system

Abstract

The system according to this embodiment aims to fully automate baggage inspection, thereby improving airport human resources and enhancing services for passengers. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a storage unit, and an adjustment unit. The acquisition unit acquires image data of baggage. The analysis unit analyzes the image data acquired by the acquisition unit and detects dangerous goods or prohibited items. The storage unit stores the inspection results obtained by the analysis unit. The adjustment unit performs fine tuning based on the data from the storage unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that baggage inspection at airports is not fully automated, resulting in the need to secure human resources and causing waiting times for passengers.

[0005] The system according to the embodiment aims to fully automate baggage inspection and improve the human resources at airports and the services provided to passengers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a storage unit, and an adjustment unit. The acquisition unit acquires image data of baggage. The analysis unit analyzes the image data acquired by the acquisition unit to detect dangerous goods or prohibited items. The storage unit stores the inspection results obtained by the analysis unit. The adjustment unit performs fine tuning based on the data from the storage unit. [Effects of the Invention]

[0007] The system according to this embodiment can fully automate baggage inspection, thereby improving airport human resources and enhancing services for passengers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The baggage inspection system according to an embodiment of the present invention is a system that fully automates airport baggage inspection using generative AI. The baggage inspection system acquires image data of baggage, and the generative AI analyzes the image data to detect dangerous goods and prohibited items. This reduces the human resources required at the airport and improves the efficiency of inspections. Furthermore, the baggage inspection system accumulates inspection results and fine-tunes them by comparing the judgment results of inspectors and the generative AI to improve accuracy. Ultimately, it achieves full automation of baggage inspection using the generative AI's video analysis platform. This system is expected to improve the human resources required at airports, enhance safety, and increase passenger satisfaction. For example, the baggage inspection system acquires image data of baggage. For example, the baggage inspection system acquires image data of baggage, and the generative AI analyzes the image data to detect dangerous goods and prohibited items. The generative AI uses pattern recognition and anomaly detection algorithms to quickly and accurately identify dangerous goods and prohibited items. This reduces the human resources required and improves the efficiency of inspections. Furthermore, the baggage screening system will accumulate inspection results and fine-tune its accuracy by comparing the results of inspectors with those of the generating AI. Ultimately, it will achieve full automation of baggage screening using the generating AI's video analysis platform. This system is expected to improve airport human resources, enhance safety, and increase passenger satisfaction. For example, the baggage screening system will reduce passenger stress by shortening waiting times and decreasing the need for re-inspections. In addition, the advanced image analysis of the generating AI will provide a higher level of security than conventional baggage screening. As a result, the baggage screening system can improve airport human resources, enhance safety, and increase passenger satisfaction.

[0029] The baggage inspection system according to this embodiment comprises an acquisition unit, an analysis unit, a storage unit, and an adjustment unit. The acquisition unit acquires image data of baggage. The acquisition unit can, for example, capture image data of baggage with a camera and store it as digital data. The acquisition unit can also, for example, acquire image data of baggage with an X-ray device and store it as digital data. The acquisition unit can also, for example, acquire image data of baggage with an infrared device and store it as digital data. The analysis unit analyzes the image data acquired by the acquisition unit and detects dangerous goods or prohibited items. The analysis unit can, for example, analyze the image data using a generation AI and detect dangerous goods. The analysis unit can also, for example, analyze the image data using a generation AI and detect prohibited items. The analysis unit can also, for example, analyze the image data using a generation AI and detect dangerous goods and prohibited items simultaneously. The storage unit stores the inspection results obtained by the analysis unit. The storage unit can, for example, store the inspection results in a database. The storage unit can also, for example, store the inspection results in cloud storage. The storage unit can, for example, save inspection results to local storage. The adjustment unit performs fine tuning based on the data from the storage unit. The adjustment unit can, for example, adjust the parameters of the generated AI using the data from the storage unit. The adjustment unit can also, for example, retrain the model of the generated AI using the data from the storage unit. The adjustment unit can also, for example, improve the algorithm of the generated AI using the data from the storage unit. As a result, the baggage inspection system according to the embodiment can fully automate baggage inspection, reduce human resources, and improve inspection efficiency.

[0030] The acquisition unit acquires image data of baggage. For example, the acquisition unit captures image data of baggage with a camera and saves it as digital data. Specifically, it uses a high-resolution camera to capture detailed images of the baggage's exterior and acquires image data in real time. This allows for accurate capture of the baggage's shape and external characteristics. The acquisition unit can also acquire image data of baggage using an X-ray device and save it as digital data. The X-ray device can transparently photograph the internal structure of the baggage and identify different materials such as metals and organic materials. This provides basic data for detecting dangerous goods and prohibited items hidden inside the baggage. Furthermore, the acquisition unit can also acquire image data of baggage using an infrared device and save it as digital data. The infrared device can detect the surface temperature and thermal radiation characteristics of the baggage and identify abnormal temperature changes and heat sources. This provides additional information for detecting heat-generating objects and electronic devices hidden inside the baggage. The acquisition unit integrates the diverse image data obtained from these different devices to provide basic data for detailed baggage inspection. This allows the acquisition unit to collect multifaceted information such as the appearance, internal structure, and thermal characteristics of the baggage, supporting highly accurate inspections by the analysis unit.

[0031] The analysis unit analyzes image data acquired by the acquisition unit to detect hazardous materials or prohibited items. The analysis uses a generative AI to analyze the image data and detect hazardous materials. Specifically, the generative AI is pre-trained using a large amount of baggage image data, enabling it to identify the characteristics of hazardous materials and prohibited items with high accuracy. For example, the generative AI can analyze X-ray images of baggage to identify the shapes of metallic weapons and explosives. It can also analyze infrared images to detect abnormal heat sources and heat-generating objects. Furthermore, the generative AI can analyze external images of baggage to identify labels and shapes of prohibited items. The analysis unit integrates these analysis results to make a comprehensive determination of the presence or absence of hazardous materials and prohibited items in the baggage. The analysis unit processes image data in real time, enabling it to provide inspection results quickly and accurately. Additionally, the analysis unit can utilize past inspection results and statistical data to improve detection accuracy and identify new hazardous materials. For example, the analysis unit can learn specific patterns and trends based on past inspection data to improve the detection algorithm for new hazardous materials. This allows the analysis unit to perform highly accurate inspections based on the latest information at all times, improving the reliability and security of baggage inspections.

[0032] The storage unit stores the inspection results obtained by the analysis unit. For example, the storage unit saves the inspection results to a database. Specifically, the inspection results include baggage image data, analysis results, and information on detected hazardous materials and prohibited items. This data is systematically stored in the database and used for later analysis and reference. The storage unit can also save the inspection results to cloud storage. Cloud storage allows for efficient storage of large amounts of data and quick access as needed. Furthermore, the storage unit can also save the inspection results to local storage. Local storage enables data storage and access even in environments with unstable network connectivity, improving system reliability. The storage unit can combine these storage methods to ensure data redundancy and availability. Additionally, the storage unit can periodically back up the stored data and take measures to prevent data loss or corruption. This allows the storage unit to safely and efficiently store the inspection results obtained by the analysis unit and support overall system data management.

[0033] The adjustment unit performs fine tuning based on the data from the storage unit. For example, the adjustment unit adjusts the parameters of the generating AI using the data from the storage unit. Specifically, it optimizes the learning rate and weight parameters of the generating AI to improve detection accuracy. The adjustment unit can also retrain the generating AI model using the data from the storage unit. Retraining allows the generating AI to learn new data and patterns, improving its detection capabilities. Furthermore, the adjustment unit can also improve the algorithm of the generating AI using the data from the storage unit. By improving the algorithm, the generating AI can analyze image data more efficiently and accurately, improving its ability to detect dangerous goods and prohibited items. The adjustment unit performs these adjustments regularly to keep the performance of the generating AI in an optimal state at all times. In addition, the adjustment unit can adjust the generating AI in response to user feedback and new regulations. In this way, the adjustment unit can continuously improve the performance of the generating AI by utilizing the data from the storage unit, thereby enhancing the reliability and security of the baggage inspection system.

