection unit collects flight information. The collection unit obtains flight information in real time, for example, by using an airline's API. The collection unit can also collect flight information in cooperation with the airport's information system. Furthermore, the collection unit can also collect flight information by scraping publicly available data on the internet. For example, the collection unit obtains flight information such as departure time, arrival time, and gate information using an airline's API. The provision unit provides the collected flight information to the user. The provision unit displays flight information, for example, through a smartphone application. The provision unit can also provide flight information using digital signage within the airport. Furthermore, the provision unit can also provide flight information through a voice assistant. For example, the provision unit notifies the user of flight delay information and gate change information through a smartphone application. The route calculation unit calculates the shortest route based on map data within the airport. The route calculation unit calculates the shortest route based on facility information and corridor information within the airport, for example. Furthermore, the route calculation unit can calculate the shortest route while considering real-time congestion information. It can also calculate the shortest route while considering the user's travel speed and physical condition. For example, the route calculation unit calculates the shortest route from the check-in counter to the boarding gate based on airport facility information. The guidance unit guides the user along the calculated shortest route. The guidance unit displays the route, for example, through a smartphone app. It can also guide the user using digital signage within the airport. Furthermore, it can guide the user through a voice assistant. For example, the guidance unit displays the route from the check-in counter to the boarding gate via a smartphone app. The conversion unit converts the input information into a specified language. The conversion unit performs language conversion, for example, using a text translation API. It can also convert voice input to text using speech recognition technology and translate that text into a specified language.Furthermore, the conversion unit can also perform real-time conversation translation. For example, the conversion unit can translate Japanese text into English using a text translation API. The conversion unit converts the input currency to a specified currency. The conversion unit can perform currency conversion using, for example, an exchange rate API. The conversion unit can also perform currency conversion based on historical exchange rate data. Furthermore, the conversion unit can perform real-time currency conversion. For example, the conversion unit can convert Japanese yen to US dollars using an exchange rate API. As a result, the airport guidance support system according to this embodiment can improve the convenience of users within the airport.

[0030] The data collection unit collects flight information. For example, it can obtain real-time flight information using airline APIs. It can also collect flight information by linking with airport information systems. Furthermore, it can collect flight information by scraping publicly available data on the internet. Specifically, it uses airline APIs to obtain flight information such as departure times, arrival times, and gate information. This allows the data collection unit to quickly and accurately collect the latest flight information. In addition, by linking with airport information systems, the data collection unit can obtain real-time operational status and gate change information within the airport. For example, airport information systems provide information such as flight delays, cancellations, and boarding gate changes. This allows the data collection unit to build a foundation for providing users with the latest flight information. Furthermore, by scraping publicly available data on the internet, it can also collect information from airline websites and flight tracking sites. This allows the data collection unit to collect data from multiple sources, improving the reliability and accuracy of the information. Finally, the data collection unit can centrally manage this data and link with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the data provisioning and route calculation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The service provider provides users with collected flight information. For example, the service provider displays flight information through a smartphone app. The service provider can also provide flight information using digital signage within the airport. Furthermore, the service provider can provide flight information through a voice assistant. Specifically, it notifies users of flight delays and gate changes via a smartphone app. This allows users to check the latest flight information in real time. For example, the smartphone app automatically retrieves the user's flight information and displays departure and arrival times, gate information, etc. In the event of flight delays or gate changes, the user can be immediately notified via push notification. Furthermore, by utilizing digital signage within the airport, flight information can be provided to a wide range of users. Digital signage is installed in key locations within the airport and displays flight information that is updated in real time. This allows users to check the latest flight information no matter where they are in the airport. Additionally, by providing flight information through a voice assistant, information can be provided to users with visual impairments or those whose hands are occupied. The voice assistant reads out flight information aloud in response to the user's voice commands. This allows the service provider to offer flight information to users in a variety of ways, thereby improving convenience.

[0032] The route calculation unit calculates the shortest route based on map data of the airport. For example, the route calculation unit calculates the shortest route based on information about airport facilities and pathways. The route calculation unit can also calculate the shortest route considering real-time congestion information. Furthermore, the route calculation unit can also calculate the shortest route considering the user's movement speed and physical condition. Specifically, it calculates the shortest route from the check-in counter to the boarding gate based on information about airport facilities. This allows users to move efficiently within the airport. For example, the route calculation unit uses map data of the airport to understand the location of each facility and the layout of pathways, and calculates the optimal route. It can also suggest routes that avoid congestion by considering real-time congestion information. This allows users to move smoothly while avoiding congestion. Furthermore, by considering the user's movement speed and physical condition, the route calculation unit can provide the optimal route for each individual user. For example, it can suggest routes that utilize escalators and elevators for users with slow movement speeds, such as the elderly or people with disabilities. It can also suggest routes that allow users to reach their destination in the shortest time for users who are in a hurry. In this way, the route calculation unit can perform flexible route calculations according to the user's needs and support movement within the airport.

[0033] The information desk guides users along the calculated shortest route. The information desk displays the route, for example, through a smartphone app. It can also provide route guidance using digital signage within the airport. Furthermore, it can provide route guidance through a voice assistant. Specifically, it displays the route from the check-in counter to the boarding gate via a smartphone app. This allows users to visually confirm the route as they move. For example, the smartphone app uses GPS to determine the user's current location and updates the route in real time. It also displays information on important points and facilities along the route, ensuring users reach their destination without getting lost. Additionally, using digital signage within the airport allows route guidance to be provided to a wide range of users. Digital signage is installed in key locations within the airport and displays real-time updated route information. This allows users to check the route no matter where they are in the airport. Furthermore, providing route guidance through a voice assistant makes it possible to provide route guidance to visually impaired users and users whose hands are occupied. The voice assistant reads out route information aloud in response to the user's voice commands. This allows the navigation system to provide users with route guidance in a variety of ways, thereby improving convenience.

[0034] The conversion unit converts input information into a specified language. For example, it uses a text translation API to perform language conversion. It can also use speech recognition technology to convert voice input into text and then translate that text into a specified language. Furthermore, the conversion unit can perform real-time conversation translation. Specifically, it uses a text translation API to translate Japanese text into English. This allows users to obtain information across language barriers. For example, the conversion unit automatically detects text entered by the user and translates it into a specified language. It can also use speech recognition technology to convert what the user says into text and then translate that text into a specified language. This allows users to easily perform language conversion through voice input. Furthermore, by performing real-time conversation translation, the conversion unit supports communication between users who speak different languages. For example, it can translate what a user says in Japanese into English in real time and convey it to the other party. In this way, the conversion unit provides users with diverse language conversion functions, facilitating smooth communication within the airport.

[0035] The conversion unit converts the entered currency to the specified currency. For example, the conversion unit uses an exchange rate API to perform currency conversions. It can also perform currency conversions based on historical exchange rate data. Furthermore, the conversion unit can perform real-time currency conversions. Specifically, it uses an exchange rate API to convert Japanese yen to US dollars. This allows users to convert currencies based on the latest exchange rates. For example, the conversion unit automatically detects the amount entered by the user and converts it to the specified currency. It can also use historical exchange rate data to perform conversions based on average, highest, and lowest rates over a specific period. This allows users to convert currencies by referring to past exchange rates. Furthermore, by performing real-time currency conversions, the conversion unit provides accurate conversion results based on the latest exchange rates. For example, when a user converts Japanese yen to US dollars, it can obtain the latest exchange rate and display the conversion result immediately. This allows the conversion unit to provide users with accurate and rapid currency conversion functionality, improving convenience within the airport.

[0036] The data collection unit can collect flight information in real time. For example, the data collection unit can obtain flight information in real time using an airline's API. The data collection unit can also collect flight information in conjunction with airport information systems. Furthermore, the data collection unit can collect flight information by scraping publicly available data on the internet. This allows for the provision of the latest information by collecting flight information in real time. Specific definitions and criteria of "real time" include, for example, update frequencies in seconds or minutes. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input flight information obtained from an airline's API into a generating AI, which can then analyze the information in real time and provide the latest flight information.

[0037] The service provider can provide the collected flight information to the user. For example, the service provider can display the flight information through a smartphone app. The service provider can also provide flight information using digital signage in the airport. Furthermore, the service provider can provide flight information through a voice assistant. By providing the collected flight information to the user, the user can obtain the latest flight information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the collected flight information into a generating AI, which can analyze the information and provide it to the user in the most optimal format.

