System

Abstract

An object of a system according to an embodiment is to automate procedures at an airport and provide an efficient and comfortable travel experience for a passenger.SOLUTION: A system according to an embodiment includes a check-in unit, a baggage inspection unit, a customs inspection unit, a baggage receiving unit, a hearing unit, and a face authentication unit. The check-in unit performs a check-in procedure. The package inspection part inspects the package. The customs inspection unit performs customs inspection. The package receiving unit receives a package. The hearing portion corresponds to passenger questions. The face authentication unit performs identity verification.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that procedures at airports are time-consuming and cumbersome, making it difficult to provide passengers with an efficient and comfortable travel experience.

[0005] The system according to the embodiment aims to automate procedures at airports and provide passengers with an efficient and comfortable travel experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a check-in unit, a baggage inspection unit, a customs inspection unit, a baggage claim unit, a hearing unit, and a face authentication unit. The check-in unit performs check-in procedures. The baggage inspection unit inspects baggage. The customs inspection unit performs customs inspection. The baggage claim unit collects baggage. The hearing unit responds to passenger questions. The face authentication unit verifies the identity of passengers. [Effects of the Invention]

[0007] Systems according to embodiments can automate airport procedures and provide an efficient and comfortable travel experience for passengers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An airport automation system according to an embodiment of the present invention is a system that completely automates airport procedures. This system automatically reads answers written by students, summarizes them using a generative AI, calculates the similarity to the model answer, and assigns a score. This allows the airport automation system to significantly reduce the time required for airport procedures and provide passengers with a comfortable and efficient travel experience.

[0029] An airport automation system according to an embodiment includes a check-in unit, a baggage inspection unit, a customs inspection unit, a baggage claim unit, a hearing unit, and a facial recognition unit. The check-in unit performs passenger check-in procedures. For example, the check-in unit performs check-in using a dedicated kiosk terminal or a smartphone app. The check-in unit can also analyze passenger reservation information and automatically complete check-in procedures. For example, when a passenger scans their passport on the kiosk terminal, a generation AI verifies the reservation information and issues a boarding pass. The baggage inspection unit inspects baggage. For example, the baggage inspection unit passes the passenger's checked baggage through an automated baggage inspection system. The baggage inspection unit can also analyze the contents of the baggage using a generation AI to check for dangerous or prohibited items. For example, as the baggage passes through the conveyor belt, the generation AI analyzes X-ray images and issues an alert if dangerous items are detected. The customs inspection unit performs customs inspections. For example, as a passenger passes through customs, the generation AI analyzes passport information and declaration details and automatically completes the necessary procedures. For example, when a passenger enters a customs declaration form into a kiosk terminal, the generation AI analyzes the contents and instructs them on the necessary procedures. The baggage claim unit collects their luggage. For example, after arrival, passengers use an automated baggage claim system. In addition, the baggage claim unit can automatically hand over the correct luggage by matching the passenger's facial recognition data with their baggage tag. For example, when a passenger arrives at the baggage claim area, the facial recognition system recognizes the passenger and automatically hands over the corresponding luggage. The hearing unit responds to passenger questions. For example, in the hearing unit, the generation AI can provide guidance and handle inquiries within the airport. For example, when a passenger asks a question at an airport information desk, the generation AI analyzes the question and provides an appropriate answer. The facial recognition unit performs identity verification. For example, the facial recognition unit can use a facial recognition system to verify the identity of passengers during various procedures within the airport. For example, the facial recognition system recognizes passengers and automatically proceeds with procedures such as check-in, boarding procedures, customs inspection, and baggage claim.As a result, the airport automation system according to the embodiment can fully automate airport procedures and provide passengers with a comfortable and efficient travel experience. For example, the time from check-in to boarding can be shortened, and lost or misplaced baggage can be prevented. In addition, the burden on airport staff can be reduced and security can be strengthened.

[0030] The check-in unit can analyze passengers' past travel history and automatically suggest optimal seats and services. For example, the generation AI in the check-in unit analyzes passengers' past travel history and makes optimal suggestions based on seats and services they preferred in the past. For example, it will preferentially suggest window seats to passengers who prefer window seats. The generation AI in the check-in unit also analyzes passengers' past travel history and makes optimal suggestions based on airlines and alliances they have used in the past. For example, it will preferentially suggest flights of that airline to passengers who use a particular airline's mileage program. The generation AI in the check-in unit also analyzes passengers' past travel history and makes optimal suggestions based on services and options they have used in the past. For example, it will make suggestions based on in-flight meal selection and entertainment preferences. This makes it possible to provide a personalized travel experience by suggesting optimal seats and services based on passengers' past travel history.

[0031] The check-in unit can check passengers' health conditions and automatically arrange for medical support if necessary. In the check-in unit, for example, the generation AI checks passengers' health conditions and automatically arranges for medical support if necessary. For example, if a passenger has a chronic illness, medical support will be arranged in advance. In addition, the generation AI checks passengers' health conditions and suggests special considerations on board if necessary. For example, it will suggest allergy-friendly in-flight meals for passengers with allergies. In addition, the generation AI checks passengers' health conditions and arranges for medical support at the airport if necessary. For example, if a passenger falls ill at the airport, medical staff will be arranged. This makes it possible to provide medical support according to passengers' health conditions, thereby providing a safe and comfortable travel experience.

[0032] During the check-in process, the generation AI can provide the passenger with weather and event information for their travel destination. During the check-in process, the generation AI can provide weather information for the passenger's travel destination. For example, the generation AI can display the weather forecast for the travel destination and suggest appropriate clothing and items to bring. During the check-in process, the generation AI can provide event information for the passenger's travel destination. For example, the generation AI can display information about events and tourist spots being held at the travel destination. During the check-in process, the generation AI can provide transportation information for the passenger's travel destination. For example, the generation AI can display information about public transportation options and traffic congestion at the travel destination. This can support the passenger's travel plans by providing them with information about their travel destination.

[0033] The check-in unit allows the generation AI to provide multilingual check-in procedures according to the passenger's language setting. For example, the check-in unit allows the generation AI to provide multilingual check-in procedures according to the passenger's language setting. For example, the check-in procedure is performed in multiple languages, such as English, French, and Chinese. The check-in unit also provides information displayed during the check-in procedure in multiple languages ​​according to the passenger's language setting. For example, the check-in unit displays boarding passes and guide displays in the passenger's native language. The check-in unit also provides audio guidance in multiple languages ​​according to the passenger's language setting. For example, the check-in unit plays audio guidance in the passenger's native language. This allows international passengers to be accommodated by providing multilingual check-in procedures.

[0034] The baggage inspection unit can automatically measure the weight and size of luggage and suggest the optimal loading method. For example, the generation AI in the baggage inspection unit automatically measures the weight and size of luggage and suggests the optimal loading method. For example, it instructs the system to load luggage in the optimal position based on its weight and size. The generation AI in the baggage inspection unit also automatically measures the weight and size of luggage and makes suggestions to maximize loading efficiency. For example, it makes effective use of loading space by optimizing the shape and placement of luggage. The generation AI in the baggage inspection unit also automatically measures the weight and size of luggage and makes suggestions that take into account balance when loading. For example, it instructs the system to place heavy luggage at the bottom and light luggage at the top. This improves loading efficiency by suggesting the optimal loading method based on the weight and size of the luggage.

