system

Abstract

The system according to the embodiment aims to fully automate baggage inspection, thereby improving human resources at airports and providing better service to passengers. [Solution] A system according to an embodiment includes a scanning unit, an analyzing unit, a detecting unit, and a notifying unit. The scanning unit scans the contents of baggage. The analyzing unit analyzes the data obtained by the scanning unit. The detecting unit detects dangerous materials and prohibited items based on the data analyzed by the analyzing unit. The notifying unit notifies the user of the inspection results obtained by the detecting unit.

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] With conventional technology, baggage inspection at airports is not fully automated, posing challenges in securing human resources and increasing passenger waiting times.

[0005] The system according to the embodiment aims to fully automate baggage inspection, thereby improving human resources at airports and providing better service to passengers. [Means for solving the problem]

[0006] The system according to the embodiment includes a scanning unit, an analyzing unit, a detecting unit, and a notifying unit. The scanning unit scans the contents of baggage. The analyzing unit analyzes the data obtained by the scanning unit. The detecting unit detects dangerous materials and prohibited items based on the data analyzed by the analyzing unit. The notifying unit notifies the inspection results obtained by the detecting unit. [Effects of the Invention]

[0007] The system according to the embodiment can fully automate baggage inspection, improving the human resources of airports and improving services 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) A baggage inspection system according to an embodiment of the present invention fully automates airport baggage inspection using generative AI. When baggage arrives at the inspection area, the generative AI scans the contents of the bag to check for any dangerous or prohibited items. The generative AI analyzes data from X-ray images and other sensors to thoroughly examine the contents of the bag. For example, if the bag contains dangerous items such as knives or explosives, the generative AI immediately issues an alert and moves the baggage to a separate inspection area. Next, the generative AI analyzes the contents of the baggage to ensure that the passenger's belongings comply with airline regulations. If any regulations are violated, such as the amount of liquid or the type of electronic device, the generative AI issues an alert and requests that the baggage be reinspected. Furthermore, the generative AI notifies passengers of the baggage inspection results in real time. Passengers can check on their smartphones or airport displays whether their baggage has passed inspection. This allows passengers to monitor the progress of baggage inspection and shortens waiting times. This system will fully automate baggage inspection at airports, freeing up manpower and shortening passenger waiting times. Generative AI will also perform detailed analysis of baggage contents, improving the accuracy of detecting dangerous and prohibited items and enhancing airport safety. This allows the baggage inspection system to free up manpower and shorten passenger waiting times.

[0029] A baggage inspection system according to an embodiment includes a scanning unit, an analyzing unit, a detecting unit, and a notifying unit. The scanning unit scans the contents of baggage. The scanning unit scans the contents of baggage using, for example, X-ray images or data obtained from other sensors. The scanning unit can scan the interior of the baggage in detail using, for example, an X-ray device. The scanning unit can also scan the contents of the baggage using a metal detector or a chemical sensor. For example, the scanning unit can scan the interior of the baggage in detail using an X-ray device and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. The analyzing unit analyzes the data obtained by the scanning unit. For example, the analyzing unit can analyze the scan data using a generating AI to check the contents of the baggage in detail. For example, the generating AI can analyze the contents of the baggage based on the scan data to detect dangerous items and prohibited items. The analyzing unit can also analyze the scan data using a generating AI to check the contents of the baggage in detail. For example, the analysis unit can have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous objects or prohibited items. The detection unit can detect dangerous objects or prohibited items based on the data analyzed by the analysis unit. For example, the detection unit can use the generation AI to detect dangerous objects or prohibited items based on the analysis data and issue a warning. The detection unit can also use the generation AI to detect dangerous objects or prohibited items based on the analysis data. For example, the detection unit can use the generation AI to detect dangerous objects or prohibited items based on the analysis data and issue a warning. The notification unit notifies the inspection results obtained by the detection unit. For example, the notification unit can notify passengers of the inspection results in real time. The notification unit can notify passengers of the inspection results using, for example, a smartphone or an airport display. The notification unit can also notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using, for example, a smartphone or an airport display. As a result, the baggage inspection system according to the embodiment can scan, analyze, detect, and notify the contents of baggage.

[0030] The scanning unit can scan the contents of baggage using data obtained from X-ray images or other sensors. The scanning unit can, for example, use an X-ray device to perform a detailed scan of the interior of the baggage. The scanning unit can, for example, use an X-ray device to perform a detailed scan of the interior of the baggage and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. For example, the scanning unit can use an X-ray device to perform a detailed scan of the interior of the baggage and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. This improves the accuracy of detecting dangerous or prohibited items by scanning the contents of the baggage in detail. Some or all of the above-described processing in the scanning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the scanning unit can input image data acquired by an X-ray device into a generation AI, which can analyze the image data to scan the contents of the baggage.

[0031] The analysis unit analyzes the data obtained from the scanning unit and can check the contents of the baggage in detail. The analysis unit analyzes the scan data using, for example, a generation AI and checks the contents of the baggage in detail. The analysis unit can, for example, have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous or prohibited items. The analysis unit can also analyze the scan data using the generation AI and check the contents of the baggage in detail. For example, the analysis unit can have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous or prohibited items. This improves the accuracy of detecting dangerous or prohibited items by checking the contents of the baggage in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the data obtained by the scanning unit into the generation AI, and the generation AI analyzes the data and checks the contents of the baggage in detail.

[0032] The detection unit can detect dangerous materials and prohibited items based on the data obtained from the analysis unit. The detection unit can, for example, use a generation AI to detect dangerous materials and prohibited items based on the analysis data. The detection unit can, for example, use a generation AI to detect dangerous materials and prohibited items based on the analysis data and issue a warning. The detection unit can also detect dangerous materials and prohibited items based on the analysis data using a generation AI. For example, the detection unit can use a generation AI to detect dangerous materials and prohibited items based on the analysis data and issue a warning. This improves the accuracy of detecting dangerous materials and prohibited items. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input data obtained by the analysis unit into a generation AI, and the generation AI can analyze the data to detect dangerous materials and prohibited items.