[0034] The analysis unit can detect hazardous materials or prohibited items using pattern recognition or anomaly detection algorithms. For example, the analysis unit can analyze image data using a neural network to detect hazardous materials. The analysis unit can also analyze image data using a support vector machine to detect prohibited items. Furthermore, the analysis unit can analyze image data using statistical methods to simultaneously detect both hazardous materials and prohibited items. This improves the accuracy of hazardous materials and prohibited items detection by employing pattern recognition and anomaly detection algorithms.

[0035] The storage unit can store inspection results and compare the judgments of the inspector and the generating AI. For example, the storage unit can save inspection results to a database and compare the inspector's judgment with the generating AI's judgment. The storage unit can also save inspection results to cloud storage and compare the inspector's judgment with the generating AI's judgment. The storage unit can also save inspection results to local storage and compare the inspector's judgment with the generating AI's judgment. This improves the accuracy of the system by storing inspection results and comparing the judgments of the inspector and the generating AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the accuracy of the system by using an AI model that stores inspection results in a database and compares the inspector's judgment with the generating AI's judgment.

[0036] The adjustment unit can perform fine-tuning based on the data from the storage unit to improve the accuracy of the analysis. For example, the adjustment unit can adjust the parameters of the generating AI using the data from the storage unit. The adjustment unit can also retrain the model of the generating AI using the data from the storage unit. For example, the adjustment unit can improve the algorithm of the generating AI using the data from the storage unit. As a result, the accuracy of the system is improved by performing fine-tuning based on the data from the storage unit. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the system by using an AI model that adjusts the parameters of the generating AI using the data from the storage unit.

[0037] The display unit can display the test results. The display unit can, for example, display the test results on a display screen. The display unit can also, for example, display the test results on a monitor. The display unit can also, for example, display the test results on a smartphone screen. This allows the user to check the test results by displaying them. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that displays the test results on a display screen to allow the user to check the test results.

[0038] The alert unit can issue alerts based on inspection results. The alert unit can issue, for example, an audio alert. The alert unit can also issue, for example, a visual alert. The alert unit can also issue, for example, a notification alert. This allows for a rapid response when hazardous materials or prohibited items are detected by issuing alerts based on inspection results. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can use an AI model that issues alerts based on inspection results to enable a rapid response when hazardous materials or prohibited items are detected.

[0039] The acquisition unit can select an appropriate image acquisition method based on the shape and material of the luggage. For example, if the luggage is made of a hard material, the acquisition unit can use X-rays to acquire detailed information about the interior. If the luggage is made of a soft material, the acquisition unit can also use infrared light to acquire information about the interior shape. If the luggage is made of a transparent material, the acquisition unit can also use visible light to directly acquire information about the interior. By selecting the optimal image acquisition method based on the shape and material of the luggage, the accuracy of image acquisition is improved. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that selects the optimal image acquisition method based on the shape and material of the luggage.

[0040] The acquisition unit can track the position and movement of luggage in real time and acquire images from the appropriate angle. For example, if luggage is moving on a conveyor belt, the acquisition unit can make the camera follow the luggage and acquire images from the optimal angle. For example, if luggage is stationary, the acquisition unit can also use multiple cameras to acquire images from different angles. For example, if luggage is rotating, the acquisition unit can adjust the camera angle to match the rotation and acquire an overall image. This allows for the acquisition of images from the optimal angle by tracking the position and movement of luggage in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that tracks the position and movement of luggage in real time and acquires images from the optimal angle.

[0041] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring images of baggage, taking into account the baggage owner's information. For example, if the baggage owner is a VIP, the acquisition unit will prioritize acquiring images of that baggage. The acquisition unit can also prioritize the acquisition of images of baggage if the baggage owner is a frequent customer. The acquisition unit can also prioritize the acquisition of images of baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspection by prioritizing the acquisition of highly relevant data while considering the baggage owner's information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the efficiency of inspection by using an AI model that prioritizes the acquisition of highly relevant data while considering the baggage owner's information.

[0042] The acquisition unit can apply different acquisition algorithms based on the contents of the baggage when acquiring images of the baggage. For example, if the contents of the baggage are liquid, the acquisition unit can acquire them in detail using X-rays. For example, if the contents of the baggage are electronic devices, the acquisition unit can also acquire the internal shape using infrared light. For example, if the contents of the baggage are metal, the acquisition unit can also acquire the interior in detail using magnetic resonance. This improves the accuracy of image acquisition by applying different acquisition algorithms based on the contents of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that applies different acquisition algorithms based on the contents of the baggage.

[0043] The analysis unit can apply different analysis algorithms based on the type of contents of the baggage during analysis. For example, if the contents of the baggage are liquid, the analysis unit can apply an algorithm for analyzing the components of the liquid. For example, if the contents of the baggage are electronic devices, the analysis unit can also apply an algorithm for analyzing the structure of the electronic devices. For example, if the contents of the baggage are metals, the analysis unit can also apply an algorithm for analyzing the components of the metals. By applying different analysis algorithms based on the type of contents of the baggage, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that applies different analysis algorithms based on the type of contents of the baggage.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the baggage's past inspection history during the analysis. For example, the analysis unit applies an algorithm that detects similar patterns based on the baggage's past inspection history. For example, if an anomaly is detected from the baggage's past inspection history, the analysis unit can also perform the analysis based on that pattern. For example, if an anomaly is not detected by referring to the baggage's past inspection history, the analysis unit can also perform the analysis based on that pattern. This improves the accuracy of the analysis by referring to the baggage's past inspection history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's past inspection history.

[0045] The analysis unit can perform analysis based on the attribute information of the baggage owner. For example, if the baggage owner is a VIP, the analysis unit can perform a detailed analysis. For example, if the baggage owner is a frequent customer, the analysis unit can also perform analysis based on past data. For example, if the baggage owner falls into a specific risk profile, the analysis unit can also perform a risk-appropriate analysis. This improves the accuracy of the analysis by considering the attribute information of the baggage owner. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that performs analysis considering the attribute information of the baggage owner.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the baggage's geographical movement history during the analysis. For example, the analysis unit applies an algorithm to detect anomalies based on the baggage's past geographical movement history. The analysis unit can also apply an algorithm to detect anomalies in specific regions based on the baggage's geographical movement history. For example, the analysis unit can refer to the baggage's geographical movement history and, if no anomalies are detected, perform the analysis based on that pattern. This improves the accuracy of the analysis by referring to the baggage's geographical movement history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's geographical movement history.

[0047] The storage unit can adjust the level of detail of the data based on the importance of the baggage inspection results during storage. For example, the storage unit stores detailed data for highly important inspection results. For example, the storage unit can also store concise data for less important inspection results. For example, the storage unit can store data with an appropriate level of detail for moderately important inspection results. This allows for detailed storage of important data by adjusting the level of detail based on the importance of the baggage inspection results. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can store detailed important data using an AI model that adjusts the level of detail based on the importance of the baggage inspection results.

[0048] The data storage unit can apply different storage algorithms depending on the category of the baggage inspection result during storage. For example, if a dangerous item is detected, the storage unit can apply an algorithm that stores detailed data. For example, if a prohibited item is detected, the storage unit can also apply an algorithm that stores concise data. For example, if no abnormality is detected, the storage unit can also apply an algorithm that stores normal data. This improves the accuracy of data storage by applying different storage algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the accuracy of data storage by using an AI model that applies different storage algorithms depending on the category of the baggage inspection result.

[0049] The storage unit can prioritize storing highly relevant data by considering the baggage owner's information during storage. For example, if the baggage owner is a VIP, the storage unit will prioritize storing data for that baggage. The storage unit can also prioritize storing data for baggage if the baggage owner is a frequent customer. The storage unit can also prioritize storing data for baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspection by prioritizing the storage of highly relevant data by considering the baggage owner's information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the efficiency of inspection by using an AI model that prioritizes the storage of highly relevant data by considering the baggage owner's information.

[0050] The storage unit can weight the data based on when the baggage inspection results were submitted. For example, the storage unit may store recent inspection results with a higher weight. For example, it may store past inspection results with a lower weight. For example, it may store inspection results from a moderate period of time with an appropriate weight. This allows the storage unit to prioritize the storage of the latest data by weighting the data based on when the baggage inspection results were submitted. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can prioritize the storage of the latest data by using an AI model that weights the data based on when the baggage inspection results were submitted.