[0035] The baggage inspection unit analyzes the contents of the baggage in detail and can automatically perform custom inspections of specific items. For example, a generation AI can analyze the contents of the baggage in detail and automatically perform custom inspections of specific items. For example, a detailed inspection can be performed on electronic devices and liquid items. The baggage inspection unit can also analyze the contents of the baggage in detail and automatically perform custom inspections of dangerous or prohibited items. For example, a detailed inspection can be performed on explosives and drugs. The baggage inspection unit can also analyze the contents of the baggage in detail and automatically perform custom inspections in accordance with the regulations of a specific country or region. For example, an inspection can be performed on items prohibited in a specific country. In this way, security can be strengthened by analyzing the contents of the baggage in detail and performing custom inspections of specific items.

[0036] The baggage inspection unit can analyze the contents of the baggage and automatically generate a list of items needed at the travel destination. For example, the baggage inspection unit uses a generation AI to analyze the contents of the baggage and automatically generate a list of items needed at the travel destination. For example, it may suggest a packing list based on the climate and activities at the travel destination. The baggage inspection unit also uses a generation AI to analyze the contents of the baggage and automatically generate a list of items based on the length of stay at the travel destination. For example, it may suggest additional clothing and toiletries for long stays. The baggage inspection unit also uses a generation AI to analyze the contents of the baggage and automatically generate a list of items based on specific events and activities at the travel destination. For example, it may suggest swimsuits and sunglasses at a beach resort. This allows the system to support travel preparations by automatically generating a list of items needed at the travel destination.

[0037] The baggage inspection department can analyze the contents of baggage and suggest security measures to prevent loss or theft. For example, the generative AI in the baggage inspection department analyzes the contents of baggage and suggests security measures to prevent loss or theft. For example, it may instruct passengers to carry valuables in separate bags. The generative AI in the baggage inspection department also analyzes the contents of baggage and suggests the use of baggage tags and locks. For example, attaching a GPS tag to baggage allows it to be tracked in case of loss. The generative AI in the baggage inspection department also analyzes the contents of baggage and suggests how to handle baggage within the airport. For example, it may instruct passengers to keep their baggage in sight at all times. This provides passengers with peace of mind by suggesting security measures to prevent baggage loss or theft.

[0038] The customs inspection department can analyze passengers' past customs declaration history and suggest the optimal declaration method. For example, the generation AI in the customs inspection department analyzes passengers' past customs declaration history and suggests the optimal declaration method. For example, it suggests an appropriate declaration method based on the items and amounts declared in the past. The customs inspection department also analyzes passengers' past customs declaration history and makes suggestions to prevent omissions in declarations. For example, it issues a warning if there has been a past omission in declaration. The customs inspection department also analyzes passengers' past customs declaration history and makes suggestions to simplify the declaration procedure. For example, it automatically enters information that has been declared in the past. This makes it possible to streamline the declaration procedure by suggesting the optimal declaration method based on passengers' past customs declaration history.

[0039] The customs inspection department can analyze passengers' belongings in detail and automatically identify items that require a customs declaration. For example, the customs inspection department uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration. For example, if the belongings contain expensive electronic devices or jewelry, the customs inspection department will notify the passenger that a declaration is required. The customs inspection department also uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration in accordance with the regulations of a specific country or region. For example, if the belongings contain items that are prohibited in a specific country, the customs inspection department will notify the passenger that a declaration is required. The customs inspection department also uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration based on the value and quantity of the belongings. For example, if the belongings contain items worth more than a certain amount, the customs inspection department will notify the passenger that a declaration is required. This allows the declaration procedure to be made more efficient by analyzing passengers' belongings in detail and automatically identifying items that require a customs declaration.

[0040] The customs inspection department can provide passengers with information on customs regulations at their travel destinations and support the declaration process. For example, the generation AI can provide passengers with information on customs regulations at their travel destinations and support the declaration process. For example, it can display a list of items prohibited in the destination country or items that must be declared. The customs inspection department can also provide passengers with information on customs regulations at their travel destinations and make suggestions to simplify the declaration process. For example, it can support the automatic generation of declaration forms and the preparation of necessary documents. The customs inspection department can also provide passengers with information on customs regulations at their travel destinations and notify them of points to note when completing the declaration process. For example, it can explain the tax rates for specific items and how to declare them. In this way, the generation AI can provide passengers with information on customs regulations at their travel destinations and support the declaration process.

[0041] The customs inspection department can provide multilingual customs declaration procedures using the generation AI according to the passenger's language setting. For example, the customs inspection department can provide multilingual customs declaration procedures using the generation AI according to the passenger's language setting. For example, the declaration procedures are performed in multiple languages, such as English, French, and Chinese. The customs inspection department can also provide information displayed during customs declaration procedures in multiple languages ​​using the generation AI according to the passenger's language setting. For example, the declaration form and guide signs are displayed in the passenger's native language. The customs inspection department can also provide multilingual audio guidance during customs declaration procedures using the generation AI according to the passenger's language setting. For example, the audio guide is played in the passenger's native language. This allows the provision of multilingual customs declaration procedures to accommodate international passengers.

[0042] The baggage collection unit can predict the arrival time of baggage and notify passengers of the optimal time to collect it. In the baggage collection unit, for example, the generation AI predicts the arrival time of baggage and notifies passengers of the optimal time to collect it. For example, it notifies passengers before their baggage arrives, thereby reducing waiting time. In addition, the generation AI in the baggage collection unit predicts the arrival time of baggage and suggests a collection time that suits the passenger's schedule. For example, it arranges for the baggage to arrive while the passenger is performing other procedures. In addition, in the baggage collection unit, the generation AI predicts the arrival time of baggage and provides arrival information to passengers in real time. For example, it allows passengers to check the arrival status of their baggage on a smartphone app. This makes it possible to predict the arrival time of baggage and notify them of the optimal time to collect it, thereby reducing passenger waiting time.

[0043] The luggage receiving unit can monitor the status of luggage in real time and issue an alert if an abnormality occurs. In the luggage receiving unit, for example, the generation AI monitors the status of luggage in real time and issues an alert if an abnormality occurs. For example, a notification is sent if the luggage is damaged or lost. In addition, in the luggage receiving unit, the generation AI monitors the status of luggage in real time and monitors environmental conditions such as temperature and humidity. For example, an alert is sent if the luggage is exposed to high or low temperatures. In addition, in the luggage receiving unit, the generation AI monitors the status of luggage in real time and tracks the location information of the luggage. For example, a notification is sent if the luggage deviates from the planned route. In this way, the safety of luggage can be ensured by monitoring the status of luggage in real time and issuing an alert if an abnormality occurs.

[0044] The baggage receiving unit can analyze baggage arrival information and make suggestions to optimize passenger travel plans. In the baggage receiving unit, for example, a generation AI analyzes baggage arrival information and makes suggestions to optimize passenger travel plans. For example, the passenger travel schedule is adjusted to match the baggage arrival time. In addition, in the baggage receiving unit, a generation AI analyzes baggage arrival information and suggests the passenger's next means of transportation. For example, it provides information on taxis and public transportation that can be used after the baggage arrives. In addition, in the baggage receiving unit, a generation AI analyzes baggage arrival information and updates the passenger travel plan in real time. For example, if the baggage arrives late, the passenger travel schedule is readjusted. In this way, by analyzing baggage arrival information and optimizing the travel plan, passenger travel can be made more efficient.