[0038] The route calculation unit can calculate the shortest route based on map data within the airport. For example, the route calculation unit calculates the shortest route based on facility information and pathway information within the airport. The route calculation unit can also calculate the shortest route considering real-time congestion information. Furthermore, the route calculation unit can calculate the shortest route considering the user's travel speed and physical condition. As a result, by calculating the shortest route based on map data within the airport, users can reach their destination efficiently. Specific criteria and calculation methods for the shortest route include, for example, reducing distance and travel time. Some or all of the above processing in the route calculation unit may be performed using AI, or not using AI. For example, the route calculation unit can input map data within the airport into a generating AI, which can then calculate the shortest route and provide it to the user.

[0039] The guidance unit can guide the user along the calculated shortest route. The guidance unit can display the route, for example, through a smartphone app. It can also guide the user along the route using digital signage within the airport. Furthermore, it can guide the user along the route through a voice assistant. By guiding the user along the calculated shortest route, the user can reach their destination without getting lost. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the calculated shortest route into a generating AI, which can then provide route guidance.

[0040] The conversion unit can convert input information into a specified language. For example, the conversion unit can perform language conversion using a text translation API. It can also convert voice input into text using speech recognition technology and then translate that text into a specified language. Furthermore, the conversion unit can perform real-time conversation translation. This allows users to communicate in different languages ​​by converting input information into a specified language. Specific examples of specified languages ​​include English, French, and Spanish. Some or all of the above-described processes in the conversion unit may be performed using AI, or without AI. For example, the conversion unit can input the input information into a generating AI, which can then convert it into a specified language.

[0041] The conversion unit can convert the input currency to a specified currency. The conversion unit can perform currency conversion using, for example, an exchange rate API. It can also perform currency conversion based on historical exchange rate data. Furthermore, the conversion unit can perform currency conversion in real time. This allows users to handle different currencies by converting the input currency to a specified currency. Specific types of specified currencies include, for example, USD, EUR, JPY, etc. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the input currency into a generating AI, which can then convert it to a specified currency.

[0042] The data collection unit can analyze past flight information collection history and select the most efficient collection method. For example, the data collection unit can prioritize collecting information on airlines that frequently experience delays based on past flight information collection history. The data collection unit can also adjust the frequency of flight information collection during specific time periods based on past collection history. Furthermore, the data collection unit can analyze past collection history and prioritize collecting information on airlines and routes frequently used by the user. This enables more efficient collection of flight information by analyzing past collection history. Specific criteria and methods for determining the most efficient collection method include, for example, data acquisition speed and accuracy. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past flight information collection history into a generating AI, which can then select the optimal collection method.

[0043] The data collection unit can filter flight information based on the user's current travel plans and areas of interest. For example, the data collection unit can collect only relevant flight information based on the user's travel plans. The data collection unit can also prioritize the collection of relevant flight information based on the user's areas of interest (e.g., business travel or sightseeing). Furthermore, the data collection unit can filter and collect flight information related to a specific destination based on the user's travel plans. This allows for the provision of highly relevant information by filtering flight information based on the user's travel plans and areas of interest. Specific criteria and methods for filtering include, for example, travel plan details and areas of interest categories. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's travel plans and areas of interest into a generating AI, which can then perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting flight information. For example, the data collection unit can prioritize the collection of flight information for the airport where the user is currently located. It can also prioritize the collection of flight information for the airport closest to the user's current location. Furthermore, the data collection unit can filter and collect the most relevant flight information based on the user's current location. This allows for the provision of highly relevant flight information by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS coordinates and address information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant flight information.

[0045] The data collection unit can analyze the user's social media activity and collect relevant information when collecting flight information. For example, the data collection unit can collect information about travel destinations and airlines of interest from the user's social media posts. The data collection unit can also collect flight information shared by the user's social media followers and friends. Furthermore, the data collection unit can analyze the user's social media activity and prioritize the collection of relevant flight information. This allows for the provision of highly relevant flight information by analyzing the user's social media activity. Specific types of social media activity and methods of acquisition include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0046] The information provider can adjust the level of detail provided based on the importance of the flight information at the time of provision. For example, the provider can provide detailed information on flight delays and cancellations. Alternatively, it can provide regular flight information concisely. Furthermore, it can provide information on gate changes and boarding times quickly. By adjusting the level of detail based on the importance of the flight information, the provider can appropriately provide information that is important to the user. Specific criteria and adjustment methods for level of detail include, for example, the granularity of the information and the number of displayed items. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the importance of the flight information into a generating AI, which can then adjust the level of detail provided.

[0047] The information provider can apply different information provision algorithms depending on the category of flight information at the time of provision. For example, the information provider can update and provide flight delay information in real time. It can also provide gate change information quickly and notify users. Furthermore, it can update and provide regular flight information periodically. By applying a information provision algorithm according to the category of flight information, it becomes possible to provide more appropriate information. Specific types and implementation methods of the information provision algorithms include, for example, recommendation algorithms and filtering algorithms. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the category of flight information into a generating AI, and the generating AI can apply an appropriate information provision algorithm.

[0048] The information provider can determine the priority of information provision based on the timing of flight information submission. For example, the information provider can prioritize the provision of flight delay and cancellation information. It can also provide gate change information promptly. Furthermore, it can provide regular flight information on a regular basis. By prioritizing information provision based on the timing of flight information submission, important information can be provided preferentially. Specific criteria and methods for determining the submission timing include, for example, before the flight departure or after arrival. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the timing of flight information submission into a generating AI, which can then determine the priority of information provision.

[0049] The information provider can adjust the order of flight information delivery based on its relevance. For example, it may prioritize providing information relevant to the user's current flight. It can also provide relevant flight information based on the user's travel plan. Furthermore, it can provide relevant flight information based on the user's areas of interest. This allows the information important to the user to be prioritized by adjusting the order of delivery based on the relevance of the flight information. Specific criteria and evaluation methods for relevance include, for example, the user's areas of interest and past behavioral history. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the relevance of flight information into a generating AI, which can then adjust the order of delivery.

[0050] The route calculation unit can calculate the most efficient route while considering the congestion situation within the airport. For example, the route calculation unit calculates the optimal route based on the real-time congestion situation within the airport. The route calculation unit can also calculate detour routes to avoid congestion. Furthermore, the route calculation unit can suggest the optimal route to the user, taking congestion into consideration. This allows users to use the optimal route that avoids congestion by considering the congestion situation within the airport. Specific evaluation criteria and acquisition methods for congestion include, for example, population density and real-time pedestrian flow data. Some or all of the above processing in the route calculation unit may be performed using, for example, AI, or not using AI. For example, the route calculation unit can input airport congestion data into a generating AI, which can then calculate the optimal route.

[0051] The route calculation unit can customize the most efficient route by considering the user's attribute information during route calculation. For example, the route calculation unit can calculate the optimal route according to the user's age and physical strength. It can also calculate the optimal route according to the amount of luggage the user is carrying. Furthermore, the route calculation unit can calculate the optimal route according to the user's special needs (such as wheelchair use). In this way, by considering the user's attribute information, the optimal route can be provided to each individual user. Specific types of attribute information and methods of acquisition include, for example, age, gender, and physical characteristics. Some or all of the above processing in the route calculation unit may be performed using AI, for example, or without AI. For example, the route calculation unit can input the user's attribute information into a generating AI, which can then customize the optimal route.

[0052] The route calculation unit can calculate the most efficient route by considering information about facilities within the airport. For example, the route calculation unit can calculate the optimal route by considering the locations of toilets and rest areas within the airport. It can also calculate the optimal route by considering the locations of shops and restaurants within the airport. Furthermore, it can calculate the optimal route by considering the locations of security checkpoints within the airport. This allows users to use routes that make it easier to access the necessary facilities by considering information about facilities within the airport. Specific types of facility information and methods of acquisition include, for example, location information for restaurants, toilets, and shops. Some or all of the above processing in the route calculation unit may be performed using AI, or not. For example, the route calculation unit can input information about facilities within the airport into a generating AI, which can then calculate the optimal route.

[0053] The route calculation unit can propose the most efficient route by referring to the user's past travel history during route calculation. For example, the route calculation unit can propose the optimal route based on routes previously used by the user. It can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the route calculation unit can analyze the user's past travel history and propose the most efficient route. This allows for the provision of more efficient routes by referring to the user's past travel history. Specific methods for acquiring and using past travel history include, for example, GPS data and past route information. Some or all of the above-described processes in the route calculation unit may be performed using, for example, AI, or without AI. For example, the route calculation unit can input the user's past travel history into a generating AI, which can then propose the optimal route.

[0054] The guidance unit can select the most efficient display method by referring to the user's past guidance history when providing guidance. For example, the guidance unit can provide the optimal display method based on the display method the user has preferred to use in the past. The guidance unit can also prioritize providing a specific display method based on the user's past guidance history. Furthermore, the guidance unit can analyze the user's past guidance history and propose the most efficient display method. This allows for the provision of a more appropriate display method by referring to the user's past guidance history. Specific methods for acquiring and using guidance history include, for example, past guidance content and user feedback. Some or all of the above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input the user's past guidance history into a generating AI, which can then select the optimal display method.