[0033] The notification unit can notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using a smartphone or an airport display. The notification unit can also notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using a smartphone or an airport display. This allows passengers to understand the progress of the baggage inspection and shorten their waiting time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the inspection results obtained by the detection unit into a generation AI, which analyzes the data and notifies the passengers of the inspection results.

[0034] The notification unit can notify the passenger of the inspection results using a smartphone or an airport display. The notification unit can notify the passenger of the inspection results using, for example, a smartphone or an airport display. The notification unit can also notify the passenger of the inspection results in real time. For example, the notification unit can notify the passenger of the inspection results using a smartphone or an airport display. This makes it easier for the passenger to understand the progress of the baggage inspection. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the notification unit can input the inspection results acquired by the detection unit into the generation AI, which can analyze the data and notify the passenger of the inspection results.

[0035] The scanning unit can automatically change the scanning method depending on the shape and material of the baggage. For example, if the baggage is made of metal, the scanning unit adjusts the intensity of the X-ray scan to obtain a detailed image. For example, if the baggage is made of cloth, the scanning unit can lower the intensity of the X-ray scan to increase transparency. Furthermore, if the baggage is made of multiple materials, the scanning unit can combine the scanning methods optimal for each material. For example, if the baggage is made of metal, the scanning unit adjusts the intensity of the X-ray scan to obtain a detailed image. This improves the accuracy of the scan by applying the optimal scanning method depending on the shape and material of the baggage. Some or all of the above-mentioned processing in the scanning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scanning unit can input data on the shape and material of the baggage into a generation AI, which can analyze the data and determine the optimal scanning method.

[0036] The scanning unit can improve the accuracy of the scan by referring to the baggage owner's past travel history during scanning. For example, the scanning unit can increase the accuracy of the scan if the baggage owner has a history of bringing in dangerous goods in the past. For example, the scanning unit can apply normal scanning accuracy if the baggage owner is a frequent traveler. The scanning unit can also set the scanning accuracy to standard if the baggage owner is traveling for the first time. For example, the scanning unit can increase the scanning accuracy if the baggage owner has a history of bringing in dangerous goods in the past. This improves the accuracy of the scan by referring to the past travel history. Some or all of the above-described processing in the scanning unit can be performed using, or without, a generation AI. For example, the scanning unit can input the baggage owner's past travel history data into the generation AI, which can analyze the data to improve the accuracy of the scan.

[0037] During scanning, the scanning unit can adjust scan settings based on the weight and size of the baggage. For example, if the baggage is heavy, the scanning unit can increase the intensity of the X-ray scan to obtain a detailed image. For example, if the baggage is light, the scanning unit can decrease the intensity of the X-ray scan to increase penetration. Furthermore, if the baggage is large, the scanning unit can widen the scan range to cover the entire bag. For example, if the baggage is heavy, the scanning unit can increase the intensity of the X-ray scan to obtain a detailed image. This improves the accuracy of the scan by applying optimal scan settings according to the weight and size of the baggage. Some or all of the above-described processing in the scanning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scanning unit can input data on the weight and size of the baggage into a generation AI, which can analyze the data and adjust the scan settings.

[0038] The scanning unit can analyze the social media activity of the baggage owner during scanning and incorporate related information into the scan. For example, if the baggage owner has recently posted about dangerous goods, the scanning unit can increase the accuracy of the scan. For example, if the baggage owner frequently posts about travel, the scanning unit can apply normal scan accuracy. The scanning unit can also set the scan accuracy to standard if the baggage owner does not use social media. For example, the scanning unit can increase the scan accuracy if the baggage owner has recently posted about dangerous goods. In this way, the scanning accuracy is improved by analyzing social media activity. Some or all of the above-described processing in the scanning unit can be performed using, or without, a generation AI. For example, the scanning unit can input the baggage owner's social media activity data into a generation AI, which can analyze the data to improve the scan accuracy.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the contents of the baggage. For example, if the contents of the baggage are electronic devices, the analysis unit can apply an electromagnetic wave analysis algorithm. For example, if the contents of the baggage are liquid, the analysis unit can apply a chemical component analysis algorithm. Furthermore, if the contents of the baggage are metal products, the analysis unit can also apply a metal detection algorithm. For example, if the contents of the baggage are electronic devices, the analysis unit applies an electromagnetic wave analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the contents of the baggage. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of the contents of the baggage into a generation AI, which analyzes the data and applies the optimal analysis algorithm.

[0040] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the contents of the baggage. For example, if the contents of the baggage consist of multiple electronic devices, the analysis unit can perform the analysis while taking into account mutual interference. For example, if the contents of the baggage consist of liquid and metal products, the analysis unit can perform the analysis while taking into account the possibility of chemical reactions. Furthermore, if the contents of the baggage consist of items of different categories, the analysis unit can also perform the analysis while taking into account the characteristics of each category. For example, if the contents of the baggage consist of multiple electronic devices, the analysis unit can perform the analysis while taking into account mutual interference. This improves the accuracy of the analysis by taking into account the interrelationships between the contents of the baggage. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input interrelationship data between the contents of the baggage into a generation AI, which can analyze the data and improve the accuracy of the analysis.

[0041] During analysis, the analysis unit can adjust the analysis settings based on the time the contents of the baggage were submitted. For example, if the baggage was submitted early in the morning, the analysis unit can set stricter analysis settings than usual. For example, if the baggage was submitted late at night, the analysis unit can set lenient analysis settings than usual. The analysis unit can also set standard analysis settings if the baggage was submitted during peak hours. For example, if the baggage was submitted early in the morning, the analysis unit can set stricter analysis settings than usual. This improves the accuracy of the analysis by applying optimal analysis settings according to the time the baggage was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the time the baggage was submitted into the generation AI, which can analyze the data and adjust the analysis settings.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the contents of the baggage. For example, if the contents of the baggage are new electronic devices, the analysis unit can perform the analysis by referring to the related literature. For example, if the contents of the baggage are special chemical substances, the analysis unit can perform the analysis by referring to the related literature. Furthermore, if the contents of the baggage are unknown items, the analysis unit can also perform the analysis by referring to the related literature. For example, if the contents of the baggage are new electronic devices, the analysis unit can perform the analysis by referring to the related literature. In this way, by referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input literature data related to the contents of the baggage into the generation AI, which then analyzes the data to improve the accuracy of the analysis.