[0045] The baggage receiving unit can analyze baggage arrival information and automatically arrange the passenger's next means of transportation. For example, the generation AI in the baggage receiving unit analyzes baggage arrival information and automatically arranges the passenger's next means of transportation. For example, it may book a taxi to match the baggage's arrival time. The generation AI in the baggage receiving unit also analyzes baggage arrival information and makes suggestions to optimize the passenger's next means of transportation. For example, it may suggest the optimal public transportation that can be used after the baggage arrives. The generation AI in the baggage receiving unit also analyzes baggage arrival information and arranges the passenger's next means of transportation in real time. For example, if the baggage arrives late, it may change the reservation for the next means of transportation. In this way, passenger travel can be made more efficient by analyzing baggage arrival information and automatically arranging the next means of transportation.

[0046] The hearing unit can analyze passengers' past inquiry history and automatically provide the most appropriate answer. In the hearing unit, for example, the generation AI analyzes passengers' past inquiry history and automatically provides the most appropriate answer. For example, it provides an answer to a similar question based on the content of past inquiries. In addition, the hearing unit uses the generation AI to analyze passengers' past inquiry history and provide a detailed answer according to the inquiry content. For example, it provides related information based on the content of past inquiries. In addition, the hearing unit uses the generation AI to analyze passengers' past inquiry history and provide a customized answer according to the inquiry content. For example, it responds individually based on the content of past inquiries. This makes it possible to respond quickly to passengers' questions by providing the most appropriate answer based on past inquiry history.

[0047] The hearing unit can analyze the passenger's current situation and automatically suggest the necessary support. For example, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically suggests the necessary support. For example, if a passenger is lost in the airport, the system will guide them to the optimal route. In addition, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically provides the necessary support. For example, if a passenger becomes ill, medical support will be arranged. In addition, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically suggests the necessary support. For example, if a passenger loses their luggage, the system will support them in tracking the lost item. This makes it possible to quickly resolve passenger problems by suggesting the necessary support based on the current situation.

[0048] The hearing unit allows the generation AI to provide multilingual hearing procedures according to the passenger's language setting. For example, the generation AI provides multilingual hearing procedures according to the passenger's language setting. For example, the hearing procedures are conducted in multiple languages, such as English, French, and Chinese. The hearing unit also provides information displayed during the hearing procedures in multiple languages ​​according to the passenger's language setting. For example, guidance displays and answers are displayed in the passenger's native language. The hearing unit also provides audio guidance in multiple languages ​​during the hearing procedures according to the passenger's language setting. For example, audio guidance is played in the passenger's native language. This allows the generation AI to provide multilingual hearing procedures, making it possible to accommodate international passengers.

[0049] In the hearing unit, the generation AI can analyze the passenger's current location and provide optimal guidance information. In the hearing unit, for example, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, if a passenger is lost in an airport, the optimal route will be guided. In addition, in the hearing unit, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, when a passenger is heading to the boarding gate, the shortest route will be guided. In addition, in the hearing unit, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, if a passenger is looking for a facility in the airport, the route to the destination will be guided. In this way, the optimal guidance information based on the current location can be provided to support passengers' travel.

[0050] The facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. In the facial recognition unit, for example, the generation AI analyzes passengers' past facial recognition history to improve authentication accuracy. For example, it analyzes past cases of failed authentication and improves the authentication algorithm. In addition, the facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. For example, it optimizes the authentication algorithm based on past cases of successful authentication. In addition, the facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. For example, it adjusts the authentication threshold based on past authentication data. This makes it possible to strengthen security by improving authentication accuracy based on past facial recognition history.

[0051] The facial recognition unit can analyze the passenger's health condition and make suggestions to maintain the accuracy of facial recognition. For example, the generation AI in the facial recognition unit analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is tired, the accuracy of facial recognition may decrease, so the generation AI suggests taking a break. The facial recognition unit also analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is feeling unwell, the accuracy of facial recognition may decrease, so the generation AI suggests medical support. The facial recognition unit also analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is feeling stressed, the generation AI suggests ways to relax. In this way, by making suggestions to maintain the accuracy of facial recognition based on the passenger's health condition, the reliability of authentication can be improved.

[0052] The facial recognition unit can analyze passengers' facial recognition data and automatically provide various services within the airport. For example, the generation AI in the facial recognition unit analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically grant access to a lounge. The facial recognition unit also analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically complete purchase procedures at duty-free shops. The facial recognition unit also analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically complete boarding procedures. This makes it possible to improve passenger convenience by automatically providing various services based on facial recognition data.

[0053] The facial recognition unit analyzes passenger facial recognition data and can automatically perform security checks. In the facial recognition unit, for example, a generation AI analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to confirm the identity of passengers and quickly perform security checks. In addition, the facial recognition unit analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to check passengers' past security history and perform risk assessments. In addition, the facial recognition unit analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to automatically inspect passengers' belongings. This makes it possible to strengthen security by automatically performing security checks based on facial recognition data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Airport automated systems can also be equipped with a passenger baggage tracking function. For example, a GPS tag can be attached to luggage, allowing passengers to check the location of their luggage in real time via a smartphone app. Alerts can also be issued if luggage deviates from its planned route or is delayed. Furthermore, the system can monitor the condition of the luggage (temperature, humidity, etc.) and notify passengers if any abnormalities occur. This ensures the safety of passengers' luggage, allowing them to enjoy their trip with peace of mind.

[0056] Airport automation systems can also be equipped with the ability to monitor passenger health. For example, if a passenger becomes ill at the airport, they can automatically dispatch medical staff. Also, if a passenger has a chronic illness, they can arrange for medical support in advance and prepare the necessary medication and medical equipment. They can also suggest special in-flight considerations (such as allergy-friendly in-flight meals) based on the passenger's health condition. This will enable them to provide support according to their health condition and provide a safe and comfortable travel experience.

[0057] Airport automation systems can also be equipped with functions to provide passengers with information about their travel destinations. For example, they can display the weather forecast for the destination and suggest appropriate clothing and items to bring. They can also provide information about events and tourist spots at the destination to support travel planning. They can also provide transportation information for the destination (such as information about public transportation and traffic congestion) to make travel smoother. In this way, they can provide passengers with information about their travel destinations and support their travel plans.

[0058] Airport automated systems can also provide multilingual services depending on passengers' language settings. For example, check-in procedures and guide displays can be provided in multiple languages. Audio guides can also be played in passengers' native languages ​​to guide passengers around the airport. Furthermore, customs procedures and baggage claim procedures can also be handled in multiple languages ​​depending on passengers' language settings. This allows for multilingual services to be provided to accommodate international passengers.

[0059] Airport automation systems can also analyze the contents of passengers' luggage and automatically generate a list of items needed at their destination. For example, they can suggest a packing list based on the climate and activities of the destination. They can also automatically generate a list of items based on the length of stay at the destination. They can also automatically generate a list of items based on specific events or activities at the destination. This allows them to automatically generate a list of items needed at their destination and support travel preparations.

[0060] Airport automated systems can also be equipped with functions to support passengers in their customs declaration procedures. For example, they can analyze passengers' belongings in detail and automatically identify items that require customs declaration. They can also automatically identify items that require customs declaration in accordance with the regulations of a specific country or region. They can also automatically identify items that require customs declaration based on the value and quantity of the belongings. This allows passengers to analyze passengers' belongings in detail and automatically identify items that require customs declaration, thereby streamlining the declaration procedure.