[0051] The adjustment unit can optimize its adjustment algorithm by referring to past data of baggage inspection results during the adjustment process. For example, the adjustment unit applies an algorithm that detects similar patterns based on past baggage inspection results. For example, if an anomaly is detected from past baggage inspection results, the adjustment unit can also perform adjustments based on that pattern. For example, if no anomaly is detected by referring to past baggage inspection results, the adjustment unit can also perform adjustments based on that pattern. This improves the accuracy of the adjustment by optimizing the adjustment algorithm by referring to past data of baggage inspection results. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that optimizes the adjustment algorithm by referring to past data of baggage inspection results.

[0052] The adjustment unit can apply different adjustment methods depending on the category of the baggage inspection result during adjustment. For example, if dangerous goods are detected, the adjustment unit may apply a detailed adjustment method. For example, if prohibited items are detected, the adjustment unit may also apply a concise adjustment method. For example, if no abnormalities are detected, the adjustment unit may also apply a standard adjustment method. This improves the accuracy of the adjustment by applying different adjustment methods depending on the category of the baggage inspection result. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that applies different adjustment methods depending on the category of the baggage inspection result.

[0053] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the baggage owner's information. For example, if the baggage owner is a VIP, the adjustment unit may prioritize the adjustment of that baggage. The adjustment unit may also prioritize the adjustment of that baggage if the baggage owner is a frequent customer. The adjustment unit may also prioritize the adjustment of that baggage if the baggage owner falls into a specific risk profile. This improves the accuracy of the adjustment by selecting the optimal adjustment method, taking into account the baggage owner's information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that selects the optimal adjustment method, taking into account the baggage owner's information.

[0054] The adjustment unit can improve the accuracy of its adjustments by referring to the geographical distribution of baggage inspection results during the adjustment process. For example, the adjustment unit applies an algorithm to detect anomalies based on the geographical history of baggage movement. The adjustment unit can also apply an algorithm to detect anomalies in specific regions based on the geographical movement history of baggage. For example, the adjustment unit can refer to the geographical movement history of baggage and, if no anomalies are detected, perform adjustments based on that pattern. This improves the accuracy of the adjustments by referring to the geographical distribution of baggage inspection results. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of its adjustments by using an AI model that improves the accuracy of adjustments by referring to the geographical distribution of baggage inspection results.

[0055] The display unit can adjust the level of detail displayed based on the importance of the baggage inspection results. For example, the display unit can display detailed information for highly important inspection results. For example, the display unit can also display concise information for less important inspection results. For example, the display unit can display information with an appropriate level of detail for moderately important inspection results. In this way, important information can be displayed in detail by adjusting the level of detail based on the importance of the baggage inspection results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display important information in detail using an AI model that adjusts the level of detail based on the importance of the baggage inspection results.

[0056] The display unit can apply different display algorithms depending on the category of the baggage inspection result when displaying information. For example, if a dangerous item is detected, the display unit can apply an algorithm that displays detailed information. For example, if a prohibited item is detected, the display unit can also apply an algorithm that displays concise information. For example, if no abnormalities are detected, the display unit can also apply an algorithm that displays normal information. This improves the accuracy of the display by applying different display algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can improve the accuracy of the display by using an AI model that applies different display algorithms depending on the category of the baggage inspection result.

[0057] The display unit can prioritize displaying highly relevant data, taking into account the baggage owner's information, when displaying information. For example, if the baggage owner is a VIP, the display unit will prioritize displaying information about that baggage. For example, if the baggage owner is a frequent customer, the display unit may also prioritize displaying information about that baggage. For example, if the baggage owner falls into a specific risk profile, the display unit may also prioritize displaying information about that baggage. This improves the efficiency of inspections by prioritizing the display of highly relevant data, taking into account the baggage owner's information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can improve the efficiency of inspections by using an AI model that prioritizes the display of highly relevant data, taking into account the baggage owner's information.

[0058] The display unit can adjust the display order based on when the baggage inspection results were submitted. For example, the display unit can prioritize displaying recent inspection results. For example, it can also postpone displaying past inspection results. For example, it can display inspection results from a moderate period of time in an appropriate order. This allows for the prioritization of the display of the most recent information by adjusting the display order based on when the baggage inspection results were submitted. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can prioritize the display of the most recent information by using an AI model that adjusts the display order based on when the baggage inspection results were submitted.

[0059] The alert unit can adjust the level of detail of an alert based on the importance of the baggage inspection result when an alert is issued. For example, the alert unit will issue a detailed alert for high-importance inspection results. For example, the alert unit can also issue a concise alert for low-importance inspection results. For example, the alert unit can issue an alert with an appropriate level of detail for moderately important inspection results. This allows important information to be alerted in detail by adjusting the level of detail of the alert based on the importance of the baggage inspection result. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can use an AI model that adjusts the level of detail of the alert based on the importance of the baggage inspection result to alert important information in detail.

[0060] The alert unit can apply different alert algorithms depending on the category of the baggage inspection result when an alert is issued. For example, if a dangerous item is detected, the alert unit can apply an algorithm that issues a detailed alert. For example, if a prohibited item is detected, the alert unit can also apply an algorithm that issues a concise alert. For example, if no abnormality is detected, the alert unit can also apply an algorithm that issues a normal alert. This improves the accuracy of the alert by applying different alert algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can improve the accuracy of the alert by using an AI model that applies different alert algorithms depending on the category of the baggage inspection result.

[0061] The alert unit can prioritize alerting on highly relevant data by considering the baggage owner's information when an alert is issued. For example, if the baggage owner is a VIP, the alert unit will prioritize alerting on information related to that baggage. The alert unit can also prioritize alerting on information related to baggage if the baggage owner is a frequent customer. The alert unit can also prioritize alerting on information related to baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspections by prioritizing alerts on highly relevant data by considering the baggage owner's information. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can improve the efficiency of inspections by using an AI model that prioritizes alerting on highly relevant data by considering the baggage owner's information.

[0062] The alert unit can adjust the order of alerts based on when the baggage inspection results are submitted. For example, the alert unit will prioritize alerts for recent inspection results. For example, the alert unit may postpone alerts for past inspection results. For example, the alert unit may issue alerts in an appropriate order for inspection results from the middle of the year. This allows for prioritizing alerts on the latest information by adjusting the order of alerts based on when the baggage inspection results are submitted. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can prioritize alerts on the latest information by using an AI model that adjusts the order of alerts based on when the baggage inspection results are submitted.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The baggage inspection system may also include a function to select an appropriate image acquisition method based on the shape and material of the baggage. For example, if the baggage is made of a hard material, X-rays can be used to acquire detailed images of the interior. If the baggage is made of a soft material, infrared light can be used to acquire the internal shape. If the baggage is made of a transparent material, visible light can be used to directly acquire images of the interior. This improves the accuracy of image acquisition by selecting the optimal image acquisition method based on the shape and material of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that selects the optimal image acquisition method based on the shape and material of the baggage.

[0065] The baggage inspection system can also be equipped with the ability to track the position and movement of baggage in real time and acquire images from the appropriate angle. For example, if baggage is moving on a conveyor belt, the camera can follow the baggage to acquire images from the optimal angle. If the baggage is stationary, multiple cameras can be used to acquire images from different angles. If the baggage is rotating, the camera angle can be adjusted to match the rotation to acquire an overall image. This allows for the acquisition of images from the optimal angle by tracking the position and movement of the baggage in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that tracks the position and movement of baggage in real time and acquires images from the optimal angle.

[0066] The baggage inspection system may also have the ability to apply different acquisition algorithms based on the contents of the baggage. For example, if the contents of the baggage are liquid, X-rays can be used to acquire detailed information. If the contents of the baggage are electronic devices, infrared light can be used to acquire the internal shape. If the contents of the baggage are metal, magnetic resonance can be used to acquire detailed information about the interior. This improves the accuracy of image acquisition by applying different acquisition algorithms based on the contents of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that applies different acquisition algorithms based on the contents of the baggage.