[0043] The detection unit can apply different detection algorithms depending on the risk level of the contents of the baggage during detection. For example, if the contents of the baggage are high-risk items, the detection unit can apply a strict detection algorithm. For example, if the contents of the baggage are medium-risk items, the detection unit can apply a normal detection algorithm. The detection unit can also apply a simple detection algorithm if the contents of the baggage are low-risk items. For example, the detection unit applies a strict detection algorithm if the contents of the baggage are high-risk items. This improves detection accuracy by applying an optimal detection algorithm depending on the risk level of the contents of the baggage. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input risk level data of the contents of the baggage into a generation AI, which analyzes the data and applies an optimal detection algorithm.

[0044] The detection unit can improve detection accuracy by taking into account the interrelationships between the contents of the baggage during detection. For example, if the contents of the baggage consist of multiple electronic devices, the detection unit can perform detection while taking into account mutual interference. For example, if the contents of the baggage consist of liquid and metal products, the detection unit can perform detection while taking into account the possibility of chemical reactions. Furthermore, if the contents of the baggage consist of items of different categories, the detection unit can also perform detection while taking into account the characteristics of each category. For example, if the contents of the baggage consist of multiple electronic devices, the detection unit can perform detection while taking into account mutual interference. This improves detection accuracy by taking into account the interrelationships between the contents of the baggage. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input interrelationship data between the contents of the baggage into a generation AI, which can analyze the data and improve detection accuracy.

[0045] The detection unit can perform detection while taking into account the geographical distribution of the contents of the baggage. For example, if the contents of the baggage are common items in a specific region, the detection unit can perform detection while taking into account the regulations of that region. For example, if the contents of the baggage are internationally regulated items, the detection unit can perform detection while taking into account international regulations. Furthermore, if the contents of the baggage are items unique to a region, the detection unit can also perform detection while taking into account the characteristics of that region. For example, if the contents of the baggage are common items in a specific region, the detection unit can perform detection while taking into account the regulations of that region. This improves the accuracy of detection by taking into account the geographical distribution of the contents of the baggage. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the detection unit can input geographical distribution data of the contents of the baggage into a generation AI, which can analyze the data and perform detection.

[0046] The detection unit can improve the accuracy of detection by referring to literature related to the contents of the baggage during detection. For example, if the contents of the baggage are new electronic devices, the detection unit can perform detection by referring to the related literature. For example, if the contents of the baggage are special chemical substances, the detection unit can perform detection by referring to the related literature. Furthermore, if the contents of the baggage are unknown items, the detection unit can also perform detection by referring to the related literature. For example, if the contents of the baggage are new electronic devices, the detection unit can perform detection by referring to the related literature. By referring to the related literature, the accuracy of detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input literature data related to the contents of the baggage into the generation AI, which can analyze the data to improve the accuracy of detection.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the baggage inspection results when notifying the user. For example, if the baggage contains dangerous goods, the notification unit can provide a detailed notification and suggest specific countermeasures. For example, if the baggage contains prohibited items, the notification unit can provide a brief notification informing the user of the need for reinspection. The notification unit can also provide a simple notification informing the user that the baggage has passed inspection if the baggage complies with regulations. For example, if the baggage contains dangerous goods, the notification unit can provide a detailed notification and suggest specific countermeasures. This allows the user to deepen their understanding by providing an optimal notification based on the importance of the baggage inspection results. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input baggage inspection result data into a generation AI, which can analyze the data and adjust the level of detail of the notification.

[0048] When notifying, the notification unit can apply different notification algorithms depending on the category of the baggage inspection results. For example, if the baggage contains dangerous goods, the notification unit can apply an emergency notification algorithm to quickly notify the user. For example, if the baggage contains prohibited items, the notification unit can apply a normal notification algorithm to notify the user. Furthermore, if the baggage complies with regulations, the notification unit can also apply a simplified notification algorithm to notify the user. For example, if the baggage contains dangerous goods, the notification unit can apply an emergency notification algorithm to quickly notify the user. This allows the user to understand the information more effectively by providing an optimal notification according to the category of the baggage inspection results. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input category data of the baggage inspection results into a generation AI, which can analyze the data and apply a different notification algorithm.

[0049] At the time of notification, the notification unit can adjust the notification settings based on the time when the baggage inspection results were submitted. For example, if the baggage was submitted early in the morning, the notification unit can set stricter notification settings than usual. For example, if the baggage was submitted late at night, the notification unit can set lenient notification settings than usual. The notification unit can also set standard notification settings if the baggage was submitted during peak hours. For example, if the baggage was submitted early in the morning, the notification unit can set stricter notification settings than usual. This improves the accuracy of notifications by applying optimal notification settings according to the time when the baggage was submitted. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input baggage submission time data into the generation AI, which can analyze the data and adjust the notification settings.

[0050] The notification unit can improve the accuracy of the notification by referring to literature related to the baggage inspection results when notifying the user. For example, if the baggage inspection results relate to a new electronic device, the notification unit can provide the notification by referring to the literature. For example, if the baggage inspection results relate to a special chemical substance, the notification unit can provide the notification by referring to the literature. The notification unit can also provide the notification by referring to the literature when the baggage inspection results relate to an unknown item. For example, if the baggage inspection results relate to a new electronic device, the notification unit can provide the notification by referring to the literature. By referring to the literature, the accuracy of the notification is improved. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input literature data related to the baggage inspection results into a generation AI, which can analyze the data to improve the accuracy of the notification.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The scanning unit can monitor the health condition of the baggage owner and adjust the scanning intensity and method according to their health condition. For example, if the owner has heart disease, the scanning intensity can be lowered to ensure safety. If the owner is pregnant, certain scanning methods can be avoided. Also, if the owner has allergies, the use of chemical sensors can be avoided. This improves safety and accuracy by applying the optimal scanning method according to the owner's health condition.