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

[0062] Step 1: The check-in unit performs the passenger check-in procedure. For example, the check-in unit uses a dedicated kiosk terminal or smartphone app to check in. The check-in unit can also analyze the passenger's reservation information and perform the check-in procedure automatically. For example, when a passenger scans their passport on the kiosk terminal, the generation AI verifies the reservation information and issues a boarding pass. Step 2: The baggage inspection department inspects the baggage. For example, the baggage inspection department passes passengers' checked baggage through an automated baggage inspection system. The baggage inspection department can also use generative AI to analyze the contents of the baggage to check for dangerous or prohibited items. For example, as the baggage passes through the conveyor belt, generative AI analyzes the X-ray image and issues an alert if dangerous items are detected. Step 3: The customs inspection department performs a customs inspection. For example, when a passenger passes through customs, the generated AI analyzes the passport information and declaration details, and can automatically carry out the necessary procedures. For example, when a passenger enters their customs declaration form into a kiosk terminal, the generated AI analyzes the contents and instructs them on the necessary procedures. Step 4: The baggage claim area collects the luggage. For example, after arrival, passengers use an automated baggage claim system. In addition, the baggage claim area can automatically hand over the correct luggage by using generative AI to match the passenger's facial recognition data with the luggage tag. For example, when a passenger arrives at the baggage claim area, the facial recognition system recognizes the passenger and automatically hands over the corresponding luggage. Step 5: The hearing section responds to passengers' questions. For example, the hearing section allows the generation AI to provide guidance and handle inquiries within the airport. For example, when a passenger asks a question at an information desk within the airport, the generation AI analyzes the question and provides an appropriate answer. Step 6: The facial recognition unit verifies the identity of the passenger. For example, the facial recognition unit can use a facial recognition system to verify the identity of the passenger during various procedures at the airport. For example, the facial recognition system recognizes the passenger during check-in, boarding procedures, customs inspection, baggage claim, etc., and automatically proceeds with the procedures.

[0063] (Example 2) An airport automation system according to an embodiment of the present invention is a system that completely automates airport procedures. This system automatically reads answers written by students, summarizes them using a generative AI, calculates the similarity to the model answer, and assigns a score. This allows the airport automation system to significantly reduce the time required for airport procedures and provide passengers with a comfortable and efficient travel experience.

[0064] An airport automation system according to an embodiment includes a check-in unit, a baggage inspection unit, a customs inspection unit, a baggage claim unit, a hearing unit, and a facial recognition unit. The check-in unit performs passenger check-in procedures. For example, the check-in unit performs check-in using a dedicated kiosk terminal or a smartphone app. The check-in unit can also analyze passenger reservation information and automatically complete check-in procedures. For example, when a passenger scans their passport on the kiosk terminal, a generation AI verifies the reservation information and issues a boarding pass. The baggage inspection unit inspects baggage. For example, the baggage inspection unit passes the passenger's checked baggage through an automated baggage inspection system. The baggage inspection unit can also analyze the contents of the baggage using a generation AI to check for dangerous or prohibited items. For example, as the baggage passes through the conveyor belt, the generation AI analyzes X-ray images and issues an alert if dangerous items are detected. The customs inspection unit performs customs inspections. For example, as a passenger passes through customs, the generation AI analyzes passport information and declaration details and automatically completes the necessary procedures. For example, when a passenger enters a customs declaration form into a kiosk terminal, the generation AI analyzes the contents and instructs them on the necessary procedures. The baggage claim unit collects their luggage. For example, after arrival, passengers use an automated baggage claim system. In addition, the baggage claim unit can automatically hand over the correct luggage by matching the passenger's facial recognition data with their baggage tag. For example, when a passenger arrives at the baggage claim area, the facial recognition system recognizes the passenger and automatically hands over the corresponding luggage. The hearing unit responds to passenger questions. For example, in the hearing unit, the generation AI can provide guidance and handle inquiries within the airport. For example, when a passenger asks a question at an airport information desk, the generation AI analyzes the question and provides an appropriate answer. The facial recognition unit performs identity verification. For example, the facial recognition unit can use a facial recognition system to verify the identity of passengers during various procedures within the airport. For example, the facial recognition system recognizes passengers and automatically proceeds with procedures such as check-in, boarding procedures, customs inspection, and baggage claim.As a result, the airport automation system according to the embodiment can fully automate airport procedures and provide passengers with a comfortable and efficient travel experience. For example, the time from check-in to boarding can be shortened, and lost or misplaced baggage can be prevented. In addition, the burden on airport staff can be reduced and security can be strengthened.

[0065] The check-in unit can analyze passengers' past travel history and automatically suggest optimal seats and services. For example, the generation AI in the check-in unit analyzes passengers' past travel history and makes optimal suggestions based on seats and services they preferred in the past. For example, it will preferentially suggest window seats to passengers who prefer window seats. The generation AI in the check-in unit also analyzes passengers' past travel history and makes optimal suggestions based on airlines and alliances they have used in the past. For example, it will preferentially suggest flights of that airline to passengers who use a particular airline's mileage program. The generation AI in the check-in unit also analyzes passengers' past travel history and makes optimal suggestions based on services and options they have used in the past. For example, it will make suggestions based on in-flight meal selection and entertainment preferences. This makes it possible to provide a personalized travel experience by suggesting optimal seats and services based on passengers' past travel history.

[0066] The check-in unit can check passengers' health conditions and automatically arrange for medical support if necessary. In the check-in unit, for example, the generation AI checks passengers' health conditions and automatically arranges for medical support if necessary. For example, if a passenger has a chronic illness, medical support will be arranged in advance. In addition, the generation AI checks passengers' health conditions and suggests special considerations on board if necessary. For example, it will suggest allergy-friendly in-flight meals for passengers with allergies. In addition, the generation AI checks passengers' health conditions and arranges for medical support at the airport if necessary. For example, if a passenger falls ill at the airport, medical staff will be arranged. This makes it possible to provide medical support according to passengers' health conditions, thereby providing a safe and comfortable travel experience.

[0067] The check-in unit can use the emotion estimation function to analyze the stress level of passengers and make suggestions for providing a relaxing environment. The check-in unit, for example, uses the emotion estimation function to analyze the stress level of passengers and make suggestions for providing a relaxing environment. For example, it can suggest the use of a relaxing lounge for passengers with high stress levels. The check-in unit can also use the emotion estimation function to analyze the stress level of passengers and suggest ways to relax on board. For example, it can suggest relaxing music or entertainment. The check-in unit can also use the emotion estimation function to analyze the stress level of passengers and suggest ways to relax in the airport. For example, it can suggest the use of a relaxing spa or massage. In this way, a relaxing environment can be provided according to the passenger's stress level, thereby providing a comfortable travel experience.

[0068] During the check-in process, the generation AI can provide the passenger with weather and event information for their travel destination. During the check-in process, the generation AI can provide weather information for the passenger's travel destination. For example, the generation AI can display the weather forecast for the travel destination and suggest appropriate clothing and items to bring. During the check-in process, the generation AI can provide event information for the passenger's travel destination. For example, the generation AI can display information about events and tourist spots being held at the travel destination. During the check-in process, the generation AI can provide transportation information for the passenger's travel destination. For example, the generation AI can display information about public transportation options and traffic congestion at the travel destination. This can support the passenger's travel plans by providing them with information about their travel destination.