[0067] The baggage inspection system can also be equipped with a function to prioritize the acquisition of highly relevant data by considering information about the baggage owner. For example, if the baggage owner is a VIP, images of that baggage can be prioritized. If the baggage owner is a frequent customer, images of that baggage can also be prioritized. If the baggage owner falls into a specific risk profile, images of that baggage can also be prioritized. This improves the efficiency of inspection by prioritizing the acquisition of highly relevant data by considering information about the baggage owner. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can improve the efficiency of inspection by using an AI model that prioritizes the acquisition of highly relevant data by considering information about the baggage owner.

[0068] The baggage inspection system may also include a function to improve the accuracy of the analysis by referring to the baggage's past inspection history. For example, an algorithm can be applied to detect similar patterns based on the baggage's past inspection history. If an anomaly is detected from the baggage's past inspection history, the analysis can also be performed based on that pattern. If no anomalies are detected by referring to the baggage's past inspection history, the analysis can also be performed based on that pattern. In this way, the accuracy of the analysis is improved by referring to the baggage's past inspection history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's past inspection history.

[0069] The baggage inspection system may also include a function to improve the accuracy of the analysis by referring to the geographical movement history of the baggage. For example, an algorithm to detect anomalies may be applied based on the geographical history of the baggage's past movements. An algorithm to detect anomalies in a specific region may also be applied based on the baggage's geographical movement history. If no anomalies are detected by referring to the baggage's geographical movement history, the analysis may be performed based on that pattern. This improves the accuracy of the analysis by referring to the baggage's geographical movement history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's geographical movement history.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The acquisition unit acquires image data of the baggage. The acquisition unit can, for example, take images of the baggage with a camera and save them as digital data. The acquisition unit can also, for example, acquire images of the baggage with an X-ray device and save them as digital data. The acquisition unit can also, for example, acquire images of the baggage with an infrared device and save them as digital data. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit to detect hazardous materials or prohibited items. The analysis unit can, for example, use a generation AI to analyze the image data and detect hazardous materials. The analysis unit can also, for example, use a generation AI to analyze the image data and detect prohibited items. The analysis unit can also, for example, use a generation AI to analyze the image data and detect both hazardous materials and prohibited items simultaneously. Step 3: The storage unit stores the test results obtained by the analysis unit. The storage unit can, for example, save the test results to a database. The storage unit can also, for example, save the test results to cloud storage. The storage unit can also, for example, save the test results to local storage. Step 4: The tuning unit performs fine tuning based on the data from the storage unit. For example, the tuning unit adjusts the parameters of the generative AI using the data from the storage unit. The tuning unit can also retrain the generative AI model using the data from the storage unit. The tuning unit can also improve the generative AI algorithm using the data from the storage unit.

[0072] (Example of form 2) The baggage inspection system according to an embodiment of the present invention is a system that fully automates airport baggage inspection using generative AI. The baggage inspection system acquires image data of baggage, and the generative AI analyzes the image data to detect dangerous goods and prohibited items. This reduces the human resources required at the airport and improves the efficiency of inspections. Furthermore, the baggage inspection system accumulates inspection results and fine-tunes them by comparing the judgment results of inspectors and the generative AI to improve accuracy. Ultimately, it achieves full automation of baggage inspection using the generative AI's video analysis platform. This system is expected to improve the human resources required at airports, enhance safety, and increase passenger satisfaction. For example, the baggage inspection system acquires image data of baggage. For example, the baggage inspection system acquires image data of baggage, and the generative AI analyzes the image data to detect dangerous goods and prohibited items. The generative AI uses pattern recognition and anomaly detection algorithms to quickly and accurately identify dangerous goods and prohibited items. This reduces the human resources required and improves the efficiency of inspections. Furthermore, the baggage screening system will accumulate inspection results and fine-tune its accuracy by comparing the results of inspectors with those of the generating AI. Ultimately, it will achieve full automation of baggage screening using the generating AI's video analysis platform. This system is expected to improve airport human resources, enhance safety, and increase passenger satisfaction. For example, the baggage screening system will reduce passenger stress by shortening waiting times and decreasing the need for re-inspections. In addition, the advanced image analysis of the generating AI will provide a higher level of security than conventional baggage screening. As a result, the baggage screening system can improve airport human resources, enhance safety, and increase passenger satisfaction.

[0073] The baggage inspection system according to this embodiment comprises an acquisition unit, an analysis unit, a storage unit, and an adjustment unit. The acquisition unit acquires image data of baggage. The acquisition unit can, for example, capture image data of baggage with a camera and store it as digital data. The acquisition unit can also, for example, acquire image data of baggage with an X-ray device and store it as digital data. The acquisition unit can also, for example, acquire image data of baggage with an infrared device and store it as digital data. The analysis unit analyzes the image data acquired by the acquisition unit and detects dangerous goods or prohibited items. The analysis unit can, for example, analyze the image data using a generation AI and detect dangerous goods. The analysis unit can also, for example, analyze the image data using a generation AI and detect prohibited items. The analysis unit can also, for example, analyze the image data using a generation AI and detect dangerous goods and prohibited items simultaneously. The storage unit stores the inspection results obtained by the analysis unit. The storage unit can, for example, store the inspection results in a database. The storage unit can also, for example, store the inspection results in cloud storage. The storage unit can, for example, save inspection results to local storage. The adjustment unit performs fine tuning based on the data from the storage unit. The adjustment unit can, for example, adjust the parameters of the generated AI using the data from the storage unit. The adjustment unit can also, for example, retrain the model of the generated AI using the data from the storage unit. The adjustment unit can also, for example, improve the algorithm of the generated AI using the data from the storage unit. As a result, the baggage inspection system according to the embodiment can fully automate baggage inspection, reduce human resources, and improve inspection efficiency.

[0074] The acquisition unit acquires image data of baggage. For example, the acquisition unit captures image data of baggage with a camera and saves it as digital data. Specifically, it uses a high-resolution camera to capture detailed images of the baggage's exterior and acquires image data in real time. This allows for accurate capture of the baggage's shape and external characteristics. The acquisition unit can also acquire image data of baggage using an X-ray device and save it as digital data. The X-ray device can transparently photograph the internal structure of the baggage and identify different materials such as metals and organic materials. This provides basic data for detecting dangerous goods and prohibited items hidden inside the baggage. Furthermore, the acquisition unit can also acquire image data of baggage using an infrared device and save it as digital data. The infrared device can detect the surface temperature and thermal radiation characteristics of the baggage and identify abnormal temperature changes and heat sources. This provides additional information for detecting heat-generating objects and electronic devices hidden inside the baggage. The acquisition unit integrates the diverse image data obtained from these different devices to provide basic data for detailed baggage inspection. This allows the acquisition unit to collect multifaceted information such as the appearance, internal structure, and thermal characteristics of the baggage, supporting highly accurate inspections by the analysis unit.

[0075] The analysis unit analyzes image data acquired by the acquisition unit to detect hazardous materials or prohibited items. The analysis uses a generative AI to analyze the image data and detect hazardous materials. Specifically, the generative AI is pre-trained using a large amount of baggage image data, enabling it to identify the characteristics of hazardous materials and prohibited items with high accuracy. For example, the generative AI can analyze X-ray images of baggage to identify the shapes of metallic weapons and explosives. It can also analyze infrared images to detect abnormal heat sources and heat-generating objects. Furthermore, the generative AI can analyze external images of baggage to identify labels and shapes of prohibited items. The analysis unit integrates these analysis results to make a comprehensive determination of the presence or absence of hazardous materials and prohibited items in the baggage. The analysis unit processes image data in real time, enabling it to provide inspection results quickly and accurately. Additionally, the analysis unit can utilize past inspection results and statistical data to improve detection accuracy and identify new hazardous materials. For example, the analysis unit can learn specific patterns and trends based on past inspection data to improve the detection algorithm for new hazardous materials. This allows the analysis unit to perform highly accurate inspections based on the latest information at all times, improving the reliability and security of baggage inspections.