[0055] The guidance unit can update the guidance in real time based on the user's current location information. For example, the guidance unit can update the user's current location in real time while the user is moving and provide guidance. The guidance unit can also update the user's current location in real time as the user approaches their destination and suggest the optimal route. Furthermore, if the user gets lost, the guidance unit can update the current location in real time and provide guidance again. This makes it possible to provide more appropriate information by updating the guidance in real time based on the user's current location information. Specific update frequencies and methods for updating the guidance in real time include, for example, updates every second or every minute. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's current location information into a generating AI, which can then update the guidance in real time.

[0056] The guidance unit can select the most efficient display method when providing guidance, taking into account the user's device information. For example, if the user is using a smartphone, the guidance unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the guidance unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the guidance unit can provide a concise and highly visible display method. This allows for the provision of a more appropriate display method by considering the user's device information. Specific types of device information and methods of acquisition include, for example, smartphones, tablets, and wearable devices. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's device information into a generating AI, which can then select the optimal display method.

[0057] The guidance unit can customize the content of the guidance based on the user's language settings. For example, the guidance unit can automatically set the language of the guidance based on the language settings of the user's device. The guidance unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the guidance unit selects a specific language, it can provide guidance in that language. This allows for the provision of more appropriate information by customizing the content of the guidance based on the user's language settings. Specific methods for obtaining and using language settings include, for example, device settings and user selection. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's language settings into a generating AI, which can then customize the content of the guidance.

[0058] The conversion unit can select the most efficient conversion method by referring to the user's past conversion history during language conversion. For example, the conversion unit can provide the optimal conversion method based on the language conversion methods the user has used in the past. The conversion unit can also prioritize providing specific expression methods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and propose the most efficient conversion method. This allows for the provision of a more appropriate conversion method by referring to the user's past conversion history. Specific methods for obtaining and using the conversion history include, for example, past conversion content and user feedback. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's past conversion history into a generating AI, which can then select the optimal conversion method.

[0059] The conversion unit can customize the conversion process by considering the user's attribute information during language conversion. For example, the conversion unit can provide the optimal conversion method according to the user's age and language level. It can also provide a conversion method that includes specialized terminology according to the user's field of expertise. Furthermore, the conversion unit can provide the optimal conversion method according to the user's special needs (for example, speech conversion for the visually impaired). This allows for the provision of a more appropriate conversion method by considering the user's attribute information. Specific types of attribute information and methods of acquisition include, for example, age, gender, and language ability. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's attribute information into a generating AI, which can then customize the optimal conversion method.

[0060] The conversion unit can select the most efficient conversion method during language conversion, taking into account the user's geographical location information. For example, the conversion unit can provide the optimal conversion method based on the language of the country or region where the user is currently located. The conversion unit can also prioritize conversion to the language most relevant to the user's current location. Furthermore, the conversion unit can provide the most relevant language conversion based on the user's current location. This allows for the provision of a more appropriate conversion method by considering the user's geographical location information. Specific types and methods of obtaining geographical location information include, for example, GPS coordinates and address information. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's geographical location information into a generating AI, which can then select the optimal conversion method.

[0061] The conversion unit can improve the accuracy of language conversion by analyzing the user's social media activity. For example, the conversion unit can analyze frequently used expressions and language from the user's social media posts to improve conversion accuracy. The conversion unit can also analyze the languages ​​used by the user's social media followers and friends to provide the optimal conversion method. Furthermore, the conversion unit can analyze the user's social media activity and prioritize providing relevant language conversions. This allows for the provision of more appropriate conversion methods by analyzing the user's social media activity. Specific types and methods of acquiring social media activity include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's social media activity data into a generating AI, which can then select the optimal conversion method.

[0062] The conversion unit can select the most efficient conversion method when converting currencies by referring to the user's past conversion history. For example, the conversion unit can provide the optimal conversion method based on the currency conversion methods the user has used in the past. The conversion unit can also prioritize providing a specific conversion method based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and propose the most efficient conversion method. This allows for the provision of a more appropriate conversion method by referring to the user's past conversion history. Specific methods for obtaining and using the conversion history include, for example, past conversion details and user feedback. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's past conversion history into a generating AI, which can then select the optimal conversion method.

[0063] The conversion unit can customize currency conversions by taking into account the user's attribute information. For example, the conversion unit can provide the optimal currency conversion method according to the user's age and economic situation. It can also provide the optimal currency conversion method according to the user's travel destination. Furthermore, the conversion unit can provide the optimal currency conversion method according to the user's special needs (for example, detailed conversion information for business travelers). This allows for the provision of a more appropriate conversion method by considering the user's attribute information. Specific types of attribute information and methods of acquisition include, for example, age, gender, and economic situation. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the user's attribute information into a generating AI, which can then customize the optimal conversion method.

[0064] The conversion unit can select the most efficient conversion method when converting currencies, taking into account the user's geographical location information. For example, the conversion unit can provide the optimal conversion method based on the currency of the country or region where the user is currently located. The conversion unit can also prioritize conversion to the currency most relevant to the user's current location. Furthermore, the conversion unit can provide the most relevant currency conversion based on the user's current location. This allows for the provision of a more appropriate conversion method by considering the user's geographical location information. Specific types and methods of obtaining geographical location information include, for example, GPS coordinates and address information. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's geographical location information into a generating AI, which can then select the optimal conversion method.

[0065] The conversion unit can improve the accuracy of currency conversions by analyzing the user's social media activity. For example, the conversion unit can analyze frequently used currencies and conversion methods from the user's social media posts to improve conversion accuracy. The conversion unit can also analyze the currencies used by the user's social media followers and friends and provide the optimal conversion method. Furthermore, the conversion unit can analyze the user's social media activity and prioritize providing relevant currency conversions. This allows for the provision of more appropriate conversion methods by analyzing the user's social media activity. Specific types and methods of acquiring social media activity include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's social media activity data into a generating AI, which can then select the optimal conversion method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The airport guidance support system can also be equipped with a health monitoring unit that monitors the user's health status. For example, the health monitoring unit can measure the user's heart rate and blood pressure, and if an abnormality is detected, it can send a notification prompting emergency action. The health monitoring unit can also monitor the user's walking speed and posture, detecting signs of fatigue and stress. Furthermore, based on the user's health data, the health monitoring unit can guide the user to the optimal route to rest areas and medical facilities within the airport. This allows for real-time monitoring of the user's health status and the provision of necessary support, thereby improving safety and comfort within the airport.

[0068] The airport information support system can also include a baggage tracking unit that provides tracking information for the user's luggage. For example, the baggage tracking unit can track the location of the user's luggage in real time and notify the user via a smartphone app. Furthermore, the baggage tracking unit can provide support information to help users respond quickly if their luggage is delayed or lost. In addition, the baggage tracking unit can guide the user to the location and time of luggage pickup. This allows users to always know the status of their luggage and continue their trip with peace of mind.

[0069] The airport information support system can further analyze the user's past behavioral history to provide personalized services. For example, it can provide information on the most suitable flights and destinations based on data from past flights and places visited. It can also recommend restaurants that match the user's preferences based on their past dining history. Furthermore, it can provide information on products and stores that the user might be interested in based on their past shopping history. In this way, it can leverage the user's past behavioral history to provide even more personalized services.

[0070] The airport information support system can further consider the user's device information to select the most suitable notification method. For example, if the user is using a smartphone, information can be provided via push notifications. If the user is using a smartwatch, information can be provided via vibration or voice notifications. Furthermore, if the user is using a tablet, a notification method optimized for the larger screen can be provided. In this way, by considering the user's device information, a more effective notification method can be provided.