[0053] The analysis unit can refer to the occupational information of the baggage owner and adjust the level of analysis detail depending on the occupation. For example, if the owner is a scientist, a detailed analysis of chemical substances can be performed. If the owner is an engineer, a detailed analysis of electronic devices can be performed. Also, if the owner is a medical professional, a detailed analysis of medical equipment can be performed. This improves the accuracy of the analysis by applying the optimal analysis method depending on the owner's occupation.

[0054] The detection unit can estimate the travel purpose of the baggage owner and adjust the detection criteria according to the purpose of the trip. For example, if the owner is traveling for business, the detection criteria for electronic devices can be stricter. If the owner is traveling for sightseeing, the detection criteria for souvenirs can be looser. Also, if the owner is traveling with family, the detection criteria for children's items can be looser. This improves detection accuracy by applying the optimal detection criteria according to the owner's travel purpose.

[0055] The notification unit can automatically change the notification language based on the language setting of the baggage owner. For example, if the owner speaks English, the notification can be made in English. If the owner speaks Japanese, the notification can be made in Japanese. Also, if the owner speaks multiple languages, the notification can be made in multiple languages ​​as options. This allows the owner to understand the notification better by applying the optimal notification method according to their language setting.

[0056] The scanning unit can refer to the baggage owner's past scan history and adjust the scan settings based on that history. For example, if the owner has a history of bringing in dangerous items in the past, the intensity of the scan can be increased. If the owner has passed the scan without any problems in the past, the normal scan settings can be applied. Also, if the owner is being scanned for the first time, the standard scan settings can be applied. This improves the accuracy of the scan by applying the optimal scan settings based on the owner's past scan history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The scanning unit scans the contents of the baggage. The scanning unit scans the contents of the baggage, for example, using data obtained from X-ray images or other sensors. The scanning unit may use an X-ray device to perform a detailed scan of the interior of the baggage, and may also use a metal detector or chemical sensor to scan the contents of the baggage. Step 2: The analysis unit analyzes the data obtained by the scanning unit. The analysis unit uses the generation AI to analyze the scan data and check the contents of the baggage in detail. The analysis unit uses the generation AI to analyze the contents of the baggage based on the scan data, allowing it to detect dangerous or prohibited items. Step 3: The detection unit detects dangerous or prohibited items based on the data analyzed by the analysis unit. The detection unit uses the generation AI to detect dangerous or prohibited items based on the analyzed data and can issue a warning. Step 4: The notification unit notifies passengers of the test results obtained by the detection unit. The notification unit can notify passengers of the test results in real time using their smartphones or airport displays.

[0059] (Example 2) A baggage inspection system according to an embodiment of the present invention fully automates airport baggage inspection using generative AI. When baggage arrives at the inspection area, the generative AI scans the contents of the bag to check for any dangerous or prohibited items. The generative AI analyzes data from X-ray images and other sensors to thoroughly examine the contents of the bag. For example, if the bag contains dangerous items such as knives or explosives, the generative AI immediately issues an alert and moves the baggage to a separate inspection area. Next, the generative AI analyzes the contents of the baggage to ensure that the passenger's belongings comply with airline regulations. If any regulations are violated, such as the amount of liquid or the type of electronic device, the generative AI issues an alert and requests that the baggage be reinspected. Furthermore, the generative AI notifies passengers of the baggage inspection results in real time. Passengers can check on their smartphones or airport displays whether their baggage has passed inspection. This allows passengers to monitor the progress of baggage inspection and shortens waiting times. This system will fully automate baggage inspection at airports, freeing up manpower and shortening passenger waiting times. Generative AI will also perform detailed analysis of baggage contents, improving the accuracy of detecting dangerous and prohibited items and enhancing airport safety. This allows the baggage inspection system to free up manpower and shorten passenger waiting times.

[0060] A baggage inspection system according to an embodiment includes a scanning unit, an analyzing unit, a detecting unit, and a notifying unit. The scanning unit scans the contents of baggage. The scanning unit scans the contents of baggage using, for example, X-ray images or data obtained from other sensors. The scanning unit can scan the interior of the baggage in detail using, for example, an X-ray device. The scanning unit can also scan the contents of the baggage using a metal detector or a chemical sensor. For example, the scanning unit can scan the interior of the baggage in detail using an X-ray device and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. The analyzing unit analyzes the data obtained by the scanning unit. For example, the analyzing unit can analyze the scan data using a generating AI to check the contents of the baggage in detail. For example, the generating AI can analyze the contents of the baggage based on the scan data to detect dangerous items and prohibited items. The analyzing unit can also analyze the scan data using a generating AI to check the contents of the baggage in detail. For example, the analysis unit can have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous objects or prohibited items. The detection unit can detect dangerous objects or prohibited items based on the data analyzed by the analysis unit. For example, the detection unit can use the generation AI to detect dangerous objects or prohibited items based on the analysis data and issue a warning. The detection unit can also use the generation AI to detect dangerous objects or prohibited items based on the analysis data. For example, the detection unit can use the generation AI to detect dangerous objects or prohibited items based on the analysis data and issue a warning. The notification unit notifies the inspection results obtained by the detection unit. For example, the notification unit can notify passengers of the inspection results in real time. The notification unit can notify passengers of the inspection results using, for example, a smartphone or an airport display. The notification unit can also notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using, for example, a smartphone or an airport display. As a result, the baggage inspection system according to the embodiment can scan, analyze, detect, and notify the contents of baggage.

[0061] The scanning unit can scan the contents of baggage using data obtained from X-ray images or other sensors. The scanning unit can, for example, use an X-ray device to perform a detailed scan of the interior of the baggage. The scanning unit can, for example, use an X-ray device to perform a detailed scan of the interior of the baggage and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. For example, the scanning unit can use an X-ray device to perform a detailed scan of the interior of the baggage and detect metal objects using a metal detector. The scanning unit can also detect chemical substances in the baggage using a chemical sensor. This improves the accuracy of detecting dangerous or prohibited items by scanning the contents of the baggage in detail. Some or all of the above-described processing in the scanning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the scanning unit can input image data acquired by an X-ray device into a generation AI, which can analyze the image data to scan the contents of the baggage.