[0076] The storage unit stores the inspection results obtained by the analysis unit. For example, the storage unit saves the inspection results to a database. Specifically, the inspection results include baggage image data, analysis results, and information on detected hazardous materials and prohibited items. This data is systematically stored in the database and used for later analysis and reference. The storage unit can also save the inspection results to cloud storage. Cloud storage allows for efficient storage of large amounts of data and quick access as needed. Furthermore, the storage unit can also save the inspection results to local storage. Local storage enables data storage and access even in environments with unstable network connectivity, improving system reliability. The storage unit can combine these storage methods to ensure data redundancy and availability. Additionally, the storage unit can periodically back up the stored data and take measures to prevent data loss or corruption. This allows the storage unit to safely and efficiently store the inspection results obtained by the analysis unit and support overall system data management.

[0077] The adjustment unit performs fine tuning based on the data from the storage unit. For example, the adjustment unit adjusts the parameters of the generating AI using the data from the storage unit. Specifically, it optimizes the learning rate and weight parameters of the generating AI to improve detection accuracy. The adjustment unit can also retrain the generating AI model using the data from the storage unit. Retraining allows the generating AI to learn new data and patterns, improving its detection capabilities. Furthermore, the adjustment unit can also improve the algorithm of the generating AI using the data from the storage unit. By improving the algorithm, the generating AI can analyze image data more efficiently and accurately, improving its ability to detect dangerous goods and prohibited items. The adjustment unit performs these adjustments regularly to keep the performance of the generating AI in an optimal state at all times. In addition, the adjustment unit can adjust the generating AI in response to user feedback and new regulations. In this way, the adjustment unit can continuously improve the performance of the generating AI by utilizing the data from the storage unit, thereby enhancing the reliability and security of the baggage inspection system.

[0078] The analysis unit can detect hazardous materials or prohibited items using pattern recognition or anomaly detection algorithms. For example, the analysis unit can analyze image data using a neural network to detect hazardous materials. The analysis unit can also analyze image data using a support vector machine to detect prohibited items. Furthermore, the analysis unit can analyze image data using statistical methods to simultaneously detect both hazardous materials and prohibited items. This improves the accuracy of hazardous materials and prohibited items detection by employing pattern recognition and anomaly detection algorithms.

[0079] The storage unit can store inspection results and compare the judgments of the inspector and the generating AI. For example, the storage unit can save inspection results to a database and compare the inspector's judgment with the generating AI's judgment. The storage unit can also save inspection results to cloud storage and compare the inspector's judgment with the generating AI's judgment. The storage unit can also save inspection results to local storage and compare the inspector's judgment with the generating AI's judgment. This improves the accuracy of the system by storing inspection results and comparing the judgments of the inspector and the generating AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the accuracy of the system by using an AI model that stores inspection results in a database and compares the inspector's judgment with the generating AI's judgment.

[0080] The adjustment unit can perform fine-tuning based on the data from the storage unit to improve the accuracy of the analysis. For example, the adjustment unit can adjust the parameters of the generating AI using the data from the storage unit. The adjustment unit can also retrain the model of the generating AI using the data from the storage unit. For example, the adjustment unit can improve the algorithm of the generating AI using the data from the storage unit. As a result, the accuracy of the system is improved by performing fine-tuning based on the data from the storage unit. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the system by using an AI model that adjusts the parameters of the generating AI using the data from the storage unit.

[0081] The display unit can display the test results. The display unit can, for example, display the test results on a display screen. The display unit can also, for example, display the test results on a monitor. The display unit can also, for example, display the test results on a smartphone screen. This allows the user to check the test results by displaying them. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that displays the test results on a display screen to allow the user to check the test results.

[0082] The alert unit can issue alerts based on inspection results. The alert unit can issue, for example, an audio alert. The alert unit can also issue, for example, a visual alert. The alert unit can also issue, for example, a notification alert. This allows for a rapid response when hazardous materials or prohibited items are detected by issuing alerts based on inspection results. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can use an AI model that issues alerts based on inspection results to enable a rapid response when hazardous materials or prohibited items are detected.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of baggage image data acquisition based on the estimated emotions. For example, if the user is nervous, the acquisition unit can delay the baggage image data acquisition timing and wait for the user to calm down. For example, if the user is in a hurry, the acquisition unit can speed up the baggage image data acquisition timing to perform a quick inspection. For example, if the user is relaxed, the acquisition unit can acquire baggage image data at the normal timing. In this way, by adjusting the timing of baggage image data acquisition according to the user's emotions, user stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can reduce user stress by using an AI model that estimates the user's emotions and adjusts the timing of baggage image data acquisition based on the estimated emotions.

[0084] The acquisition unit can select an appropriate image acquisition method based on the shape and material of the luggage. For example, if the luggage is made of a hard material, the acquisition unit can use X-rays to acquire detailed information about the interior. If the luggage is made of a soft material, the acquisition unit can also use infrared light to acquire information about the interior shape. If the luggage is made of a transparent material, the acquisition unit can also use visible light to directly acquire information about the interior. By selecting the optimal image acquisition method based on the shape and material of the luggage, the accuracy of image acquisition is improved. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that selects the optimal image acquisition method based on the shape and material of the luggage.

[0085] The acquisition unit can track the position and movement of luggage in real time and acquire images from the appropriate angle. For example, if luggage is moving on a conveyor belt, the acquisition unit can make the camera follow the luggage and acquire images from the optimal angle. For example, if luggage is stationary, the acquisition unit can also use multiple cameras to acquire images from different angles. For example, if luggage is rotating, the acquisition unit can adjust the camera angle to match the rotation and acquire an overall image. This allows for the acquisition of images from the optimal angle by tracking the position and movement of luggage in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that tracks the position and movement of luggage in real time and acquires images from the optimal angle.

[0086] The retrieval unit can estimate the user's emotions and determine the priority of luggage to retrieve based on the estimated emotions. For example, if the user is in a hurry, the retrieval unit will prioritize retrieving that user's luggage. If the user is relaxed, the retrieval unit can also retrieve luggage in the normal order. If the user is feeling anxious, the retrieval unit can retrieve that user's luggage earlier to reassure them. This reduces user stress by determining luggage priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the retrieval unit may be performed using AI, for example, or without AI. For example, the retrieval unit can reduce user stress by using an AI model that estimates the user's emotions and determines the priority of luggage to retrieve based on the estimated emotions.

[0087] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring images of baggage, taking into account the baggage owner's information. For example, if the baggage owner is a VIP, the acquisition unit will prioritize acquiring images of that baggage. The acquisition unit can also prioritize the acquisition of images of baggage if the baggage owner is a frequent customer. The acquisition unit can also prioritize the acquisition of images of baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspection by prioritizing the acquisition of highly relevant data while considering the baggage owner's information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the efficiency of inspection by using an AI model that prioritizes the acquisition of highly relevant data while considering the baggage owner's information.

[0088] The acquisition unit can apply different acquisition algorithms based on the contents of the baggage when acquiring images of the baggage. For example, if the contents of the baggage are liquid, the acquisition unit can acquire them in detail using X-rays. For example, if the contents of the baggage are electronic devices, the acquisition unit can also acquire the internal shape using infrared light. For example, if the contents of the baggage are metal, the acquisition unit can also acquire the interior in detail using magnetic resonance. This improves the accuracy of image acquisition by applying different acquisition algorithms based on the contents of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that applies different acquisition algorithms based on the contents of the baggage.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results. This allows for a deeper understanding of the user by adjusting the display method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can deepen user understanding by using an AI model that estimates the user's emotions and adjusts the display method of the analysis based on the estimated user emotions.

[0090] The analysis unit can apply different analysis algorithms based on the type of contents of the baggage during analysis. For example, if the contents of the baggage are liquid, the analysis unit can apply an algorithm for analyzing the components of the liquid. For example, if the contents of the baggage are electronic devices, the analysis unit can also apply an algorithm for analyzing the structure of the electronic devices. For example, if the contents of the baggage are metals, the analysis unit can also apply an algorithm for analyzing the components of the metals. By applying different analysis algorithms based on the type of contents of the baggage, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that applies different analysis algorithms based on the type of contents of the baggage.

[0091] The analysis unit can improve the accuracy of its analysis by referring to the baggage's past inspection history during the analysis. For example, the analysis unit applies an algorithm that detects similar patterns based on the baggage's past inspection history. For example, if an anomaly is detected from the baggage's past inspection history, the analysis unit can also perform the analysis based on that pattern. For example, if an anomaly is not detected by referring to the baggage's past inspection history, the analysis unit can also perform the analysis based on that pattern. This improves the accuracy of the analysis by referring to the baggage's past inspection history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's past inspection history.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can deepen the user's understanding by using an AI model that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions.