[0071] The airport information support system can further consider the user's travel plans to provide optimal flight information. For example, if the user is planning a business trip, it can provide business class flight information and conference room reservation information. If the user is planning a sightseeing trip, it can provide direct flights to tourist destinations and information on tour guides. Furthermore, if the user is planning a family trip, it can provide family-friendly flight information and information on kids' areas. In this way, by providing optimal flight information tailored to the user's travel plans, the system can help them plan their trip smoothly.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects flight information. The collection unit obtains flight information in real time using airline APIs. It can also collect flight information by linking with airport information systems. Furthermore, it can collect flight information by scraping publicly available data on the internet. For example, it can obtain flight information such as departure time, arrival time, and gate information. Step 2: The service provider provides the collected flight information to the user. The service provider displays the flight information through a smartphone app. Flight information can also be provided using digital signage within the airport. Furthermore, flight information can be provided through a voice assistant. For example, it can notify the user of flight delays or gate changes. Step 3: The route calculation unit calculates the shortest route based on airport map data. The route calculation unit calculates the shortest route based on airport facility information and pathway information. It can also calculate the shortest route considering real-time congestion information. Furthermore, it can calculate the shortest route considering the user's movement speed and physical condition. For example, it can calculate the shortest route from the check-in counter to the boarding gate. Step 4: The information desk guides the user to the calculated shortest route. The information desk displays the route via a smartphone app. It can also guide users to the route using digital signage within the airport. Furthermore, it can guide users to the route via a voice assistant. For example, it can display the route from the check-in counter to the boarding gate. Step 5: The conversion unit converts the input information into the specified language. The conversion unit uses a text translation API to perform language conversion. It can also use speech recognition technology to convert voice input into text and then translate that text into the specified language. Furthermore, it can perform real-time conversation translation. For example, translating Japanese text into English. Step 6: The conversion unit converts the entered currency to the specified currency. The conversion unit uses an exchange rate API to perform currency conversion. It can also perform currency conversion based on historical exchange rate data. Furthermore, it can perform currency conversion in real time. For example, convert Japanese Yen to US Dollars.

[0074] (Example of form 2) An airport guidance support system according to an embodiment of the present invention is an AI assistant that provides guidance support within the airport. This airport guidance support system has a function to provide real-time flight information. This allows users to instantly obtain the latest flight information. Next, it has a function to guide users with a map of the airport and the shortest route to their destination. This allows users to reach their destination without getting lost in the airport. Furthermore, it has a function to support language conversion and currency conversion. This allows users to communicate in different languages ​​and currencies. First, if a user wants to obtain flight information, they enter the flight number into the AI ​​assistant. The AI ​​assistant collects flight information in real time and provides it to the user. For example, it can instantly inform the user of flight delay information or gate change information. Next, if a user wants to go to a destination within the airport, they enter the destination into the AI ​​assistant. The AI ​​assistant calculates the shortest route based on the airport map data and guides the user. For example, it can display the route to destinations such as check-in counters, boarding gates, and lounges. Furthermore, if a user wants to communicate in different languages ​​or currencies, they request language conversion or currency conversion from the AI ​​assistant. The AI ​​assistant analyzes the input information and converts it to the specified language or converts the currency. For example, it can convert Japanese yen to US dollars or translate Japanese to English. In this way, the AI ​​assistant of the present invention can improve the user's convenience within the airport by providing real-time flight information, airport maps and guidance on the shortest route to the destination, and support for language and currency conversion. As a result, the airport guidance support system can improve the user's convenience within the airport.

[0075] The airport guidance support system according to this embodiment comprises a collection unit, a provision unit, a route calculation unit, a guidance unit, a conversion unit, and a conversion unit. The collection unit collects flight information. The collection unit obtains flight information in real time, for example, by using an airline's API. The collection unit can also collect flight information in cooperation with the airport's information system. Furthermore, the collection unit can also collect flight information by scraping publicly available data on the internet. For example, the collection unit obtains flight information such as departure time, arrival time, and gate information using an airline's API. The provision unit provides the collected flight information to the user. The provision unit displays flight information, for example, through a smartphone application. The provision unit can also provide flight information using digital signage within the airport. Furthermore, the provision unit can also provide flight information through a voice assistant. For example, the provision unit notifies the user of flight delay information and gate change information through a smartphone application. The route calculation unit calculates the shortest route based on map data within the airport. The route calculation unit calculates the shortest route based on facility information and corridor information within the airport, for example. Furthermore, the route calculation unit can calculate the shortest route while considering real-time congestion information. It can also calculate the shortest route while considering the user's travel speed and physical condition. For example, the route calculation unit calculates the shortest route from the check-in counter to the boarding gate based on airport facility information. The guidance unit guides the user along the calculated shortest route. The guidance unit displays the route, for example, through a smartphone app. It can also guide the user using digital signage within the airport. Furthermore, it can guide the user through a voice assistant. For example, the guidance unit displays the route from the check-in counter to the boarding gate via a smartphone app. The conversion unit converts the input information into a specified language. The conversion unit performs language conversion, for example, using a text translation API. It can also convert voice input to text using speech recognition technology and translate that text into a specified language.Furthermore, the conversion unit can also perform real-time conversation translation. For example, the conversion unit can translate Japanese text into English using a text translation API. The conversion unit converts the input currency to a specified currency. The conversion unit can perform currency conversion using, for example, an exchange rate API. The conversion unit can also perform currency conversion based on historical exchange rate data. Furthermore, the conversion unit can perform real-time currency conversion. For example, the conversion unit can convert Japanese yen to US dollars using an exchange rate API. As a result, the airport guidance support system according to this embodiment can improve the convenience of users within the airport.

[0076] The data collection unit collects flight information. For example, it can obtain real-time flight information using airline APIs. It can also collect flight information by linking with airport information systems. Furthermore, it can collect flight information by scraping publicly available data on the internet. Specifically, it uses airline APIs to obtain flight information such as departure times, arrival times, and gate information. This allows the data collection unit to quickly and accurately collect the latest flight information. In addition, by linking with airport information systems, the data collection unit can obtain real-time operational status and gate change information within the airport. For example, airport information systems provide information such as flight delays, cancellations, and boarding gate changes. This allows the data collection unit to build a foundation for providing users with the latest flight information. Furthermore, by scraping publicly available data on the internet, it can also collect information from airline websites and flight tracking sites. This allows the data collection unit to collect data from multiple sources, improving the reliability and accuracy of the information. Finally, the data collection unit can centrally manage this data and link with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the data provisioning and route calculation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The service provider provides users with collected flight information. For example, the service provider displays flight information through a smartphone app. The service provider can also provide flight information using digital signage within the airport. Furthermore, the service provider can provide flight information through a voice assistant. Specifically, it notifies users of flight delays and gate changes via a smartphone app. This allows users to check the latest flight information in real time. For example, the smartphone app automatically retrieves the user's flight information and displays departure and arrival times, gate information, etc. In the event of flight delays or gate changes, the user can be immediately notified via push notification. Furthermore, by utilizing digital signage within the airport, flight information can be provided to a wide range of users. Digital signage is installed in key locations within the airport and displays flight information that is updated in real time. This allows users to check the latest flight information no matter where they are in the airport. Additionally, by providing flight information through a voice assistant, information can be provided to users with visual impairments or those whose hands are occupied. The voice assistant reads out flight information aloud in response to the user's voice commands. This allows the service provider to offer flight information to users in a variety of ways, thereby improving convenience.

[0078] The route calculation unit calculates the shortest route based on map data of the airport. For example, the route calculation unit calculates the shortest route based on information about airport facilities and pathways. The route calculation unit can also calculate the shortest route considering real-time congestion information. Furthermore, the route calculation unit can also calculate the shortest route considering the user's movement speed and physical condition. Specifically, it calculates the shortest route from the check-in counter to the boarding gate based on information about airport facilities. This allows users to move efficiently within the airport. For example, the route calculation unit uses map data of the airport to understand the location of each facility and the layout of pathways, and calculates the optimal route. It can also suggest routes that avoid congestion by considering real-time congestion information. This allows users to move smoothly while avoiding congestion. Furthermore, by considering the user's movement speed and physical condition, the route calculation unit can provide the optimal route for each individual user. For example, it can suggest routes that utilize escalators and elevators for users with slow movement speeds, such as the elderly or people with disabilities. It can also suggest routes that allow users to reach their destination in the shortest time for users who are in a hurry. In this way, the route calculation unit can perform flexible route calculations according to the user's needs and support movement within the airport.

[0079] The information desk guides users along the calculated shortest route. The information desk displays the route, for example, through a smartphone app. It can also provide route guidance using digital signage within the airport. Furthermore, it can provide route guidance through a voice assistant. Specifically, it displays the route from the check-in counter to the boarding gate via a smartphone app. This allows users to visually confirm the route as they move. For example, the smartphone app uses GPS to determine the user's current location and updates the route in real time. It also displays information on important points and facilities along the route, ensuring users reach their destination without getting lost. Additionally, using digital signage within the airport allows route guidance to be provided to a wide range of users. Digital signage is installed in key locations within the airport and displays real-time updated route information. This allows users to check the route no matter where they are in the airport. Furthermore, providing route guidance through a voice assistant makes it possible to provide route guidance to visually impaired users and users whose hands are occupied. The voice assistant reads out route information aloud in response to the user's voice commands. This allows the navigation system to provide users with route guidance in a variety of ways, thereby improving convenience.