[0062] The analysis unit analyzes the data obtained from the scanning unit and can check the contents of the baggage in detail. The analysis unit analyzes the scan data using, for example, a generation AI and checks the contents of the baggage in detail. The analysis unit can, for example, have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous or prohibited items. The analysis unit can also analyze the scan data using the generation AI and check the contents of the baggage in detail. For example, the analysis unit can have the generation AI analyze the contents of the baggage based on the scan data and detect dangerous or prohibited items. This improves the accuracy of detecting dangerous or prohibited items by checking the contents of the baggage in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the data obtained by the scanning unit into the generation AI, and the generation AI analyzes the data and checks the contents of the baggage in detail.

[0063] The detection unit can detect dangerous materials and prohibited items based on the data obtained from the analysis unit. The detection unit can, for example, use a generation AI to detect dangerous materials and prohibited items based on the analysis data. The detection unit can, for example, use a generation AI to detect dangerous materials and prohibited items based on the analysis data and issue a warning. The detection unit can also detect dangerous materials and prohibited items based on the analysis data using a generation AI. For example, the detection unit can use a generation AI to detect dangerous materials and prohibited items based on the analysis data and issue a warning. This improves the accuracy of detecting dangerous materials and prohibited items. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input data obtained by the analysis unit into a generation AI, and the generation AI can analyze the data to detect dangerous materials and prohibited items.

[0064] The notification unit can notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using a smartphone or an airport display. The notification unit can also notify passengers of the inspection results in real time. For example, the notification unit can notify passengers of the inspection results using a smartphone or an airport display. This allows passengers to understand the progress of the baggage inspection and shorten their waiting time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can input the inspection results obtained by the detection unit into a generation AI, which analyzes the data and notifies the passengers of the inspection results.

[0065] The notification unit can notify the passenger of the inspection results using a smartphone or an airport display. The notification unit can notify the passenger of the inspection results using, for example, a smartphone or an airport display. The notification unit can also notify the passenger of the inspection results in real time. For example, the notification unit can notify the passenger of the inspection results using a smartphone or an airport display. This makes it easier for the passenger to understand the progress of the baggage inspection. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the notification unit can input the inspection results acquired by the detection unit into the generation AI, which can analyze the data and notify the passenger of the inspection results.

[0066] The scanning unit can estimate the user's emotions and adjust the timing of scanning based on the estimated user emotions. For example, if the user is nervous, the scanning unit can delay the timing of scanning to give the user time to relax. For example, if the user is in a hurry, the scanning unit can speed up the timing of scanning to perform processing quickly. The scanning unit can also perform scanning at a normal timing if the user is relaxed. For example, if the user is nervous, the scanning unit can delay the timing of scanning to give the user time to relax. This adjusts the timing of scanning according to the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the scanning unit can be performed using, for example, the generation AI. For example, the scanning unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and adjust the timing of scanning.

[0067] The scanning unit can automatically change the scanning method depending on the shape and material of the baggage. For example, if the baggage is made of metal, the scanning unit adjusts the intensity of the X-ray scan to obtain a detailed image. For example, if the baggage is made of cloth, the scanning unit can lower the intensity of the X-ray scan to increase transparency. Furthermore, if the baggage is made of multiple materials, the scanning unit can combine the scanning methods optimal for each material. For example, if the baggage is made of metal, the scanning unit adjusts the intensity of the X-ray scan to obtain a detailed image. This improves the accuracy of the scan by applying the optimal scanning method depending on the shape and material of the baggage. Some or all of the above-mentioned processing in the scanning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scanning unit can input data on the shape and material of the baggage into a generation AI, which can analyze the data and determine the optimal scanning method.

[0068] The scanning unit can improve the accuracy of the scan by referring to the baggage owner's past travel history during scanning. For example, the scanning unit can increase the accuracy of the scan if the baggage owner has a history of bringing in dangerous goods in the past. For example, the scanning unit can apply normal scanning accuracy if the baggage owner is a frequent traveler. The scanning unit can also set the scanning accuracy to standard if the baggage owner is traveling for the first time. For example, the scanning unit can increase the scanning accuracy if the baggage owner has a history of bringing in dangerous goods in the past. This improves the accuracy of the scan by referring to the past travel history. Some or all of the above-described processing in the scanning unit can be performed using, or without, a generation AI. For example, the scanning unit can input the baggage owner's past travel history data into the generation AI, which can analyze the data to improve the accuracy of the scan.

[0069] The scanning unit can estimate the user's emotions and determine the scanning priority based on the estimated user's emotions. For example, if the user is nervous, the scanning unit can lower the scanning priority to allow the user time to relax. For example, if the user is in a hurry, the scanning unit can increase the scanning priority to perform the scan quickly. Furthermore, if the user is relaxed, the scanning unit can perform the scan at a normal priority. For example, if the user is nervous, the scanning unit can lower the scanning priority to allow the user time to relax. This adjusts the scanning priority according to the user's emotions, thereby reducing the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scanning unit can be performed using, for example, the generation AI. For example, the scanning unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and determine the scanning priority.

[0070] During scanning, the scanning unit can adjust scan settings based on the weight and size of the baggage. For example, if the baggage is heavy, the scanning unit can increase the intensity of the X-ray scan to obtain a detailed image. For example, if the baggage is light, the scanning unit can decrease the intensity of the X-ray scan to increase penetration. Furthermore, if the baggage is large, the scanning unit can widen the scan range to cover the entire bag. For example, if the baggage is heavy, the scanning unit can increase the intensity of the X-ray scan to obtain a detailed image. This improves the accuracy of the scan by applying optimal scan settings according to the weight and size of the baggage. Some or all of the above-described processing in the scanning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scanning unit can input data on the weight and size of the baggage into a generation AI, which can analyze the data and adjust the scan settings.

[0071] The scanning unit can analyze the social media activity of the baggage owner during scanning and incorporate related information into the scan. For example, if the baggage owner has recently posted about dangerous goods, the scanning unit can increase the accuracy of the scan. For example, if the baggage owner frequently posts about travel, the scanning unit can apply normal scan accuracy. The scanning unit can also set the scan accuracy to standard if the baggage owner does not use social media. For example, the scanning unit can increase the scan accuracy if the baggage owner has recently posted about dangerous goods. In this way, the scanning accuracy is improved by analyzing social media activity. Some or all of the above-described processing in the scanning unit can be performed using, or without, a generation AI. For example, the scanning unit can input the baggage owner's social media activity data into a generation AI, which can analyze the data to improve the scan accuracy.