[0069] The check-in unit allows the generation AI to provide multilingual check-in procedures according to the passenger's language setting. For example, the check-in unit allows the generation AI to provide multilingual check-in procedures according to the passenger's language setting. For example, the check-in procedure is performed in multiple languages, such as English, French, and Chinese. The check-in unit also provides information displayed during the check-in procedure in multiple languages ​​according to the passenger's language setting. For example, the check-in unit displays boarding passes and guide displays in the passenger's native language. The check-in unit also provides audio guidance in multiple languages ​​according to the passenger's language setting. For example, the check-in unit plays audio guidance in the passenger's native language. This allows international passengers to be accommodated by providing multilingual check-in procedures.

[0070] The check-in unit can use the emotion estimation function to suggest entertainment and services that correspond to the passenger's emotions. For example, the check-in unit uses the emotion estimation function to suggest entertainment that corresponds to the passenger's emotions. For example, if the passenger wants to relax, the check-in unit suggests relaxing movies or music. The check-in unit also uses the emotion estimation function to suggest services that correspond to the passenger's emotions. For example, if the passenger is tired, the check-in unit suggests a massage service. The check-in unit also uses the emotion estimation function to suggest food and drink that correspond to the passenger's emotions. For example, if the passenger wants to refresh, the check-in unit suggests fresh juice. In this way, by providing entertainment and services that correspond to the passenger's emotions, it is possible to provide a comfortable travel experience.

[0071] The baggage inspection unit can automatically measure the weight and size of luggage and suggest the optimal loading method. For example, the generation AI in the baggage inspection unit automatically measures the weight and size of luggage and suggests the optimal loading method. For example, it instructs the system to load luggage in the optimal position based on its weight and size. The generation AI in the baggage inspection unit also automatically measures the weight and size of luggage and makes suggestions to maximize loading efficiency. For example, it makes effective use of loading space by optimizing the shape and placement of luggage. The generation AI in the baggage inspection unit also automatically measures the weight and size of luggage and makes suggestions that take into account balance when loading. For example, it instructs the system to place heavy luggage at the bottom and light luggage at the top. This improves loading efficiency by suggesting the optimal loading method based on the weight and size of the luggage.

[0072] The baggage inspection unit analyzes the contents of the baggage in detail and can automatically perform custom inspections of specific items. For example, a generation AI can analyze the contents of the baggage in detail and automatically perform custom inspections of specific items. For example, a detailed inspection can be performed on electronic devices and liquid items. The baggage inspection unit can also analyze the contents of the baggage in detail and automatically perform custom inspections of dangerous or prohibited items. For example, a detailed inspection can be performed on explosives and drugs. The baggage inspection unit can also analyze the contents of the baggage in detail and automatically perform custom inspections in accordance with the regulations of a specific country or region. For example, an inspection can be performed on items prohibited in a specific country. In this way, security can be strengthened by analyzing the contents of the baggage in detail and performing custom inspections of specific items.

[0073] The baggage inspection unit can use the emotion estimation function to suggest a baggage inspection process to reduce passenger anxiety. The baggage inspection unit, for example, uses the emotion estimation function to suggest a baggage inspection process to reduce passenger anxiety. For example, if a passenger feels anxious, the baggage inspection unit carefully explains the inspection process. The baggage inspection unit also uses the emotion estimation function to suggest relaxation methods to reduce passenger anxiety. For example, the baggage inspection unit takes measures such as playing relaxing music. The baggage inspection unit also uses the emotion estimation function to provide support to reduce passenger anxiety. For example, if a passenger feels anxious, the baggage inspection unit instructs staff to provide support. In this way, by suggesting a baggage inspection process to reduce passenger anxiety, it is possible to provide a comfortable travel experience.

[0074] The baggage inspection unit can analyze the contents of the baggage and automatically generate a list of items needed at the travel destination. For example, the baggage inspection unit uses a generation AI to analyze the contents of the baggage and automatically generate a list of items needed at the travel destination. For example, it may suggest a packing list based on the climate and activities at the travel destination. The baggage inspection unit also uses a generation AI to analyze the contents of the baggage and automatically generate a list of items based on the length of stay at the travel destination. For example, it may suggest additional clothing and toiletries for long stays. The baggage inspection unit also uses a generation AI to analyze the contents of the baggage and automatically generate a list of items based on specific events and activities at the travel destination. For example, it may suggest swimsuits and sunglasses at a beach resort. This allows the system to support travel preparations by automatically generating a list of items needed at the travel destination.

[0075] The baggage inspection department can analyze the contents of baggage and suggest security measures to prevent loss or theft. For example, the generative AI in the baggage inspection department analyzes the contents of baggage and suggests security measures to prevent loss or theft. For example, it may instruct passengers to carry valuables in separate bags. The generative AI in the baggage inspection department also analyzes the contents of baggage and suggests the use of baggage tags and locks. For example, attaching a GPS tag to baggage allows it to be tracked in case of loss. The generative AI in the baggage inspection department also analyzes the contents of baggage and suggests how to handle baggage within the airport. For example, it may instruct passengers to keep their baggage in sight at all times. This provides passengers with peace of mind by suggesting security measures to prevent baggage loss or theft.

[0076] The baggage inspection unit can use the emotion estimation function to notify passengers of the progress of the baggage inspection in real time according to their emotions. For example, the baggage inspection unit uses the emotion estimation function to notify passengers of the progress of the baggage inspection in real time according to their emotions. For example, if a passenger is feeling anxious, the baggage inspection unit provides a detailed explanation of the progress of the inspection. The baggage inspection unit also uses the emotion estimation function to visualize the progress of the baggage inspection in accordance with the passenger's emotions. For example, the inspection progress is displayed in real time on a smartphone app. The baggage inspection unit also uses the emotion estimation function to notify passengers of the progress of the baggage inspection in audio according to their emotions. For example, the inspection progress is explained by audio guidance. In this way, by notifying passengers of the progress of the baggage inspection in real time according to their emotions, a sense of security can be provided.

[0077] The customs inspection department can analyze passengers' past customs declaration history and suggest the optimal declaration method. For example, the generation AI in the customs inspection department analyzes passengers' past customs declaration history and suggests the optimal declaration method. For example, it suggests an appropriate declaration method based on the items and amounts declared in the past. The customs inspection department also analyzes passengers' past customs declaration history and makes suggestions to prevent omissions in declarations. For example, it issues a warning if there has been a past omission in declaration. The customs inspection department also analyzes passengers' past customs declaration history and makes suggestions to simplify the declaration procedure. For example, it automatically enters information that has been declared in the past. This makes it possible to streamline the declaration procedure by suggesting the optimal declaration method based on passengers' past customs declaration history.

[0078] The customs inspection department can analyze passengers' belongings in detail and automatically identify items that require a customs declaration. For example, the customs inspection department uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration. For example, if the belongings contain expensive electronic devices or jewelry, the customs inspection department will notify the passenger that a declaration is required. The customs inspection department also uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration in accordance with the regulations of a specific country or region. For example, if the belongings contain items that are prohibited in a specific country, the customs inspection department will notify the passenger that a declaration is required. The customs inspection department also uses a generation AI to analyze passengers' belongings in detail and automatically identify items that require a customs declaration based on the value and quantity of the belongings. For example, if the belongings contain items worth more than a certain amount, the customs inspection department will notify the passenger that a declaration is required. This allows the declaration procedure to be made more efficient by analyzing passengers' belongings in detail and automatically identifying items that require a customs declaration.