[0093] The analysis unit can perform analysis based on the attribute information of the baggage owner. For example, if the baggage owner is a VIP, the analysis unit can perform a detailed analysis. For example, if the baggage owner is a frequent customer, the analysis unit can also perform analysis based on past data. For example, if the baggage owner falls into a specific risk profile, the analysis unit can also perform a risk-appropriate analysis. This improves the accuracy of the analysis by considering the attribute information of the baggage owner. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that performs analysis considering the attribute information of the baggage owner.

[0094] The analysis unit can improve the accuracy of its analysis by referring to the baggage's geographical movement history during the analysis. For example, the analysis unit applies an algorithm to detect anomalies based on the baggage's past geographical movement history. The analysis unit can also apply an algorithm to detect anomalies in specific regions based on the baggage's geographical movement history. For example, the analysis unit can refer to the baggage's geographical movement history and, if no anomalies are detected, perform the analysis based on that pattern. This improves the accuracy of the analysis by referring to the baggage's geographical movement history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's geographical movement history.

[0095] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is tense, the data storage unit will prioritize storing important data. For example, if the user is relaxed, the data storage unit can also store detailed data. For example, if the user is in a hurry, the data storage unit can also store concise data. This allows for the priority storage of important data by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI, or not. For example, the data storage unit can prioritize storing important data using an AI model that estimates the user's emotions and selects data to store based on the estimated emotions.

[0096] The storage unit can adjust the level of detail of the data based on the importance of the baggage inspection results during storage. For example, the storage unit stores detailed data for highly important inspection results. For example, the storage unit can also store concise data for less important inspection results. For example, the storage unit can store data with an appropriate level of detail for moderately important inspection results. This allows for detailed storage of important data by adjusting the level of detail based on the importance of the baggage inspection results. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can store detailed important data using an AI model that adjusts the level of detail based on the importance of the baggage inspection results.

[0097] The data storage unit can apply different storage algorithms depending on the category of the baggage inspection result during storage. For example, if a dangerous item is detected, the storage unit can apply an algorithm that stores detailed data. For example, if a prohibited item is detected, the storage unit can also apply an algorithm that stores concise data. For example, if no abnormality is detected, the storage unit can also apply an algorithm that stores normal data. This improves the accuracy of data storage by applying different storage algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the accuracy of data storage by using an AI model that applies different storage algorithms depending on the category of the baggage inspection result.

[0098] The data storage unit can estimate the user's emotions and prioritize the stored data based on the estimated emotions. For example, if the user is tense, the storage unit will prioritize storing important data. If the user is relaxed, the storage unit may also prioritize storing detailed data. If the user is in a hurry, the storage unit may also prioritize storing concise data. This allows for the priority storage of important data by prioritizing the stored data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can prioritize storing important data using an AI model that estimates the user's emotions and prioritizes the stored data based on the estimated emotions.

[0099] The storage unit can prioritize storing highly relevant data by considering the baggage owner's information during storage. For example, if the baggage owner is a VIP, the storage unit will prioritize storing data for that baggage. The storage unit can also prioritize storing data for baggage if the baggage owner is a frequent customer. The storage unit can also prioritize storing data for baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspection by prioritizing the storage of highly relevant data by considering the baggage owner's information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can improve the efficiency of inspection by using an AI model that prioritizes the storage of highly relevant data by considering the baggage owner's information.

[0100] The storage unit can weight the data based on when the baggage inspection results were submitted. For example, the storage unit may store recent inspection results with a higher weight. For example, it may store past inspection results with a lower weight. For example, it may store inspection results from a moderate period of time with an appropriate weight. This allows the storage unit to prioritize the storage of the latest data by weighting the data based on when the baggage inspection results were submitted. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can prioritize the storage of the latest data by using an AI model that weights the data based on when the baggage inspection results were submitted.

[0101] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is tense, the adjustment unit can provide a simple and easily understandable adjustment method. For example, if the user is relaxed, the adjustment unit can also provide a detailed adjustment method. For example, if the user is in a hurry, the adjustment unit can provide a concise adjustment method. This deepens the understanding of the user by adjusting the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can deepen its understanding of the user by using an AI model that estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions.

[0102] The adjustment unit can optimize its adjustment algorithm by referring to past data of baggage inspection results during the adjustment process. For example, the adjustment unit applies an algorithm that detects similar patterns based on past baggage inspection results. For example, if an anomaly is detected from past baggage inspection results, the adjustment unit can also perform adjustments based on that pattern. For example, if no anomaly is detected by referring to past baggage inspection results, the adjustment unit can also perform adjustments based on that pattern. This improves the accuracy of the adjustment by optimizing the adjustment algorithm by referring to past data of baggage inspection results. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that optimizes the adjustment algorithm by referring to past data of baggage inspection results.

[0103] The adjustment unit can apply different adjustment methods depending on the category of the baggage inspection result during adjustment. For example, if dangerous goods are detected, the adjustment unit may apply a detailed adjustment method. For example, if prohibited items are detected, the adjustment unit may also apply a concise adjustment method. For example, if no abnormalities are detected, the adjustment unit may also apply a standard adjustment method. This improves the accuracy of the adjustment by applying different adjustment methods depending on the category of the baggage inspection result. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that applies different adjustment methods depending on the category of the baggage inspection result.

[0104] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is tense, the adjustment unit will prioritize important adjustments. For example, if the user is relaxed, the adjustment unit may also prioritize detailed adjustments. For example, if the user is in a hurry, the adjustment unit may also prioritize concise adjustments. In this way, by determining the priority of adjustments according to the user's emotions, important adjustments can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can prioritize important adjustments by using an AI model that estimates the user's emotions and determines the priority of adjustments based on the estimated emotions.

[0105] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the baggage owner's information. For example, if the baggage owner is a VIP, the adjustment unit may prioritize the adjustment of that baggage. The adjustment unit may also prioritize the adjustment of that baggage if the baggage owner is a frequent customer. The adjustment unit may also prioritize the adjustment of that baggage if the baggage owner falls into a specific risk profile. This improves the accuracy of the adjustment by selecting the optimal adjustment method, taking into account the baggage owner's information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can improve the accuracy of the adjustment by using an AI model that selects the optimal adjustment method, taking into account the baggage owner's information.

[0106] The adjustment unit can improve the accuracy of its adjustments by referring to the geographical distribution of baggage inspection results during the adjustment process. For example, the adjustment unit applies an algorithm to detect anomalies based on the geographical history of baggage movement. The adjustment unit can also apply an algorithm to detect anomalies in specific regions based on the geographical movement history of baggage. For example, the adjustment unit can refer to the geographical movement history of baggage and, if no anomalies are detected, perform adjustments based on that pattern. This improves the accuracy of the adjustments by referring to the geographical distribution of baggage inspection results. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can improve the accuracy of its adjustments by using an AI model that improves the accuracy of adjustments by referring to the geographical distribution of baggage inspection results.

[0107] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, the user's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can deepen the user's understanding by using an AI model that estimates the user's emotions and adjusts the display method based on the estimated emotions.

[0108] The display unit can adjust the level of detail displayed based on the importance of the baggage inspection results. For example, the display unit can display detailed information for highly important inspection results. For example, the display unit can also display concise information for less important inspection results. For example, the display unit can display information with an appropriate level of detail for moderately important inspection results. In this way, important information can be displayed in detail by adjusting the level of detail based on the importance of the baggage inspection results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display important information in detail using an AI model that adjusts the level of detail based on the importance of the baggage inspection results.

[0109] The display unit can apply different display algorithms depending on the category of the baggage inspection result when displaying information. For example, if a dangerous item is detected, the display unit can apply an algorithm that displays detailed information. For example, if a prohibited item is detected, the display unit can also apply an algorithm that displays concise information. For example, if no abnormalities are detected, the display unit can also apply an algorithm that displays normal information. This improves the accuracy of the display by applying different display algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can improve the accuracy of the display by using an AI model that applies different display algorithms depending on the category of the baggage inspection result.