[0072] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is nervous, the analysis unit can increase the level of analysis detail to provide a sense of security. For example, if the user is relaxed, the analysis unit can perform the analysis at a normal level of detail. Furthermore, if the user is in a hurry, the analysis unit can reduce the level of analysis detail to perform the process quickly. For example, if the user is nervous, the analysis unit can increase the level of analysis detail to provide a sense of security. This adjusts the level of analysis detail according to the user's emotions, thereby enhancing the user's sense of security. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and adjust the level of analysis detail.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the contents of the baggage. For example, if the contents of the baggage are electronic devices, the analysis unit can apply an electromagnetic wave analysis algorithm. For example, if the contents of the baggage are liquid, the analysis unit can apply a chemical component analysis algorithm. Furthermore, if the contents of the baggage are metal products, the analysis unit can also apply a metal detection algorithm. For example, if the contents of the baggage are electronic devices, the analysis unit applies an electromagnetic wave analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the contents of the baggage. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of the contents of the baggage into a generation AI, which analyzes the data and applies the optimal analysis algorithm.

[0074] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the contents of the baggage. For example, if the contents of the baggage consist of multiple electronic devices, the analysis unit can perform the analysis while taking into account mutual interference. For example, if the contents of the baggage consist of liquid and metal products, the analysis unit can perform the analysis while taking into account the possibility of chemical reactions. Furthermore, if the contents of the baggage consist of items of different categories, the analysis unit can also perform the analysis while taking into account the characteristics of each category. For example, if the contents of the baggage consist of multiple electronic devices, the analysis unit can perform the analysis while taking into account mutual interference. This improves the accuracy of the analysis by taking into account the interrelationships between the contents of the baggage. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input interrelationship data between the contents of the baggage into a generation AI, which can analyze the data and improve the accuracy of the analysis.

[0075] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit can increase the analysis priority and provide results quickly. For example, if the user is relaxed, the analysis unit can perform analysis with normal priority. Furthermore, if the user is in a hurry, the analysis unit can increase the analysis priority and provide results quickly. For example, if the user is nervous, the analysis unit can increase the analysis priority and provide results quickly. This allows the user's stress to be reduced by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the analysis settings based on the time the contents of the baggage were submitted. For example, if the baggage was submitted early in the morning, the analysis unit can set stricter analysis settings than usual. For example, if the baggage was submitted late at night, the analysis unit can set lenient analysis settings than usual. The analysis unit can also set standard analysis settings if the baggage was submitted during peak hours. For example, if the baggage was submitted early in the morning, the analysis unit can set stricter analysis settings than usual. This improves the accuracy of the analysis by applying optimal analysis settings according to the time the baggage was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the time the baggage was submitted into the generation AI, which can analyze the data and adjust the analysis settings.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the contents of the baggage. For example, if the contents of the baggage are new electronic devices, the analysis unit can perform the analysis by referring to the related literature. For example, if the contents of the baggage are special chemical substances, the analysis unit can perform the analysis by referring to the related literature. Furthermore, if the contents of the baggage are unknown items, the analysis unit can also perform the analysis by referring to the related literature. For example, if the contents of the baggage are new electronic devices, the analysis unit can perform the analysis by referring to the related literature. In this way, by referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input literature data related to the contents of the baggage into the generation AI, which then analyzes the data to improve the accuracy of the analysis.

[0078] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated user emotions. For example, if the user is nervous, the detection unit can tighten the detection criteria to provide a sense of security. For example, if the user is relaxed, the detection unit can perform detection using normal criteria. Furthermore, if the user is in a hurry, the detection unit can loosen the detection criteria to perform processing quickly. For example, if the user is nervous, the detection unit can tighten the detection criteria to provide a sense of security. This adjusts the detection criteria according to the user's emotions, thereby enhancing the user's sense of security. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI. For example, the detection unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and adjust the detection criteria.

[0079] The detection unit can apply different detection algorithms depending on the risk level of the contents of the baggage during detection. For example, if the contents of the baggage are high-risk items, the detection unit can apply a strict detection algorithm. For example, if the contents of the baggage are medium-risk items, the detection unit can apply a normal detection algorithm. The detection unit can also apply a simple detection algorithm if the contents of the baggage are low-risk items. For example, the detection unit applies a strict detection algorithm if the contents of the baggage are high-risk items. This improves detection accuracy by applying an optimal detection algorithm depending on the risk level of the contents of the baggage. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input risk level data of the contents of the baggage into a generation AI, which analyzes the data and applies an optimal detection algorithm.

[0080] The detection unit can improve detection accuracy by taking into account the interrelationships between the contents of the baggage during detection. For example, if the contents of the baggage consist of multiple electronic devices, the detection unit can perform detection while taking into account mutual interference. For example, if the contents of the baggage consist of liquid and metal products, the detection unit can perform detection while taking into account the possibility of chemical reactions. Furthermore, if the contents of the baggage consist of items of different categories, the detection unit can also perform detection while taking into account the characteristics of each category. For example, if the contents of the baggage consist of multiple electronic devices, the detection unit can perform detection while taking into account mutual interference. This improves detection accuracy by taking into account the interrelationships between the contents of the baggage. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input interrelationship data between the contents of the baggage into a generation AI, which can analyze the data and improve detection accuracy.

[0081] The detection unit can estimate the user's emotion and determine a detection priority based on the estimated user's emotion. For example, if the user is nervous, the detection unit can increase the detection priority and provide results quickly. For example, if the user is relaxed, the detection unit can perform detection with normal priority. Furthermore, if the user is in a hurry, the detection unit can increase the detection priority and provide results quickly. For example, if the user is nervous, the detection unit can increase the detection priority and provide results quickly. This allows the user's stress to be reduced by adjusting the detection priority according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI. For example, the detection unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotion and determine the detection priority.