[0080] The conversion unit converts input information into a specified language. For example, it uses a text translation API to perform language conversion. It can also use speech recognition technology to convert voice input into text and then translate that text into a specified language. Furthermore, the conversion unit can perform real-time conversation translation. Specifically, it uses a text translation API to translate Japanese text into English. This allows users to obtain information across language barriers. For example, the conversion unit automatically detects text entered by the user and translates it into a specified language. It can also use speech recognition technology to convert what the user says into text and then translate that text into a specified language. This allows users to easily perform language conversion through voice input. Furthermore, by performing real-time conversation translation, the conversion unit supports communication between users who speak different languages. For example, it can translate what a user says in Japanese into English in real time and convey it to the other party. In this way, the conversion unit provides users with diverse language conversion functions, facilitating smooth communication within the airport.

[0081] The conversion unit converts the entered currency to the specified currency. For example, the conversion unit uses an exchange rate API to perform currency conversions. It can also perform currency conversions based on historical exchange rate data. Furthermore, the conversion unit can perform real-time currency conversions. Specifically, it uses an exchange rate API to convert Japanese yen to US dollars. This allows users to convert currencies based on the latest exchange rates. For example, the conversion unit automatically detects the amount entered by the user and converts it to the specified currency. It can also use historical exchange rate data to perform conversions based on average, highest, and lowest rates over a specific period. This allows users to convert currencies by referring to past exchange rates. Furthermore, by performing real-time currency conversions, the conversion unit provides accurate conversion results based on the latest exchange rates. For example, when a user converts Japanese yen to US dollars, it can obtain the latest exchange rate and display the conversion result immediately. This allows the conversion unit to provide users with accurate and rapid currency conversion functionality, improving convenience within the airport.

[0082] The data collection unit can collect flight information in real time. For example, the data collection unit can obtain flight information in real time using an airline's API. The data collection unit can also collect flight information in conjunction with airport information systems. Furthermore, the data collection unit can collect flight information by scraping publicly available data on the internet. This allows for the provision of the latest information by collecting flight information in real time. Specific definitions and criteria of "real time" include, for example, update frequencies in seconds or minutes. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input flight information obtained from an airline's API into a generating AI, which can then analyze the information in real time and provide the latest flight information.

[0083] The service provider can provide the collected flight information to the user. For example, the service provider can display the flight information through a smartphone app. The service provider can also provide flight information using digital signage in the airport. Furthermore, the service provider can provide flight information through a voice assistant. By providing the collected flight information to the user, the user can obtain the latest flight information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the collected flight information into a generating AI, which can analyze the information and provide it to the user in the most optimal format.

[0084] The route calculation unit can calculate the shortest route based on map data within the airport. For example, the route calculation unit calculates the shortest route based on facility information and pathway information within the airport. The route calculation unit can also calculate the shortest route considering real-time congestion information. Furthermore, the route calculation unit can calculate the shortest route considering the user's travel speed and physical condition. As a result, by calculating the shortest route based on map data within the airport, users can reach their destination efficiently. Specific criteria and calculation methods for the shortest route include, for example, reducing distance and travel time. Some or all of the above processing in the route calculation unit may be performed using AI, or not using AI. For example, the route calculation unit can input map data within the airport into a generating AI, which can then calculate the shortest route and provide it to the user.

[0085] The guidance unit can guide the user along the calculated shortest route. The guidance unit can display the route, for example, through a smartphone app. It can also guide the user along the route using digital signage within the airport. Furthermore, it can guide the user along the route through a voice assistant. By guiding the user along the calculated shortest route, the user can reach their destination without getting lost. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the calculated shortest route into a generating AI, which can then provide route guidance.

[0086] The conversion unit can convert input information into a specified language. For example, the conversion unit can perform language conversion using a text translation API. It can also convert voice input into text using speech recognition technology and then translate that text into a specified language. Furthermore, the conversion unit can perform real-time conversation translation. This allows users to communicate in different languages ​​by converting input information into a specified language. Specific examples of specified languages ​​include English, French, and Spanish. Some or all of the above-described processes in the conversion unit may be performed using AI, or without AI. For example, the conversion unit can input the input information into a generating AI, which can then convert it into a specified language.

[0087] The conversion unit can convert the input currency to a specified currency. The conversion unit can perform currency conversion using, for example, an exchange rate API. It can also perform currency conversion based on historical exchange rate data. Furthermore, the conversion unit can perform currency conversion in real time. This allows users to handle different currencies by converting the input currency to a specified currency. Specific types of specified currencies include, for example, USD, EUR, JPY, etc. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the input currency into a generating AI, which can then convert it to a specified currency.

[0088] The data collection unit can estimate the user's emotions and dynamically adjust the frequency of flight information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can increase the frequency of flight information collection and update it in real time. If the user is relaxed, the data collection unit can maintain a normal frequency of flight information collection and provide only the necessary information. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important flight information and provide it quickly. This allows for more appropriate information to be provided by adjusting the frequency of flight information collection according to the user'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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection frequency.

[0089] The data collection unit can analyze past flight information collection history and select the most efficient collection method. For example, the data collection unit can prioritize collecting information on airlines that frequently experience delays based on past flight information collection history. The data collection unit can also adjust the frequency of flight information collection during specific time periods based on past collection history. Furthermore, the data collection unit can analyze past collection history and prioritize collecting information on airlines and routes frequently used by the user. This enables more efficient collection of flight information by analyzing past collection history. Specific criteria and methods for determining the most efficient collection method include, for example, data acquisition speed and accuracy. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past flight information collection history into a generating AI, which can then select the optimal collection method.

[0090] The data collection unit can filter flight information based on the user's current travel plans and areas of interest. For example, the data collection unit can collect only relevant flight information based on the user's travel plans. The data collection unit can also prioritize the collection of relevant flight information based on the user's areas of interest (e.g., business travel or sightseeing). Furthermore, the data collection unit can filter and collect flight information related to a specific destination based on the user's travel plans. This allows for the provision of highly relevant information by filtering flight information based on the user's travel plans and areas of interest. Specific criteria and methods for filtering include, for example, travel plan details and areas of interest categories. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's travel plans and areas of interest into a generating AI, which can then perform the filtering.

[0091] The data collection unit can estimate the user's emotions and dynamically determine the priority of flight information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information about delays or cancellations. Conversely, if the user is relaxed, the data collection unit can prioritize collecting information about normal flights. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information about gate changes or boarding times. This allows for the priority of important information to be provided by prioritizing flight information according to the user'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 above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of flight information to collect.

[0092] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting flight information. For example, the data collection unit can prioritize the collection of flight information for the airport where the user is currently located. It can also prioritize the collection of flight information for the airport closest to the user's current location. Furthermore, the data collection unit can filter and collect the most relevant flight information based on the user's current location. This allows for the provision of highly relevant flight information by considering the user's geographical location. Specific types and methods of acquiring geographical location information include, for example, GPS coordinates and address information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant flight information.

[0093] The data collection unit can analyze the user's social media activity and collect relevant information when collecting flight information. For example, the data collection unit can collect information about travel destinations and airlines of interest from the user's social media posts. The data collection unit can also collect flight information shared by the user's social media followers and friends. Furthermore, the data collection unit can analyze the user's social media activity and prioritize the collection of relevant flight information. This allows for the provision of highly relevant flight information by analyzing the user's social media activity. Specific types of social media activity and methods of acquisition include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0094] The service provider can estimate the user's emotions and dynamically adjust how flight information is provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide detailed flight information to reassure them. If the user is relaxed, the service provider can also provide concise flight information. Furthermore, if the user is in a hurry, the service provider can quickly provide important flight information. This allows for more appropriate information to be provided by adjusting how flight information is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the delivery method.

[0095] The information provider can adjust the level of detail provided based on the importance of the flight information at the time of provision. For example, the provider can provide detailed information on flight delays and cancellations. Alternatively, it can provide regular flight information concisely. Furthermore, it can provide information on gate changes and boarding times quickly. By adjusting the level of detail based on the importance of the flight information, the provider can appropriately provide information that is important to the user. Specific criteria and adjustment methods for level of detail include, for example, the granularity of the information and the number of displayed items. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the importance of the flight information into a generating AI, which can then adjust the level of detail provided.

[0096] The information provider can apply different information provision algorithms depending on the category of flight information at the time of provision. For example, the information provider can update and provide flight delay information in real time. It can also provide gate change information quickly and notify users. Furthermore, it can update and provide regular flight information periodically. By applying a information provision algorithm according to the category of flight information, it becomes possible to provide more appropriate information. Specific types and implementation methods of the information provision algorithms include, for example, recommendation algorithms and filtering algorithms. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the category of flight information into a generating AI, and the generating AI can apply an appropriate information provision algorithm.