[0079] The customs inspection department can use the emotion estimation function to suggest a customs inspection process to reduce passenger anxiety. The customs inspection department, for example, uses the emotion estimation function to suggest a customs inspection process to reduce passenger anxiety. For example, if a passenger feels anxious, the customs inspection department can carefully explain the customs inspection procedures. The customs inspection department also uses the emotion estimation function to suggest relaxation methods to reduce passenger anxiety. For example, the customs inspection department can take measures such as playing relaxing music. The customs inspection department also uses the emotion estimation function to provide support to reduce passenger anxiety. For example, if a passenger feels anxious, the customs inspection department can instruct customs staff to provide support. In this way, by suggesting a customs inspection process to reduce passenger anxiety, it is possible to provide a comfortable travel experience.

[0080] The customs inspection department can provide passengers with information on customs regulations at their travel destinations and support the declaration process. For example, the generation AI can provide passengers with information on customs regulations at their travel destinations and support the declaration process. For example, it can display a list of items prohibited in the destination country or items that must be declared. The customs inspection department can also provide passengers with information on customs regulations at their travel destinations and make suggestions to simplify the declaration process. For example, it can support the automatic generation of declaration forms and the preparation of necessary documents. The customs inspection department can also provide passengers with information on customs regulations at their travel destinations and notify them of points to note when completing the declaration process. For example, it can explain the tax rates for specific items and how to declare them. In this way, the generation AI can provide passengers with information on customs regulations at their travel destinations and support the declaration process.

[0081] The customs inspection department can provide multilingual customs declaration procedures using the generation AI according to the passenger's language setting. For example, the customs inspection department can provide multilingual customs declaration procedures using the generation AI according to the passenger's language setting. For example, the declaration procedures are performed in multiple languages, such as English, French, and Chinese. The customs inspection department can also provide information displayed during customs declaration procedures in multiple languages ​​using the generation AI according to the passenger's language setting. For example, the declaration form and guide signs are displayed in the passenger's native language. The customs inspection department can also provide multilingual audio guidance during customs declaration procedures using the generation AI according to the passenger's language setting. For example, the audio guide is played in the passenger's native language. This allows the provision of multilingual customs declaration procedures to accommodate international passengers.

[0082] The customs inspection department can use the emotion estimation function to notify passengers of the progress of the customs inspection in real time according to their emotions. For example, the customs inspection department uses the emotion estimation function to notify passengers of the progress of the customs inspection in real time according to their emotions. For example, if a passenger feels anxious, the customs inspection department provides a detailed explanation of the progress of the inspection. The customs inspection department also uses the emotion estimation function to visualize the progress of the customs inspection in accordance with the passenger's emotions. For example, the inspection progress is displayed in real time on a smartphone app. The customs inspection department also uses the emotion estimation function to notify passengers of the progress of the customs inspection by voice according to their emotions. For example, the inspection progress is explained by voice guidance. In this way, passengers can feel reassured by being notified of the progress of the customs inspection in real time according to their emotions.

[0083] The baggage collection unit can predict the arrival time of baggage and notify passengers of the optimal time to collect it. In the baggage collection unit, for example, the generation AI predicts the arrival time of baggage and notifies passengers of the optimal time to collect it. For example, it notifies passengers before their baggage arrives, thereby reducing waiting time. In addition, the generation AI in the baggage collection unit predicts the arrival time of baggage and suggests a collection time that suits the passenger's schedule. For example, it arranges for the baggage to arrive while the passenger is performing other procedures. In addition, in the baggage collection unit, the generation AI predicts the arrival time of baggage and provides arrival information to passengers in real time. For example, it allows passengers to check the arrival status of their baggage on a smartphone app. This makes it possible to predict the arrival time of baggage and notify them of the optimal time to collect it, thereby reducing passenger waiting time.

[0084] The luggage receiving unit can monitor the status of luggage in real time and issue an alert if an abnormality occurs. In the luggage receiving unit, for example, the generation AI monitors the status of luggage in real time and issues an alert if an abnormality occurs. For example, a notification is sent if the luggage is damaged or lost. In addition, in the luggage receiving unit, the generation AI monitors the status of luggage in real time and monitors environmental conditions such as temperature and humidity. For example, an alert is sent if the luggage is exposed to high or low temperatures. In addition, in the luggage receiving unit, the generation AI monitors the status of luggage in real time and tracks the location information of the luggage. For example, a notification is sent if the luggage deviates from the planned route. In this way, the safety of luggage can be ensured by monitoring the status of luggage in real time and issuing an alert if an abnormality occurs.

[0085] The baggage claim unit can use the emotion estimation function to suggest a baggage claim process to reduce passenger anxiety. The baggage claim unit, for example, uses the emotion estimation function to suggest a baggage claim process to reduce passenger anxiety. For example, if a passenger feels anxious, the baggage claim unit carefully explains the baggage claim procedure. The baggage claim unit also uses the emotion estimation function to suggest relaxation methods to reduce passenger anxiety. For example, the baggage claim unit may play relaxing music or take other measures. The baggage claim unit also uses the emotion estimation function to provide support to reduce passenger anxiety. For example, if a passenger feels anxious, the baggage claim unit may instruct staff to provide support. In this way, by suggesting a baggage claim process to reduce passenger anxiety, it is possible to provide a comfortable travel experience.

[0086] The baggage receiving unit can analyze baggage arrival information and make suggestions to optimize passenger travel plans. In the baggage receiving unit, for example, a generation AI analyzes baggage arrival information and makes suggestions to optimize passenger travel plans. For example, the passenger travel schedule is adjusted to match the baggage arrival time. In addition, in the baggage receiving unit, a generation AI analyzes baggage arrival information and suggests the passenger's next means of transportation. For example, it provides information on taxis and public transportation that can be used after the baggage arrives. In addition, in the baggage receiving unit, a generation AI analyzes baggage arrival information and updates the passenger travel plan in real time. For example, if the baggage arrives late, the passenger travel schedule is readjusted. In this way, by analyzing baggage arrival information and optimizing the travel plan, passenger travel can be made more efficient.

[0087] The baggage receiving unit can analyze baggage arrival information and automatically arrange the passenger's next means of transportation. For example, the generation AI in the baggage receiving unit analyzes baggage arrival information and automatically arranges the passenger's next means of transportation. For example, it may book a taxi to match the baggage's arrival time. The generation AI in the baggage receiving unit also analyzes baggage arrival information and makes suggestions to optimize the passenger's next means of transportation. For example, it may suggest the optimal public transportation that can be used after the baggage arrives. The generation AI in the baggage receiving unit also analyzes baggage arrival information and arranges the passenger's next means of transportation in real time. For example, if the baggage arrives late, it may change the reservation for the next means of transportation. In this way, passenger travel can be made more efficient by analyzing baggage arrival information and automatically arranging the next means of transportation.

[0088] The baggage claiming unit can use the emotion estimation function to notify passengers of the progress of baggage claim in real time according to their emotions. The baggage claiming unit, for example, uses the emotion estimation function to notify passengers of the progress of baggage claim in real time according to their emotions. For example, if a passenger is feeling anxious, the baggage claiming unit provides a detailed explanation of the progress of baggage claim. The baggage claiming unit also uses the emotion estimation function to visualize the progress of baggage claim in accordance with the passenger's emotions. For example, the progress of baggage claiming is displayed in real time on a smartphone app. The baggage claiming unit also uses the emotion estimation function to notify passengers of the progress of baggage claim in audio according to their emotions. For example, the progress of baggage claiming is explained by audio guidance. This allows passengers to feel a sense of security by notifying them of the progress of baggage claim in real time according to their emotions.