[0082] The detection unit can perform detection while taking into account the geographical distribution of the contents of the baggage. For example, if the contents of the baggage are common items in a specific region, the detection unit can perform detection while taking into account the regulations of that region. For example, if the contents of the baggage are internationally regulated items, the detection unit can perform detection while taking into account international regulations. Furthermore, if the contents of the baggage are items unique to a region, the detection unit can also perform detection while taking into account the characteristics of that region. For example, if the contents of the baggage are common items in a specific region, the detection unit can perform detection while taking into account the regulations of that region. This improves the accuracy of detection by taking into account the geographical distribution of the contents of the baggage. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the detection unit can input geographical distribution data of the contents of the baggage into a generation AI, which can analyze the data and perform detection.

[0083] The detection unit can improve the accuracy of detection by referring to literature related to the contents of the baggage during detection. For example, if the contents of the baggage are new electronic devices, the detection unit can perform detection by referring to the related literature. For example, if the contents of the baggage are special chemical substances, the detection unit can perform detection by referring to the related literature. Furthermore, if the contents of the baggage are unknown items, the detection unit can also perform detection by referring to the related literature. For example, if the contents of the baggage are new electronic devices, the detection unit can perform detection by referring to the related literature. By referring to the related literature, the accuracy of detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can input literature data related to the contents of the baggage into the generation AI, which can analyze the data to improve the accuracy of detection.

[0084] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide the notification in a calm expression. For example, if the user is relaxed, the notification unit can provide the notification in a normal expression. Furthermore, if the user is in a hurry, the notification unit can provide the notification in a concise and quick expression. For example, if the user is nervous, the notification unit can provide the notification in a calm expression. This allows the user's stress to be reduced by adjusting the notification expression method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI. For example, the notification unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and adjust the notification expression method.

[0085] The notification unit can adjust the level of detail of the notification based on the importance of the baggage inspection results when notifying the user. For example, if the baggage contains dangerous goods, the notification unit can provide a detailed notification and suggest specific countermeasures. For example, if the baggage contains prohibited items, the notification unit can provide a brief notification informing the user of the need for reinspection. The notification unit can also provide a simple notification informing the user that the baggage has passed inspection if the baggage complies with regulations. For example, if the baggage contains dangerous goods, the notification unit can provide a detailed notification and suggest specific countermeasures. This allows the user to deepen their understanding by providing an optimal notification based on the importance of the baggage inspection results. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input baggage inspection result data into a generation AI, which can analyze the data and adjust the level of detail of the notification.

[0086] When notifying, the notification unit can apply different notification algorithms depending on the category of the baggage inspection results. For example, if the baggage contains dangerous goods, the notification unit can apply an emergency notification algorithm to quickly notify the user. For example, if the baggage contains prohibited items, the notification unit can apply a normal notification algorithm to notify the user. Furthermore, if the baggage complies with regulations, the notification unit can also apply a simplified notification algorithm to notify the user. For example, if the baggage contains dangerous goods, the notification unit can apply an emergency notification algorithm to quickly notify the user. This allows the user to understand the information more effectively by providing an optimal notification according to the category of the baggage inspection results. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input category data of the baggage inspection results into a generation AI, which can analyze the data and apply a different notification algorithm.

[0087] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is nervous, the notification unit can increase the priority of notifications and provide results quickly. For example, if the user is relaxed, the notification unit can provide notifications with normal priority. Furthermore, if the user is in a hurry, the notification unit can increase the priority of notifications and provide results quickly. For example, if the user is nervous, the notification unit can increase the priority of notifications and provide results quickly. This allows the user's stress to be reduced by adjusting the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, the generation AI. For example, the notification unit can input the user's facial expression data into the generation AI, which can analyze the data to estimate the user's emotions and determine the priority of notifications.

[0088] At the time of notification, the notification unit can adjust the notification settings based on the time when the baggage inspection results were submitted. For example, if the baggage was submitted early in the morning, the notification unit can set stricter notification settings than usual. For example, if the baggage was submitted late at night, the notification unit can set lenient notification settings than usual. The notification unit can also set standard notification settings if the baggage was submitted during peak hours. For example, if the baggage was submitted early in the morning, the notification unit can set stricter notification settings than usual. This improves the accuracy of notifications by applying optimal notification settings according to the time when the baggage was submitted. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input baggage submission time data into the generation AI, which can analyze the data and adjust the notification settings.

[0089] The notification unit can improve the accuracy of the notification by referring to literature related to the baggage inspection results when notifying the user. For example, if the baggage inspection results relate to a new electronic device, the notification unit can provide the notification by referring to the literature. For example, if the baggage inspection results relate to a special chemical substance, the notification unit can provide the notification by referring to the literature. The notification unit can also provide the notification by referring to the literature when the baggage inspection results relate to an unknown item. For example, if the baggage inspection results relate to a new electronic device, the notification unit can provide the notification by referring to the literature. By referring to the literature, the accuracy of the notification is improved. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input literature data related to the baggage inspection results into a generation AI, which can analyze the data to improve the accuracy of the notification. === Hard Collateral 1-1 === Each of the multiple elements, including the scanning unit, analyzing unit, detecting unit, and notifying unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the scanning unit scans the contents of baggage using the camera 42 or an X-ray device of the smart device 14. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the scan data using a generating AI. The detecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects dangerous or prohibited items based on the analyzed data. The notifying unit notifies the inspection results using, for example, the output device 40 of the smart device 14 or a display at the airport. === Hard Collateral 1-2 === Each of the multiple elements, including the scanning unit, analyzing unit, detecting unit, and notifying unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the scanning unit scans the contents of baggage using the camera 42 of the smart glasses 214 or an X-ray device. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the scan data using a generating AI. The detecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects dangerous or prohibited items based on the analyzed data. The notifying unit notifies the inspection results using, for example, the speaker 240 of the smart glasses 214 or a display at the airport. === Hard Collateral 1-3 === Each of the multiple elements including the scanning unit, analyzing unit, detecting unit, and notifying unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the scanning unit scans the contents of baggage using the camera 42 or X-ray device of the headset-type terminal 314. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scan data using a generating AI. The detecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects dangerous objects and prohibited items based on the analyzed data. The notifying unit notifies the inspection results using, for example, the display 343 of the headset-type terminal 314 or a display at the airport. === Hard Collateral 1-4 === Each of the multiple elements including the scanning unit, analyzing unit, detecting unit, and notifying unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the scanning unit scans the contents of baggage using the camera 42 or X-ray device of the robot 414. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scan data using a generative AI. The detecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects dangerous objects and prohibited items based on the analyzed data. The notifying unit notifies the inspection results using, for example, the speaker 240 of the robot 414 or a display at the airport.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The scanning unit can monitor the health condition of the baggage owner and adjust the scanning intensity and method according to their health condition. For example, if the owner has heart disease, the scanning intensity can be lowered to ensure safety. If the owner is pregnant, certain scanning methods can be avoided. Also, if the owner has allergies, the use of chemical sensors can be avoided. This improves safety and accuracy by applying the optimal scanning method according to the owner's health condition.