[0097] The information provider can estimate the user's emotions and dynamically adjust the timing of flight information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide flight information more frequently. It can also provide flight information at the necessary time if the user is relaxed. Furthermore, if the user is in a hurry, the information provider can quickly provide important flight information. This allows for more timely information delivery by adjusting the timing of flight information delivery according to the user'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 above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the delivery timing.

[0098] The information provider can determine the priority of information provision based on the timing of flight information submission. For example, the information provider can prioritize the provision of flight delay and cancellation information. It can also provide gate change information promptly. Furthermore, it can provide regular flight information on a regular basis. By prioritizing information provision based on the timing of flight information submission, important information can be provided preferentially. Specific criteria and methods for determining the submission timing include, for example, before the flight departure or after arrival. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the timing of flight information submission into a generating AI, which can then determine the priority of information provision.

[0099] The information provider can adjust the order of flight information delivery based on its relevance. For example, it may prioritize providing information relevant to the user's current flight. It can also provide relevant flight information based on the user's travel plan. Furthermore, it can provide relevant flight information based on the user's areas of interest. This allows the information important to the user to be prioritized by adjusting the order of delivery based on the relevance of the flight information. Specific criteria and evaluation methods for relevance include, for example, the user's areas of interest and past behavioral history. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the relevance of flight information into a generating AI, which can then adjust the order of delivery.

[0100] The route calculation unit can estimate the user's emotions and dynamically adjust the route calculation criteria based on the estimated emotions. For example, if the user is feeling anxious, the route calculation unit will prioritize calculating the safest route. It can also calculate the normal shortest route if the user is relaxed. Furthermore, if the user is in a hurry, the route calculation unit can prioritize calculating the fastest route. This allows for the provision of a more appropriate route by adjusting the route calculation criteria according to the user'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 above processing in the route calculation unit may be performed using AI, or not. For example, the route calculation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the route calculation criteria.

[0101] The route calculation unit can calculate the most efficient route while considering the congestion situation within the airport. For example, the route calculation unit calculates the optimal route based on the real-time congestion situation within the airport. The route calculation unit can also calculate detour routes to avoid congestion. Furthermore, the route calculation unit can suggest the optimal route to the user, taking congestion into consideration. This allows users to use the optimal route that avoids congestion by considering the congestion situation within the airport. Specific evaluation criteria and acquisition methods for congestion include, for example, population density and real-time pedestrian flow data. Some or all of the above processing in the route calculation unit may be performed using, for example, AI, or not using AI. For example, the route calculation unit can input airport congestion data into a generating AI, which can then calculate the optimal route.

[0102] The route calculation unit can customize the most efficient route by considering the user's attribute information during route calculation. For example, the route calculation unit can calculate the optimal route according to the user's age and physical strength. It can also calculate the optimal route according to the amount of luggage the user is carrying. Furthermore, the route calculation unit can calculate the optimal route according to the user's special needs (such as wheelchair use). In this way, by considering the user's attribute information, the optimal route can be provided to each individual user. Specific types of attribute information and methods of acquisition include, for example, age, gender, and physical characteristics. Some or all of the above processing in the route calculation unit may be performed using AI, for example, or without AI. For example, the route calculation unit can input the user's attribute information into a generating AI, which can then customize the optimal route.

[0103] The route calculation unit can estimate the user's emotions and dynamically adjust the order in which the route calculation results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the route calculation unit can display the safest route first. If the user is relaxed, the route calculation unit can also display the normal shortest route first. Furthermore, if the user is in a hurry, the route calculation unit can display the fastest route first. This allows for more appropriate information to be provided by adjusting the order in which the route calculation results are displayed according to the user'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 route calculation unit may be performed using AI, or not using AI. For example, the route calculation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display order.

[0104] The route calculation unit can calculate the most efficient route by considering information about facilities within the airport. For example, the route calculation unit can calculate the optimal route by considering the locations of toilets and rest areas within the airport. It can also calculate the optimal route by considering the locations of shops and restaurants within the airport. Furthermore, it can calculate the optimal route by considering the locations of security checkpoints within the airport. This allows users to use routes that make it easier to access the necessary facilities by considering information about facilities within the airport. Specific types of facility information and methods of acquisition include, for example, location information for restaurants, toilets, and shops. Some or all of the above processing in the route calculation unit may be performed using AI, or not. For example, the route calculation unit can input information about facilities within the airport into a generating AI, which can then calculate the optimal route.

[0105] The route calculation unit can propose the most efficient route by referring to the user's past travel history during route calculation. For example, the route calculation unit can propose the optimal route based on routes previously used by the user. It can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the route calculation unit can analyze the user's past travel history and propose the most efficient route. This allows for the provision of more efficient routes by referring to the user's past travel history. Specific methods for acquiring and using past travel history include, for example, GPS data and past route information. Some or all of the above-described processes in the route calculation unit may be performed using, for example, AI, or without AI. For example, the route calculation unit can input the user's past travel history into a generating AI, which can then propose the optimal route.

[0106] The guidance unit can estimate the user's emotions and dynamically adjust the display method of the guidance based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit can provide a simple and highly visible display method. If the user is relaxed, the guidance unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide a concise display method. By adjusting the display method of the guidance according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, or not using AI. For example, the guidance unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method.

[0107] The guidance unit can select the most efficient display method by referring to the user's past guidance history when providing guidance. For example, the guidance unit can provide the optimal display method based on the display method the user has preferred to use in the past. The guidance unit can also prioritize providing a specific display method based on the user's past guidance history. Furthermore, the guidance unit can analyze the user's past guidance history and propose the most efficient display method. This allows for the provision of a more appropriate display method by referring to the user's past guidance history. Specific methods for acquiring and using guidance history include, for example, past guidance content and user feedback. Some or all of the above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input the user's past guidance history into a generating AI, which can then select the optimal display method.

[0108] The guidance unit can update the guidance in real time based on the user's current location information. For example, the guidance unit can update the user's current location in real time while the user is moving and provide guidance. The guidance unit can also update the user's current location in real time as the user approaches their destination and suggest the optimal route. Furthermore, if the user gets lost, the guidance unit can update the current location in real time and provide guidance again. This makes it possible to provide more appropriate information by updating the guidance in real time based on the user's current location information. Specific update frequencies and methods for updating the guidance in real time include, for example, updates every second or every minute. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's current location information into a generating AI, which can then update the guidance in real time.

[0109] The guidance unit can estimate the user's emotions and dynamically determine the priority of guidance based on the estimated emotions. For example, if the user is feeling anxious, the guidance unit will prioritize providing important guidance information. It can also provide normal guidance information if the user is relaxed. Furthermore, if the user is in a hurry, the guidance unit can quickly provide the most important guidance information. This ensures that important information is provided preferentially by prioritizing guidance according to the user'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 above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of guidance.

[0110] The guidance unit can select the most efficient display method when providing guidance, taking into account the user's device information. For example, if the user is using a smartphone, the guidance unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the guidance unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the guidance unit can provide a concise and highly visible display method. This allows for the provision of a more appropriate display method by considering the user's device information. Specific types of device information and methods of acquisition include, for example, smartphones, tablets, and wearable devices. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's device information into a generating AI, which can then select the optimal display method.

[0111] The guidance unit can customize the content of the guidance based on the user's language settings. For example, the guidance unit can automatically set the language of the guidance based on the language settings of the user's device. The guidance unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the guidance unit selects a specific language, it can provide guidance in that language. This allows for the provision of more appropriate information by customizing the content of the guidance based on the user's language settings. Specific methods for obtaining and using language settings include, for example, device settings and user selection. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's language settings into a generating AI, which can then customize the content of the guidance.

[0112] The translation unit can estimate the user's emotions and dynamically adjust the expression of the language translation based on the estimated emotions. For example, if the user is feeling anxious, the translation unit can provide a concise and easy-to-understand expression. If the user is relaxed, the translation unit can also provide an expression that includes detailed explanations. Furthermore, if the user is in a hurry, the translation unit can provide a quick and concise expression. This allows for the provision of more appropriate information by adjusting the expression of the language translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 translation unit may be performed using AI, or not using AI. For example, the translation unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the expression.

[0113] The conversion unit can select the most efficient conversion method by referring to the user's past conversion history during language conversion. For example, the conversion unit can provide the optimal conversion method based on the language conversion methods the user has used in the past. The conversion unit can also prioritize providing specific expression methods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and propose the most efficient conversion method. This allows for the provision of a more appropriate conversion method by referring to the user's past conversion history. Specific methods for obtaining and using the conversion history include, for example, past conversion content and user feedback. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's past conversion history into a generating AI, which can then select the optimal conversion method.