[0089] The hearing unit can analyze passengers' past inquiry history and automatically provide the most appropriate answer. In the hearing unit, for example, the generation AI analyzes passengers' past inquiry history and automatically provides the most appropriate answer. For example, it provides an answer to a similar question based on the content of past inquiries. In addition, the hearing unit uses the generation AI to analyze passengers' past inquiry history and provide a detailed answer according to the inquiry content. For example, it provides related information based on the content of past inquiries. In addition, the hearing unit uses the generation AI to analyze passengers' past inquiry history and provide a customized answer according to the inquiry content. For example, it responds individually based on the content of past inquiries. This makes it possible to respond quickly to passengers' questions by providing the most appropriate answer based on past inquiry history.

[0090] The hearing unit can analyze the passenger's current situation and automatically suggest the necessary support. For example, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically suggests the necessary support. For example, if a passenger is lost in the airport, the system will guide them to the optimal route. In addition, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically provides the necessary support. For example, if a passenger becomes ill, medical support will be arranged. In addition, in the hearing unit, the generation AI analyzes the passenger's current situation and automatically suggests the necessary support. For example, if a passenger loses their luggage, the system will support them in tracking the lost item. This makes it possible to quickly resolve passenger problems by suggesting the necessary support based on the current situation.

[0091] The hearing unit can use the emotion estimation function to provide an answer that corresponds to the passenger's emotion, thereby reducing stress. The hearing unit, for example, uses the emotion estimation function to provide an answer that corresponds to the passenger's emotion, thereby reducing stress. For example, if the passenger is feeling anxious, the hearing unit provides an answer that reassures the passenger. The hearing unit also uses the emotion estimation function to provide an answer that corresponds to the passenger's emotion, thereby reducing stress. For example, if the passenger is feeling angry, the hearing unit provides an answer that responds calmly. The hearing unit also uses the emotion estimation function to provide an answer that corresponds to the passenger's emotion, thereby reducing stress. For example, if the passenger is tired, the hearing unit provides an answer that helps the passenger to relax. In this way, by providing an answer that corresponds to the passenger's emotion, stress can be reduced and a comfortable travel experience can be provided.

[0092] The hearing unit allows the generation AI to provide multilingual hearing procedures according to the passenger's language setting. For example, the generation AI provides multilingual hearing procedures according to the passenger's language setting. For example, the hearing procedures are conducted in multiple languages, such as English, French, and Chinese. The hearing unit also provides information displayed during the hearing procedures in multiple languages ​​according to the passenger's language setting. For example, guidance displays and answers are displayed in the passenger's native language. The hearing unit also provides audio guidance in multiple languages ​​during the hearing procedures according to the passenger's language setting. For example, audio guidance is played in the passenger's native language. This allows the generation AI to provide multilingual hearing procedures, making it possible to accommodate international passengers.

[0093] In the hearing unit, the generation AI can analyze the passenger's current location and provide optimal guidance information. In the hearing unit, for example, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, if a passenger is lost in an airport, the optimal route will be guided. In addition, in the hearing unit, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, when a passenger is heading to the boarding gate, the shortest route will be guided. In addition, in the hearing unit, the generation AI analyzes the passenger's current location and provides optimal guidance information. For example, if a passenger is looking for a facility in the airport, the route to the destination will be guided. In this way, the optimal guidance information based on the current location can be provided to support passengers' travel.

[0094] The hearing unit can use the emotion estimation function to provide guidance information according to the passenger's emotion in real time. The hearing unit, for example, uses the emotion estimation function to provide guidance information according to the passenger's emotion in real time. For example, if a passenger is feeling anxious, the hearing unit provides guidance information that reassures the passenger. The hearing unit also uses the emotion estimation function to provide guidance information according to the passenger's emotion in real time. For example, if a passenger is feeling angry, the hearing unit provides guidance information that responds calmly. The hearing unit also uses the emotion estimation function to provide guidance information according to the passenger's emotion in real time. For example, if a passenger is tired, the hearing unit provides guidance information that helps the passenger to relax. In this way, by providing guidance information according to the passenger's emotion in real time, a sense of security can be provided.

[0095] The facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. In the facial recognition unit, for example, the generation AI analyzes passengers' past facial recognition history to improve authentication accuracy. For example, it analyzes past cases of failed authentication and improves the authentication algorithm. In addition, the facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. For example, it optimizes the authentication algorithm based on past cases of successful authentication. In addition, the facial recognition unit can analyze passengers' past facial recognition history to improve authentication accuracy. For example, it adjusts the authentication threshold based on past authentication data. This makes it possible to strengthen security by improving authentication accuracy based on past facial recognition history.

[0096] The facial recognition unit can analyze the passenger's health condition and make suggestions to maintain the accuracy of facial recognition. For example, the generation AI in the facial recognition unit analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is tired, the accuracy of facial recognition may decrease, so the generation AI suggests taking a break. The facial recognition unit also analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is feeling unwell, the accuracy of facial recognition may decrease, so the generation AI suggests medical support. The facial recognition unit also analyzes the passenger's health condition and makes suggestions to maintain the accuracy of facial recognition. For example, if the passenger is feeling stressed, the generation AI suggests ways to relax. In this way, by making suggestions to maintain the accuracy of facial recognition based on the passenger's health condition, the reliability of authentication can be improved.

[0097] The face authentication unit can use the emotion estimation function to suggest a face authentication process according to the passenger's emotion. The face authentication unit, for example, uses the emotion estimation function to suggest a face authentication process according to the passenger's emotion. For example, if the passenger is feeling anxious, the face authentication unit will carefully explain the face authentication procedure. The face authentication unit also uses the emotion estimation function to suggest a face authentication process according to the passenger's emotion. For example, if the passenger is feeling angry, the face authentication unit will suggest a face authentication process that responds calmly. The face authentication unit also uses the emotion estimation function to suggest a face authentication process according to the passenger's emotion. For example, if the passenger is tired, the face authentication unit will suggest a face authentication process that will help the passenger to relax. In this way, by suggesting a face authentication process according to the passenger's emotion, the reliability of authentication can be improved.

[0098] The facial recognition unit can analyze passengers' facial recognition data and automatically provide various services within the airport. For example, the generation AI in the facial recognition unit analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically grant access to a lounge. The facial recognition unit also analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically complete purchase procedures at duty-free shops. The facial recognition unit also analyzes passengers' facial recognition data and automatically provides various services within the airport. For example, facial recognition can be used to automatically complete boarding procedures. This makes it possible to improve passenger convenience by automatically providing various services based on facial recognition data.

[0099] The facial recognition unit analyzes passenger facial recognition data and can automatically perform security checks. In the facial recognition unit, for example, a generation AI analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to confirm the identity of passengers and quickly perform security checks. In addition, the facial recognition unit analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to check passengers' past security history and perform risk assessments. In addition, the facial recognition unit analyzes passenger facial recognition data and automatically performs security checks. For example, facial recognition is used to automatically inspect passengers' belongings. This makes it possible to strengthen security by automatically performing security checks based on facial recognition data.