[0092] The analysis unit can refer to the occupational information of the baggage owner and adjust the level of analysis detail depending on the occupation. For example, if the owner is a scientist, a detailed analysis of chemical substances can be performed. If the owner is an engineer, a detailed analysis of electronic devices can be performed. Also, if the owner is a medical professional, a detailed analysis of medical equipment can be performed. This improves the accuracy of the analysis by applying the optimal analysis method depending on the owner's occupation.

[0093] The detection unit can estimate the travel purpose of the baggage owner and adjust the detection criteria according to the purpose of the trip. For example, if the owner is traveling for business, the detection criteria for electronic devices can be stricter. If the owner is traveling for sightseeing, the detection criteria for souvenirs can be looser. Also, if the owner is traveling with family, the detection criteria for children's items can be looser. This improves detection accuracy by applying the optimal detection criteria according to the owner's travel purpose.

[0094] The notification unit can automatically change the notification language based on the language setting of the baggage owner. For example, if the owner speaks English, the notification can be made in English. If the owner speaks Japanese, the notification can be made in Japanese. Also, if the owner speaks multiple languages, the notification can be made in multiple languages ​​as options. This allows the owner to understand the notification better by applying the optimal notification method according to their language setting.

[0095] The scanning unit can refer to the baggage owner's past scan history and adjust the scan settings based on that history. For example, if the owner has a history of bringing in dangerous items in the past, the intensity of the scan can be increased. If the owner has passed the scan without any problems in the past, the normal scan settings can be applied. Also, if the owner is being scanned for the first time, the standard scan settings can be applied. This improves the accuracy of the scan by applying the optimal scan settings based on the owner's past scan history.

[0096] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis priority can be increased to provide results quickly. If the user is relaxed, the analysis can be performed with normal priority. Also, if the user is in a hurry, the analysis priority can be increased to provide results quickly. In this way, the user's stress can be reduced by adjusting the analysis priority according to the user's emotions.

[0097] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated user emotions. For example, if the user is nervous, the detection criteria can be tightened to provide a sense of security. If the user is relaxed, detection can be performed using normal criteria. Also, if the user is in a hurry, the detection criteria can be loosened to allow for quick processing. In this way, adjusting the detection criteria according to the user's emotions increases the user's sense of security.

[0098] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification can be made in a calm expression method. If the user is relaxed, the notification can be made in a normal expression method. Also, if the user is in a hurry, the notification can be made in a concise and quick expression method. In this way, by adjusting the notification expression method according to the user's emotions, the user's stress can be reduced.

[0099] The scanning unit can estimate the user's emotions and determine the priority of scanning based on the estimated user's emotions. For example, if the user is nervous, the scanning priority can be lowered to give the user time to relax. If the user is in a hurry, the scanning priority can be increased to speed up the process. Also, if the user is relaxed, scanning can be performed at normal priority. In this way, adjusting the scanning priority according to the user's emotions can reduce the user's stress.

[0100] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, if the user is nervous, the level of detail of the analysis can be increased to provide a sense of security. If the user is relaxed, the analysis can be performed at a normal level of detail. Also, if the user is in a hurry, the level of detail of the analysis can be reduced to speed up the process. In this way, adjusting the level of detail of the analysis according to the user's emotions increases the user's sense of security.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The scanning unit scans the contents of the baggage. The scanning unit scans the contents of the baggage, for example, using data obtained from X-ray images or other sensors. The scanning unit may use an X-ray device to perform a detailed scan of the interior of the baggage, and may also use a metal detector or chemical sensor to scan the contents of the baggage. Step 2: The analysis unit analyzes the data obtained by the scanning unit. The analysis unit uses the generation AI to analyze the scan data and check the contents of the baggage in detail. The analysis unit uses the generation AI to analyze the contents of the baggage based on the scan data, allowing it to detect dangerous or prohibited items. Step 3: The detection unit detects dangerous or prohibited items based on the data analyzed by the analysis unit. The detection unit uses the generation AI to detect dangerous or prohibited items based on the analyzed data and can issue a warning. Step 4: The notification unit notifies passengers of the test results obtained by the detection unit. The notification unit can notify passengers of the test results in real time using their smartphones or airport displays.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 scanning unit for scanning the contents of baggage; an analysis unit that analyzes data obtained by the scanning unit; a detection unit that detects dangerous objects and prohibited items based on the data analyzed by the analysis unit; a notification unit that notifies the test result obtained by the detection unit. A system characterized by:

2. The scanning unit Scanning the contents of baggage using data from X-ray images and other sensors The system of claim 1 .

3. The analysis unit The data obtained from the scanner is analyzed to check the contents of the baggage in detail. The system of claim 1 .

4. The detection unit Detecting dangerous and prohibited items based on the data obtained from the analysis unit The system of claim 1 .

5. The notification unit Notifying passengers of test results in real time The system of claim 1 .

6. The notification unit Test results will be communicated via smartphones and airport displays The system of claim 1 .

7. The scanning unit Estimate the user's emotions and adjust the timing of scans based on the estimated user emotions The system of claim 1 .

8. The scanning unit Automatically change the scanning method depending on the shape and material of the baggage The system of claim 1 .

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