[0114] The conversion unit can customize the conversion process by considering the user's attribute information during language conversion. For example, the conversion unit can provide the optimal conversion method according to the user's age and language level. It can also provide a conversion method that includes specialized terminology according to the user's field of expertise. Furthermore, the conversion unit can provide the optimal conversion method according to the user's special needs (for example, speech conversion for the visually impaired). This allows for the provision of a more appropriate conversion method by considering the user's attribute information. Specific types of attribute information and methods of acquisition include, for example, age, gender, and language ability. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's attribute information into a generating AI, which can then customize the optimal conversion method.

[0115] The translation unit can estimate the user's emotions and dynamically determine the priority of language translation based on the estimated emotions. For example, if the user is feeling anxious, the translation unit will prioritize providing the translation of important information. It can also provide normal language translation if the user is relaxed. Furthermore, if the user is in a hurry, the translation unit can quickly provide the translation of the most important information. This allows for the priority of providing important information by determining the priority of language translation according to the user'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 above processing in the translation unit may be performed using AI, or not. For example, the translation unit can input user emotion data into a generative AI, which can estimate emotions and determine priorities.

[0116] The conversion unit can select the most efficient conversion method during language conversion, taking into account the user's geographical location information. For example, the conversion unit can provide the optimal conversion method based on the language of the country or region where the user is currently located. The conversion unit can also prioritize conversion to the language most relevant to the user's current location. Furthermore, the conversion unit can provide the most relevant language conversion based on the user's current location. This allows for the provision of a more appropriate conversion method by considering the user's geographical location information. Specific types and methods of obtaining geographical location information include, for example, GPS coordinates and address information. Some or all of the above-described processes in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's geographical location information into a generating AI, which can then select the optimal conversion method.

[0117] The conversion unit can improve the accuracy of language conversion by analyzing the user's social media activity. For example, the conversion unit can analyze frequently used expressions and language from the user's social media posts to improve conversion accuracy. The conversion unit can also analyze the languages ​​used by the user's social media followers and friends to provide the optimal conversion method. Furthermore, the conversion unit can analyze the user's social media activity and prioritize providing relevant language conversions. This allows for the provision of more appropriate conversion methods by analyzing the user's social media activity. Specific types and methods of acquiring social media activity include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's social media activity data into a generating AI, which can then select the optimal conversion method.

[0118] The conversion unit can estimate the user's emotions and dynamically adjust the currency conversion method based on the estimated emotions. For example, if the user is feeling anxious, the conversion unit can provide a concise and easy-to-understand currency conversion method. If the user is relaxed, the conversion unit can also provide a currency conversion method that includes detailed explanations. Furthermore, if the user is in a hurry, the conversion unit can provide a quick and concise currency conversion method. This allows for more appropriate information to be provided by adjusting the currency conversion method according to the user'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 conversion unit may be performed using AI or not using AI. For example, the conversion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the currency conversion method.

[0119] The conversion unit can select the most efficient conversion method when converting currencies by referring to the user's past conversion history. For example, the conversion unit can provide the optimal conversion method based on the currency conversion methods the user has used in the past. The conversion unit can also prioritize providing a specific conversion method based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and propose the most efficient conversion method. This allows for the provision of a more appropriate conversion method by referring to the user's past conversion history. Specific methods for obtaining and using the conversion history include, for example, past conversion details and user feedback. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's past conversion history into a generating AI, which can then select the optimal conversion method.

[0120] The conversion unit can customize currency conversions by taking into account the user's attribute information. For example, the conversion unit can provide the optimal currency conversion method according to the user's age and economic situation. It can also provide the optimal currency conversion method according to the user's travel destination. Furthermore, the conversion unit can provide the optimal currency conversion method according to the user's special needs (for example, detailed conversion information for business travelers). This allows for the provision of a more appropriate conversion method by considering the user's attribute information. Specific types of attribute information and methods of acquisition include, for example, age, gender, and economic situation. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the user's attribute information into a generating AI, which can then customize the optimal conversion method.

[0121] The conversion unit can estimate the user's emotions and dynamically determine the priority of currency conversions based on the estimated emotions. For example, if the user is feeling anxious, the conversion unit will prioritize providing important currency conversion information. It can also provide normal currency conversion information if the user is relaxed. Furthermore, if the user is in a hurry, the conversion unit can quickly provide the most important currency conversion information. This allows for the priority of providing important information by determining the priority of currency conversions according to the user'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 above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of currency conversions.

[0122] The conversion unit can select the most efficient conversion method when converting currencies, taking into account the user's geographical location information. For example, the conversion unit can provide the optimal conversion method based on the currency of the country or region where the user is currently located. The conversion unit can also prioritize conversion to the currency most relevant to the user's current location. Furthermore, the conversion unit can provide the most relevant currency conversion based on the user's current location. This allows for the provision of a more appropriate conversion method by considering the user's geographical location information. Specific types and methods of obtaining geographical location information include, for example, GPS coordinates and address information. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's geographical location information into a generating AI, which can then select the optimal conversion method.

[0123] The conversion unit can improve the accuracy of currency conversions by analyzing the user's social media activity. For example, the conversion unit can analyze frequently used currencies and conversion methods from the user's social media posts to improve conversion accuracy. The conversion unit can also analyze the currencies used by the user's social media followers and friends and provide the optimal conversion method. Furthermore, the conversion unit can analyze the user's social media activity and prioritize providing relevant currency conversions. This allows for the provision of more appropriate conversion methods by analyzing the user's social media activity. Specific types and methods of acquiring social media activity include, for example, post content, number of likes, and number of followers. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's social media activity data into a generating AI, which can then select the optimal conversion method.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The airport guidance support system can also be equipped with a health monitoring unit that monitors the user's health status. For example, the health monitoring unit can measure the user's heart rate and blood pressure, and if an abnormality is detected, it can send a notification prompting emergency action. The health monitoring unit can also monitor the user's walking speed and posture, detecting signs of fatigue and stress. Furthermore, based on the user's health data, the health monitoring unit can guide the user to the optimal route to rest areas and medical facilities within the airport. This allows for real-time monitoring of the user's health status and the provision of necessary support, thereby improving safety and comfort within the airport.

[0126] The airport information support system can further estimate the user's emotions and provide entertainment information within the airport based on those emotions. For example, if the user is feeling stressed, it can provide information on relaxing lounges and spas. If the user is bored, it can provide information on movie theaters and arcades. Furthermore, if the user is excited, it can provide information on shopping areas and events. In this way, by providing entertainment information tailored to the user's emotions, the system can make their stay at the airport more enjoyable.

[0127] The airport information support system can also include a baggage tracking unit that provides tracking information for the user's luggage. For example, the baggage tracking unit can track the location of the user's luggage in real time and notify the user via a smartphone app. Furthermore, the baggage tracking unit can provide support information to help users respond quickly if their luggage is delayed or lost. In addition, the baggage tracking unit can guide the user to the location and time of luggage pickup. This allows users to always know the status of their luggage and continue their trip with peace of mind.

[0128] The airport guidance support system can further estimate the user's emotions and provide restaurant recommendations based on those emotions. For example, if the user is tired, it can provide information on relaxing cafes and restaurants. If the user is in a hurry, it can provide information on fast food restaurants where they can eat quickly. Furthermore, if the user wants to enjoy a special meal, it can provide information on high-end restaurants or restaurants serving local specialties. In this way, by providing restaurant information tailored to the user's emotions, the dining experience within the airport can be improved.

[0129] The airport information support system can further analyze the user's past behavioral history to provide personalized services. For example, it can provide information on the most suitable flights and destinations based on data from past flights and places visited. It can also recommend restaurants that match the user's preferences based on their past dining history. Furthermore, it can provide information on products and stores that the user might be interested in based on their past shopping history. In this way, it can leverage the user's past behavioral history to provide even more personalized services.

[0130] The airport navigation support system can further estimate the user's emotions and guide them along routes that avoid congestion within the airport based on those emotions. For example, if the user is feeling stressed, it can provide an alternative route to avoid congestion. If the user is relaxed, it can provide the usual shortest route. Furthermore, if the user is in a hurry, it can provide the fastest route. In this way, by providing route guidance tailored to the user's emotions, it can make moving around the airport more comfortable.

[0131] The airport information support system can further consider the user's device information to select the most suitable notification method. For example, if the user is using a smartphone, information can be provided via push notifications. If the user is using a smartwatch, information can be provided via vibration or voice notifications. Furthermore, if the user is using a tablet, a notification method optimized for the larger screen can be provided. In this way, by considering the user's device information, a more effective notification method can be provided.