[0100] The facial authentication unit can use the emotion estimation function to notify the passenger of the progress of facial authentication in real time according to their emotions. For example, the facial authentication unit uses the emotion estimation function to notify the passenger of the progress of facial authentication in real time according to their emotions. For example, if the passenger is feeling anxious, the facial authentication unit may explain the progress of facial authentication in detail. The facial authentication unit also uses the emotion estimation function to visualize the progress of facial authentication in accordance with the passenger's emotions. For example, the progress of facial authentication may be displayed in real time on a smartphone app. The facial authentication unit also uses the emotion estimation function to notify the passenger of the progress of facial authentication in audio according to their emotions. For example, the progress of facial authentication may be explained by audio guidance. In this way, the progress of facial authentication in accordance with the passenger's emotions may be notified in real time, providing a sense of security.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] Airport automated systems can also be equipped with a passenger baggage tracking function. For example, a GPS tag can be attached to luggage, allowing passengers to check the location of their luggage in real time via a smartphone app. Alerts can also be issued if luggage deviates from its planned route or is delayed. Furthermore, the system can monitor the condition of the luggage (temperature, humidity, etc.) and notify passengers if any abnormalities occur. This ensures the safety of passengers' luggage, allowing them to enjoy their trip with peace of mind.

[0103] Airport automation systems can also be equipped with the ability to monitor passenger health. For example, if a passenger becomes ill at the airport, they can automatically dispatch medical staff. Also, if a passenger has a chronic illness, they can arrange for medical support in advance and prepare the necessary medication and medical equipment. They can also suggest special in-flight considerations (such as allergy-friendly in-flight meals) based on the passenger's health condition. This will enable them to provide support according to their health condition and provide a safe and comfortable travel experience.

[0104] The airport automation system can also estimate passengers' emotions and provide services to reduce stress. For example, if a passenger feels stressed, it can suggest a lounge where they can relax. If a passenger feels anxious, it can provide reassuring information. Furthermore, if a passenger feels tired, it can suggest relaxing music or entertainment. In this way, it is possible to provide services that correspond to passengers' emotions and provide a comfortable travel experience.

[0105] Airport automation systems can also be equipped with functions to provide passengers with information about their travel destinations. For example, they can display the weather forecast for the destination and suggest appropriate clothing and items to bring. They can also provide information about events and tourist spots at the destination to support travel planning. They can also provide transportation information for the destination (such as information about public transportation and traffic congestion) to make travel smoother. In this way, they can provide passengers with information about their travel destinations and support their travel plans.

[0106] Airport automated systems can also provide multilingual services depending on passengers' language settings. For example, check-in procedures and guide displays can be provided in multiple languages. Audio guides can also be played in passengers' native languages ​​to guide passengers around the airport. Furthermore, customs procedures and baggage claim procedures can also be handled in multiple languages ​​depending on passengers' language settings. This allows for multilingual services to be provided to accommodate international passengers.

[0107] The airport automation system can also provide entertainment tailored to passengers' emotions. For example, if a passenger wants to relax, it can suggest relaxing movies or music. If a passenger is excited, it can suggest action movies or energetic music. Furthermore, if a passenger is sad, it can provide entertainment that will brighten their mood. This allows the system to provide entertainment tailored to passengers' emotions and provide a comfortable travel experience.

[0108] Airport automation systems can also analyze the contents of passengers' luggage and automatically generate a list of items needed at their destination. For example, they can suggest a packing list based on the climate and activities of the destination. They can also automatically generate a list of items based on the length of stay at the destination. They can also automatically generate a list of items based on specific events or activities at the destination. This allows them to automatically generate a list of items needed at their destination and support travel preparations.

[0109] The airport automated system can also suggest baggage inspection processes that correspond to the passenger's emotions. For example, if a passenger feels anxious, the system can carefully explain the inspection process. If a passenger feels stressed, the system can also play relaxing music. Furthermore, if a passenger feels angry, the system can instruct the passenger to respond calmly. In this way, the system can suggest baggage inspection processes that correspond to the passenger's emotions, providing a comfortable travel experience.

[0110] Airport automated systems can also be equipped with functions to support passengers in their customs declaration procedures. For example, they can analyze passengers' belongings in detail and automatically identify items that require customs declaration. They can also automatically identify items that require customs declaration in accordance with the regulations of a specific country or region. They can also automatically identify items that require customs declaration based on the value and quantity of the belongings. This allows passengers to analyze passengers' belongings in detail and automatically identify items that require customs declaration, thereby streamlining the declaration procedure.

[0111] The airport automated system can also suggest customs inspection procedures that correspond to the passenger's emotions. For example, if a passenger feels anxious, it can carefully explain the customs inspection procedures. If a passenger feels stressed, it can also play relaxing music. Furthermore, if a passenger feels angry, it can instruct the passenger to remain calm. In this way, it can suggest customs inspection procedures that correspond to the passenger's emotions and provide a comfortable travel experience.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The check-in unit performs the passenger check-in procedure. For example, the check-in unit uses a dedicated kiosk terminal or smartphone app to check in. The check-in unit can also analyze the passenger's reservation information and perform the check-in procedure automatically. For example, when a passenger scans their passport on the kiosk terminal, the generation AI verifies the reservation information and issues a boarding pass. Step 2: The baggage inspection department inspects the baggage. For example, the baggage inspection department passes passengers' checked baggage through an automated baggage inspection system. The baggage inspection department can also use generative AI to analyze the contents of the baggage to check for dangerous or prohibited items. For example, as the baggage passes through the conveyor belt, generative AI analyzes the X-ray image and issues an alert if dangerous items are detected. Step 3: The customs inspection department performs a customs inspection. For example, when a passenger passes through customs, the generated AI analyzes the passport information and declaration details, and can automatically carry out the necessary procedures. For example, when a passenger enters their customs declaration form into a kiosk terminal, the generated AI analyzes the contents and instructs them on the necessary procedures. Step 4: The baggage claim area collects the luggage. For example, after arrival, passengers use an automated baggage claim system. In addition, the baggage claim area can automatically hand over the correct luggage by using generative AI to match the passenger's facial recognition data with the luggage tag. For example, when a passenger arrives at the baggage claim area, the facial recognition system recognizes the passenger and automatically hands over the corresponding luggage. Step 5: The hearing section responds to passengers' questions. For example, the hearing section allows the generation AI to provide guidance and handle inquiries within the airport. For example, when a passenger asks a question at an information desk within the airport, the generation AI analyzes the question and provides an appropriate answer. Step 6: The facial recognition unit verifies the identity of the passenger. For example, the facial recognition unit can use a facial recognition system to verify the identity of the passenger during various procedures at the airport. For example, the facial recognition system recognizes the passenger during check-in, boarding procedures, customs inspection, baggage claim, etc., and automatically proceeds with the procedures.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] 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.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A check-in department where check-in procedures are carried out; a baggage inspection department that inspects baggage; Customs Inspection Department, which conducts customs inspections; a baggage receiving section for receiving baggage; A hearing department that responds to passenger questions, A face authentication unit that performs identity verification. A system characterized by:

2. The check-in unit Analyze the passenger's past travel history and automatically suggest the most suitable seats and services.

2. The system of claim 1.

3. The check-in unit Check the passenger's health status and automatically arrange medical assistance if necessary 2. The system of claim 1.

4. The check-in unit Analyze the passenger's stress level and make suggestions to provide a relaxing environment 2. The system of claim 1.

5. The check-in unit During the check-in process, the AI ​​generator provides weather and event information for the passenger's destination.

2. The system of claim 1.

6. The check-in unit The generative AI provides a multilingual check-in process based on the passenger's language preference.

2. The system of claim 1.

7. The check-in unit Propose entertainment and services that correspond to the passenger's emotions 2. The system of claim 1.

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