[0110] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is tense, the display unit will prioritize displaying important information. For example, if the user is relaxed, the display unit may also prioritize displaying detailed information. For example, if the user is in a hurry, the display unit may also prioritize displaying concise information. In this way, by determining the display priority according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can prioritize displaying important information using an AI model that estimates the user's emotions and determines the display priority based on the estimated emotions.

[0111] The display unit can prioritize displaying highly relevant data, taking into account the baggage owner's information, when displaying information. For example, if the baggage owner is a VIP, the display unit will prioritize displaying information about that baggage. For example, if the baggage owner is a frequent customer, the display unit may also prioritize displaying information about that baggage. For example, if the baggage owner falls into a specific risk profile, the display unit may also prioritize displaying information about that baggage. This improves the efficiency of inspections by prioritizing the display of highly relevant data, taking into account the baggage owner's information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can improve the efficiency of inspections by using an AI model that prioritizes the display of highly relevant data, taking into account the baggage owner's information.

[0112] The display unit can adjust the display order based on when the baggage inspection results were submitted. For example, the display unit can prioritize displaying recent inspection results. For example, it can also postpone displaying past inspection results. For example, it can display inspection results from a moderate period of time in an appropriate order. This allows for the prioritization of the display of the most recent information by adjusting the display order based on when the baggage inspection results were submitted. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can prioritize the display of the most recent information by using an AI model that adjusts the display order based on when the baggage inspection results were submitted.

[0113] The alert unit can estimate the user's emotions and adjust the alert delivery method based on the estimated emotions. For example, if the user is nervous, the alert unit may deliver an alert in a calm voice. For example, if the user is relaxed, the alert unit may deliver an alert in a cheerful voice. For example, if the user is in a hurry, the alert unit may deliver a quick and concise alert. This allows for a deeper understanding of the user by adjusting the alert delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can deepen its understanding of the user by using an AI model that estimates the user's emotions and adjusts the alert delivery method based on the estimated emotions.

[0114] The alert unit can adjust the level of detail of an alert based on the importance of the baggage inspection result when an alert is issued. For example, the alert unit will issue a detailed alert for high-importance inspection results. For example, the alert unit can also issue a concise alert for low-importance inspection results. For example, the alert unit can issue an alert with an appropriate level of detail for moderately important inspection results. This allows important information to be alerted in detail by adjusting the level of detail of the alert based on the importance of the baggage inspection result. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can use an AI model that adjusts the level of detail of the alert based on the importance of the baggage inspection result to alert important information in detail.

[0115] The alert unit can apply different alert algorithms depending on the category of the baggage inspection result when an alert is issued. For example, if a dangerous item is detected, the alert unit can apply an algorithm that issues a detailed alert. For example, if a prohibited item is detected, the alert unit can also apply an algorithm that issues a concise alert. For example, if no abnormality is detected, the alert unit can also apply an algorithm that issues a normal alert. This improves the accuracy of the alert by applying different alert algorithms depending on the category of the baggage inspection result. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can improve the accuracy of the alert by using an AI model that applies different alert algorithms depending on the category of the baggage inspection result.

[0116] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit will prioritize important alerts. For example, if the user is relaxed, the alert unit may also prioritize detailed alerts. For example, if the user is in a hurry, the alert unit may also prioritize concise alerts. This allows for the priority of important alerts by determining the priority of alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can prioritize important alerts by using an AI model that estimates the user's emotions and determines the priority of alerts based on the estimated emotions.

[0117] The alert unit can prioritize alerting on highly relevant data by considering the baggage owner's information when an alert is issued. For example, if the baggage owner is a VIP, the alert unit will prioritize alerting on information related to that baggage. The alert unit can also prioritize alerting on information related to baggage if the baggage owner is a frequent customer. The alert unit can also prioritize alerting on information related to baggage if the baggage owner falls into a specific risk profile. This improves the efficiency of inspections by prioritizing alerts on highly relevant data by considering the baggage owner's information. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can improve the efficiency of inspections by using an AI model that prioritizes alerting on highly relevant data by considering the baggage owner's information.

[0118] The alert unit can adjust the order of alerts based on when the baggage inspection results are submitted. For example, the alert unit will prioritize alerts for recent inspection results. For example, the alert unit may postpone alerts for past inspection results. For example, the alert unit may issue alerts in an appropriate order for inspection results from the middle of the year. This allows for prioritizing alerts on the latest information by adjusting the order of alerts based on when the baggage inspection results are submitted. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can prioritize alerts on the latest information by using an AI model that adjusts the order of alerts based on when the baggage inspection results are submitted.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The baggage inspection system may also have a function to estimate the user's emotions and determine inspection priorities based on those emotions. For example, if the user is in a hurry, their baggage may be inspected first. If the user is relaxed, their baggage may be inspected in the normal order. If the user is feeling anxious, their baggage may be inspected earlier to reassure them. This reduces user stress by determining inspection priorities according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can reduce user stress by using an AI model that estimates the user's emotions and determines inspection priorities based on those estimated emotions.

[0121] The baggage inspection system may also include a function to select an appropriate image acquisition method based on the shape and material of the baggage. For example, if the baggage is made of a hard material, X-rays can be used to acquire detailed images of the interior. If the baggage is made of a soft material, infrared light can be used to acquire the internal shape. If the baggage is made of a transparent material, visible light can be used to directly acquire images of the interior. This improves the accuracy of image acquisition by selecting the optimal image acquisition method based on the shape and material of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that selects the optimal image acquisition method based on the shape and material of the baggage.

[0122] The baggage inspection system can also be equipped with the ability to track the position and movement of baggage in real time and acquire images from the appropriate angle. For example, if baggage is moving on a conveyor belt, the camera can follow the baggage to acquire images from the optimal angle. If the baggage is stationary, multiple cameras can be used to acquire images from different angles. If the baggage is rotating, the camera angle can be adjusted to match the rotation to acquire an overall image. This allows for the acquisition of images from the optimal angle by tracking the position and movement of the baggage in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that tracks the position and movement of baggage in real time and acquires images from the optimal angle.

[0123] The baggage inspection system may also have the ability to apply different acquisition algorithms based on the contents of the baggage. For example, if the contents of the baggage are liquid, X-rays can be used to acquire detailed information. If the contents of the baggage are electronic devices, infrared light can be used to acquire the internal shape. If the contents of the baggage are metal, magnetic resonance can be used to acquire detailed information about the interior. This improves the accuracy of image acquisition by applying different acquisition algorithms based on the contents of the baggage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can improve the accuracy of image acquisition by using an AI model that applies different acquisition algorithms based on the contents of the baggage.

[0124] The baggage inspection system can also be equipped with a function to prioritize the acquisition of highly relevant data by considering information about the baggage owner. For example, if the baggage owner is a VIP, images of that baggage can be prioritized. If the baggage owner is a frequent customer, images of that baggage can also be prioritized. If the baggage owner falls into a specific risk profile, images of that baggage can also be prioritized. This improves the efficiency of inspection by prioritizing the acquisition of highly relevant data by considering information about the baggage owner. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can improve the efficiency of inspection by using an AI model that prioritizes the acquisition of highly relevant data by considering information about the baggage owner.

[0125] The baggage inspection system may also have the function to estimate the user's emotions and determine the priority of baggage to be retrieved based on those emotions. For example, if the user is in a hurry, their baggage may be retrieved first. If the user is relaxed, their baggage may be retrieved in the normal order. If the user is feeling anxious, their baggage may be retrieved early to reassure them. This reduces user stress by determining baggage priority according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the retrieval unit may be performed using, for example, AI, or not using AI. For example, the retrieval unit can reduce user stress by using an AI model that estimates the user's emotions and determines the priority of baggage to be retrieved based on those estimated emotions.

[0126] The baggage inspection system may also have a function to estimate the user's emotions and adjust the display method of the analysis based on the estimated emotions. For example, if the user is nervous, it may provide a simple and easy-to-read analysis result. If the user is relaxed, it may also provide a detailed analysis result. If the user is in a hurry, it may also provide a concise analysis result. This allows for a deeper understanding of the user by adjusting the display method of the analysis according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can deepen its understanding of the user by using an AI model that estimates the user's emotions and adjusts the display method of the analysis based on the estimated user emotions.