[0132] The airport guidance support system can further estimate the user's emotions and provide information on rest areas and relaxation spaces within the airport based on those estimated emotions. For example, if the user is tired, it can provide information on nearby rest areas and relaxation spaces. It can also provide information on relaxing spaces if the user is stressed. Furthermore, if the user wants to refresh themselves, it can provide information on massage chairs and spas. In this way, by providing information on rest areas and relaxation spaces tailored to the user's emotions, it can support a comfortable stay within the airport.

[0133] The airport information support system can further consider the user's travel plans to provide optimal flight information. For example, if the user is planning a business trip, it can provide business class flight information and conference room reservation information. If the user is planning a sightseeing trip, it can provide direct flights to tourist destinations and information on tour guides. Furthermore, if the user is planning a family trip, it can provide family-friendly flight information and information on kids' areas. In this way, by providing optimal flight information tailored to the user's travel plans, the system can help them plan their trip smoothly.

[0134] The airport information support system can further estimate the user's emotions and provide shopping information within the airport based on those emotions. For example, if the user is feeling stressed, it can provide information on relaxing aromatherapy shops or massage products. If the user is bored, it can provide information on entertainment goods or bookstores. Furthermore, if the user is excited, it can provide information on fashion brands or luxury watches. In this way, the system can improve the shopping experience within the airport by providing shopping information tailored to the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects flight information. The collection unit obtains flight information in real time using airline APIs. It can also collect flight information by linking with airport information systems. Furthermore, it can collect flight information by scraping publicly available data on the internet. For example, it can obtain flight information such as departure time, arrival time, and gate information. Step 2: The service provider provides the collected flight information to the user. The service provider displays the flight information through a smartphone app. Flight information can also be provided using digital signage within the airport. Furthermore, flight information can be provided through a voice assistant. For example, it can notify the user of flight delays or gate changes. Step 3: The route calculation unit calculates the shortest route based on airport map data. The route calculation unit calculates the shortest route based on airport facility information and pathway information. It can also calculate the shortest route considering real-time congestion information. Furthermore, it can calculate the shortest route considering the user's movement speed and physical condition. For example, it can calculate the shortest route from the check-in counter to the boarding gate. Step 4: The information desk guides the user to the calculated shortest route. The information desk displays the route via a smartphone app. It can also guide users to the route using digital signage within the airport. Furthermore, it can guide users to the route via a voice assistant. For example, it can display the route from the check-in counter to the boarding gate. Step 5: The conversion unit converts the input information into the specified language. The conversion unit uses a text translation API to perform language conversion. It can also use speech recognition technology to convert voice input into text and then translate that text into the specified language. Furthermore, it can perform real-time conversation translation. For example, translating Japanese text into English. Step 6: The conversion unit converts the entered currency to the specified currency. The conversion unit uses an exchange rate API to perform currency conversion. It can also perform currency conversion based on historical exchange rate data. Furthermore, it can perform currency conversion in real time. For example, convert Japanese Yen to US Dollars.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit can collect flight information using the camera 42 and microphone 38B of the smart device 14. The data provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the collected flight information to the user through the display 40A and speaker 40B of the smart device 14. The route calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the shortest route based on map data of the airport. The guidance unit is implemented by the control unit 46A of the smart device 14 and guides the user along the calculated route. The conversion and conversion units are implemented by the specific processing unit 290 of the data processing device 12 and perform language conversion and currency conversion. 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.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0154] 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.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0156] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit can collect flight information using the camera 42 and microphone 238 of the smart glasses 214. The data provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the collected flight information to the user through the speaker 240 of the smart glasses 214. The route calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the shortest route based on map data of the airport. The guidance unit is implemented by the control unit 46A of the smart glasses 214 and guides the user along the calculated route. The conversion and conversion units are implemented by the specific processing unit 290 of the data processing device 12 and perform language conversion and currency conversion. 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.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit can collect flight information using the camera 42 and microphone 238 of the headset terminal 314. The data provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the collected flight information to the user through the display 343 and speaker 240 of the headset terminal 314. The route calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the shortest route based on map data of the airport. The guidance unit is implemented by the control unit 46A of the headset terminal 314 and guides the user along the calculated route. The conversion and currency conversion units are implemented by the specific processing unit 290 of the data processing device 12 and perform language conversion and currency conversion. 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.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit can collect flight information using the camera 42 and microphone 238 of the robot 414. The data provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the collected flight information to the user through the speaker 240 of the robot 414. The route calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and calculates the shortest route based on map data of the airport. The guidance unit is implemented by the control unit 46A of the robot 414 and guides the user along the calculated route. The conversion and conversion units are implemented by the specific processing unit 290 of the data processing device 12 and perform language conversion and currency conversion. 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection unit that collects flight information, A providing unit that provides flight information collected by the aforementioned collection unit, A route calculation unit that calculates the most efficient route based on detailed map data of the airport, A guidance unit that guides the user along the most efficient route calculated by the aforementioned route calculation unit, A conversion unit that performs language conversion, It comprises a conversion unit that performs currency conversion. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect flight information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide users with collected flight information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned route calculation unit, Calculate the shortest route based on map data within the airport. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is Guide the user to the calculated shortest route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The conversion unit is Convert the input information to the specified language. The system described in Appendix 1, characterized by the features described herein. (Note 7) The conversion unit is, Convert the entered currency to the specified currency. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and dynamically adjusts the frequency of flight information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We analyze past flight data collection history and select the most efficient collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting flight information, filtering is performed based on the user's current travel plans and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and dynamically determines the priority of flight information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting flight information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting flight information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and dynamically adjusts how flight information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, the level of detail provided will be adjusted based on the importance of the flight information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, different provisioning algorithms are applied depending on the category of flight information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and dynamically adjusts the timing of flight information delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, priority will be determined based on when the flight information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, the order of delivery will be adjusted based on the relevance of the flight information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned route calculation unit, It estimates the user's emotions and dynamically adjusts the basis for route calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned route calculation unit, When calculating the route, the most efficient route is calculated by taking into account the congestion situation within the airport. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned route calculation unit, When calculating a route, the most efficient route is customized by taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned route calculation unit, It estimates the user's emotions and dynamically adjusts the order in which the results of the root calculation are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned route calculation unit, When calculating the route, the most efficient route is calculated by taking into account information about facilities within the airport. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned route calculation unit, When calculating a route, the system refers to the user's past travel history to suggest the most efficient route. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is It estimates the user's emotions and dynamically adjusts how the guidance is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing guidance, the system selects the most efficient display method by referring to the user's past guidance history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is During navigation, the guidance is updated in real time based on the user's current location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is It estimates the user's emotions and dynamically determines the priority of guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When providing guidance, the most efficient display method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide section is When providing guidance, the guidance content will be customized based on the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 32) The conversion unit is It estimates the user's emotions and dynamically adjusts the language translation expression based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The conversion unit is During language conversion, the system selects the most efficient conversion method by referring to the user's past conversion history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The conversion unit is When converting languages, the conversion is customized by taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The conversion unit is It estimates the user's emotions and dynamically determines the priority of language translation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The conversion unit is During language conversion, the most efficient conversion method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The conversion unit is During language conversion, we analyze the user's social media activity to improve the accuracy of the conversion. The system described in Appendix 1, characterized by the features described herein. (Note 38) The conversion unit is, It estimates the user's emotions and dynamically adjusts the currency conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The conversion unit is, When converting currencies, the system selects the most efficient conversion method by referring to the user's past conversion history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The conversion unit is, When converting currencies, the conversion is customized by taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The conversion unit is, It estimates the user's emotions and dynamically determines the priority of currency conversion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The conversion unit is, When converting currencies, the most efficient conversion method is selected by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The conversion unit is, When converting currencies, we analyze users' social media activity to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 collection unit that collects flight information, A providing unit that provides flight information collected by the aforementioned collection unit, A route calculation unit that calculates the most efficient route based on map data within the airport, A guidance unit that guides the user along an efficient route calculated by the aforementioned route calculation unit, A conversion unit that performs language conversion, It comprises a conversion unit that performs currency conversion. A system characterized by the following features.

2. The aforementioned collection unit is Collect flight information in real time. The system according to feature 1.

3. The aforementioned route calculation unit, Calculate the shortest route based on map data within the airport. The system according to feature 1.

4. The conversion unit is Convert the input information to the specified language. The system according to feature 1.

5. The conversion unit is, Convert the entered currency to the specified currency. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and dynamically adjusts the frequency of flight information collection based on the estimated user emotions. The system according to feature 1.

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