[0127] The baggage inspection system may also include a function to improve the accuracy of the analysis by referring to the baggage's past inspection history. For example, an algorithm can be applied to detect similar patterns based on the baggage's past inspection history. If an anomaly is detected from the baggage's past inspection history, the analysis can also be performed based on that pattern. If no anomalies are detected by referring to the baggage's past inspection history, the analysis can also be performed based on that pattern. In this way, the accuracy of the analysis is improved by referring to the baggage's past inspection history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's past inspection history.

[0128] The baggage inspection system may also include a function to improve the accuracy of the analysis by referring to the geographical movement history of the baggage. For example, an algorithm to detect anomalies may be applied based on the geographical history of the baggage's past movements. An algorithm to detect anomalies in a specific region may also be applied based on the baggage's geographical movement history. If no anomalies are detected by referring to the baggage's geographical movement history, the analysis may be performed based on that pattern. This improves the accuracy of the analysis by referring to the baggage's geographical movement history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can improve the accuracy of the analysis by using a generative AI model that improves the accuracy of the analysis by referring to the baggage's geographical movement history.

[0129] The baggage inspection system can also be equipped with a function to estimate the user's emotions and select the data to be stored based on the estimated emotions. For example, if the user is nervous, important data can be prioritized for storage. If the user is relaxed, detailed data can also be stored. If the user is in a hurry, concise data can also be stored. This allows for the priority storage of important data by selecting the data to be stored according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can prioritize the storage of important data using an AI model that estimates the user's emotions and selects the data to be stored based on the estimated user emotions.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The acquisition unit acquires image data of the baggage. The acquisition unit can, for example, take images of the baggage with a camera and save them as digital data. The acquisition unit can also, for example, acquire images of the baggage with an X-ray device and save them as digital data. The acquisition unit can also, for example, acquire images of the baggage with an infrared device and save them as digital data. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit to detect hazardous materials or prohibited items. The analysis unit can, for example, use a generation AI to analyze the image data and detect hazardous materials. The analysis unit can also, for example, use a generation AI to analyze the image data and detect prohibited items. The analysis unit can also, for example, use a generation AI to analyze the image data and detect both hazardous materials and prohibited items simultaneously. Step 3: The storage unit stores the test results obtained by the analysis unit. The storage unit can, for example, save the test results to a database. The storage unit can also, for example, save the test results to cloud storage. The storage unit can also, for example, save the test results to local storage. Step 4: The tuning unit performs fine tuning based on the data from the storage unit. For example, the tuning unit adjusts the parameters of the generative AI using the data from the storage unit. The tuning unit can also retrain the generative AI model using the data from the storage unit. The tuning unit can also improve the generative AI algorithm using the data from the storage unit.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the acquisition unit, analysis unit, storage unit, adjustment unit, display unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire image data of luggage using the camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the image data using a generation AI to detect dangerous goods and prohibited items. The storage unit stores the inspection results in the database 24 of the data processing unit 12. The adjustment unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and adjusts the parameters of the generation AI using the data from the storage unit. The display unit displays the inspection results on the display 40A of the smart device 14. The alert unit can issue an audio alert using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the acquisition unit, analysis unit, storage unit, adjustment unit, display unit, and alert unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire image data of luggage using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the image data using a generation AI to detect dangerous goods and prohibited items. The storage unit stores the inspection results in the database 24 of the data processing unit 12. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which adjusts the parameters of the generation AI using the data from the storage unit. The display unit displays the inspection results on the display of the smart glasses 214. The alert unit can issue voice alerts using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the acquisition unit, analysis unit, storage unit, adjustment unit, display unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire image data of luggage using the camera 42 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the image data using a generation AI to detect dangerous goods and prohibited items. The storage unit stores the inspection results in the database 24 of the data processing unit 12. The adjustment unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and adjusts the parameters of the generation AI using the data from the storage unit. The display unit displays the inspection results on the display 343 of the headset terminal 314. The alert unit can issue voice alerts using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the acquisition unit, analysis unit, storage unit, adjustment unit, display unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire image data of luggage using the camera 42 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the image data using a generation AI to detect dangerous goods and prohibited items. The storage unit stores the inspection results in the database 24 of the data processing unit 12. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which adjusts the parameters of the generation AI using the data from the storage unit. The display unit displays the inspection results on the display of the robot 414. The alert unit can issue an audio alert using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0185] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) An acquisition unit that acquires image data of baggage, An analysis unit analyzes image data acquired by the acquisition unit to detect hazardous materials or prohibited items, An accumulation unit for accumulating the inspection results obtained by the analysis unit, The system includes an adjustment unit that performs fine tuning based on the data from the aforementioned storage unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Detecting hazardous materials or prohibited items using pattern recognition or anomaly detection algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The storage unit is The system accumulates inspection results and compares them with the judgments of the inspectors and the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Fine-tuning is performed based on the data from the storage unit to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a display unit that shows the test results. The system described in Appendix 1, characterized by the features described herein. (Note 6) It is equipped with an alert unit that issues alerts based on the test results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of baggage image data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Select the appropriate image acquisition method based on the shape and material of the baggage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Track the location and movement of luggage in real time and acquire images from the appropriate angle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of the luggage to be retrieved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring images of baggage, the system prioritizes acquiring data that is highly relevant based on the baggage owner's information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring images of baggage, different acquisition algorithms are applied based on the contents of the baggage. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the type of contents of the baggage. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referring to the baggage's past inspection history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the analysis is performed based on the attribute information of the baggage owner. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the geographical movement history of the baggage is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The storage unit is The system estimates the user's emotions and selects stored data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The storage unit is When data is stored, the level of detail is adjusted based on the importance of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The storage unit is During data accumulation, different accumulation algorithms are applied depending on the category of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The storage unit is It estimates user emotions and prioritizes accumulated data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The storage unit is During data accumulation, the system prioritizes the accumulation of highly relevant data based on the baggage owner's information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The storage unit is During data accumulation, the data is weighted based on when the baggage inspection results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, The system estimates the user's emotions and adjusts the adjustment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During the adjustment process, the adjustment algorithm is optimized by referring to past data on baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During the adjustment process, different adjustment methods will be applied depending on the category of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, the appropriate adjustment method will be selected based on the baggage owner's information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, the geographical distribution of baggage inspection results is referenced to improve the accuracy of the adjustment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying the information, adjust the level of detail based on the importance of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying the results, different display algorithms are applied depending on the category of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is When displaying information, the system prioritizes showing data that is most relevant based on the baggage owner's information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned display unit is When displaying the information, the order of display will be adjusted based on when the baggage inspection results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The alert unit is, The system estimates the user's emotions and adjusts the alert delivery method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The alert unit is, When an alert is issued, the level of detail of the alert is adjusted based on the importance of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 39) The alert unit is, When an alert is issued, a different alert algorithm is applied depending on the category of the baggage inspection results. The system described in Appendix 1, characterized by the features described herein. (Note 40) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The alert unit is, When an alert is issued, the system prioritizes alerting users with the most relevant data based on the baggage owner's information. The system described in Appendix 1, characterized by the features described herein. (Note 42) The alert unit is, When an alert is issued, the order of alerts will be adjusted based on when the baggage inspection results are submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires image data of baggage, An analysis unit analyzes image data acquired by the acquisition unit to detect hazardous materials or prohibited items, An accumulation unit for accumulating the inspection results obtained by the analysis unit, The system includes an adjustment unit that performs fine tuning based on the data from the aforementioned storage unit. A system characterized by the following features.

2. The aforementioned analysis unit, Detecting hazardous materials or prohibited items using pattern recognition or anomaly detection algorithms. The system according to feature 1.

3. The storage unit is The test results are accumulated and compared with the judgments of the inspector and the generated AI. The system according to feature 1.

4. The adjustment unit is, Fine-tuning is performed based on the data from the aforementioned storage unit to improve the accuracy of the analysis. The system according to feature 1.

5. It is equipped with a display unit that shows the test results. The system according to feature 1.

6. It is equipped with an alert unit that issues alerts based on the test results. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of baggage image data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Select the appropriate image acquisition method based on the shape and material of the baggage. The system according to feature 1.

Images