system

Abstract

The system according to this embodiment aims to achieve automatic classification and highly accurate matching of lost items, thereby providing an efficient return process. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, a return unit, and a tracking unit. The collection unit collects images of lost items. The analysis unit analyzes the images collected by the collection unit and extracts features. The matching unit matches the lost items with the person who lost them based on the feature information extracted by the analysis unit. The return unit returns the lost items that have been matched by the matching unit. The tracking unit tracks the location of the lost items that have been returned by the return 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the manual classification of lost items and the matching accuracy with the lost persons are low, and there is a risk that a large number of lost items will become a source of confusion.

[0005] The system according to the embodiment aims to realize automatic classification of lost items and high-precision matching, and provide an efficient return process.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a return unit, and a tracking unit. The collection unit collects images of lost items. The analysis unit analyzes the images collected by the collection unit and extracts features. The matching unit matches the lost items with the person who lost them based on the feature information extracted by the analysis unit. The return unit returns the lost items that have been matched by the matching unit. The tracking unit tracks the location of the lost items returned by the return unit. [Effects of the Invention]

[0007] The system according to this embodiment can achieve automatic classification and highly accurate matching of lost items, and provide an efficient return process. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The lost and found management system according to an embodiment of the present invention is a system that utilizes AI to streamline the management of lost and found items. This lost and found management system can automate and streamline processes such as classification, searching, matching, and return of lost and found items. For example, the lost and found management system classifies and digitizes lost and found items. It automatically classifies lost and found items using image recognition technology and registers detailed information. This eliminates the need for manual classification work and can significantly reduce working hours. Next, the lost and found management system streamlines the searching and matching process. It uses natural language processing technology to quickly search and perform feature matching based on feature information entered by the user. For example, in response to the input "I lost a blue backpack near the station," it can quickly suggest the relevant item. This improves the accuracy of matching with the person who lost their item and increases owner satisfaction. Furthermore, the lost and found management system enhances matching with the person who lost their item. It uses a notification function and a 2D code (e.g., QR code®) to enable smooth matching with the person who lost their item. For example, if a lost item is found, it can quickly return the item to the person who lost it by sending a notification and presenting the 2D code. Furthermore, the lost and found management system will improve the return process. By introducing an automated evaluation and reservation system, it will achieve efficient returns. For example, by automatically evaluating the condition of lost items and making return reservations, the return process can be carried out smoothly. In addition, the lost and found management system will be integrated with location information. By using GPS data to track the location of lost items, the recovery rate will be improved. This will enable the rapid recovery of lost items. The lost and found management system is also being considered for use in public facilities and events. By installing AI-enabled kiosk terminals and performing immediate searches and organization, congestion in public facilities will be alleviated and a quick response will be achieved. The lost and found management system will analyze trends and timing of lost items through statistics and analysis and propose improvements. This will improve the efficiency of lost and found management. The lost and found management system will also provide FAQs. User support will be provided in a chat format to quickly respond to user questions. Finally, the lost and found management system will introduce an anonymization function. It will match lost items with the person who lost them while protecting the privacy of the information.This enables efficient lost and found management while protecting user privacy. As a result, the lost and found management system can streamline the management of lost items and improve the accuracy of matching them with the person who lost them.

[0029] The lost property management system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a return unit, and a tracking unit. The collection unit collects images of lost property. The collection unit can collect, for example, photographs, videos, or scanned images of lost property. For example, the collection unit can take a photograph of lost property and save it as digital data. The collection unit can also take a video of lost property and record detailed information. Furthermore, the collection unit can acquire scanned images of lost property and save them as digital data. The analysis unit analyzes the images collected by the collection unit and extracts features. For example, the analysis unit can extract features such as shape, color, and size. For example, the analysis unit can use image recognition technology to analyze the shape of lost property and extract features. The analysis unit can also use color recognition technology to analyze the color of lost property and extract features. Furthermore, the analysis unit can use size recognition technology to analyze the size of lost property and extract features. The matching unit matches the lost property with the person who lost it based on the feature information extracted by the analysis unit. The matching unit can perform matching using, for example, a similarity score. The matching unit can calculate a similarity score based on feature information and perform matching with the person who lost their item. The matching unit can also perform matching using a matching algorithm. Furthermore, the matching unit can apply a matching algorithm based on feature information and perform matching with the person who lost their item. The return unit returns the lost item that has been matched by the matching unit. The return unit can return the lost item using, for example, a notification function and a QR code. The return unit can return the lost item by, for example, sending a notification to the person who lost their item and having them present a QR code. Furthermore, the return unit can return the lost item using methods such as mail or drone delivery. Furthermore, the return unit can return the lost item in person. The tracking unit tracks the location of the lost item that has been returned by the return unit. The tracking unit can track the location of the lost item using, for example, GPS data. The tracking unit can acquire location information of the lost item using, for example, a GPS module and perform tracking. Furthermore, the tracking unit can track the location of the lost item using Wi-Fi location information. Furthermore, the tracking unit can also use beacons to track the location of lost items.As a result, the lost property management system according to this embodiment can streamline the management of lost property and improve the accuracy of matching it with the person who lost it.

[0030] The collection unit collects images of lost items. For example, it can collect photographs, videos, and scanned images of lost items. Specifically, when a lost item is found, it uses a dedicated device or smartphone to take high-resolution photographs and saves them as digital data. This allows for accurate recording of the detailed appearance and characteristics of the lost item. Video recording also allows for recording the overall appearance of the lost item and its surroundings, enabling the collection of more information. Furthermore, it is possible to obtain detailed images of lost items using a scanner and save them as digital data. This allows for accurate recording of lost items in paper format and items with intricate characteristics. The collection unit centrally manages this data and stores it on a cloud server, enabling quick access by the analysis and matching units. The collection unit can also adjust the data collection frequency and resolution, allowing for flexible responses to specific situations and conditions. For example, lost items found in public places or on public transportation require rapid data collection and immediate registration in the system. This allows the collection unit to efficiently and effectively collect data, improving the overall performance of the lost item management system.

[0031] The analysis unit analyzes images collected by the collection unit and extracts features. The analysis unit can extract features such as shape, color, and size. Specifically, it can analyze the shape of lost items and extract features using image recognition technology. For example, it can automatically identify the shape of lost items and extract features using an image classification model based on deep learning. It can also analyze the color of lost items and extract features using color recognition technology. For example, by performing color space transformation and emphasizing specific color components, the color of lost items can be accurately identified. Furthermore, it can analyze the size of lost items and extract features using size recognition technology. For example, by calculating the number of pixels of an object in an image and converting it to its actual size, the size of lost items can be accurately determined. The analysis unit stores this feature information in a database, allowing the matching unit to efficiently search for it. The analysis unit can also evaluate the quality of the collected data and provide feedback to the collection unit to supplement missing or inaccurate information. This allows the analysis unit to quickly and accurately analyze the collected data and gain a detailed understanding of the characteristics of lost items.

[0032] The matching unit performs matching with the person who lost the item based on the feature information extracted by the analysis unit. The matching unit can perform matching using, for example, a similarity score. Specifically, it calculates a similarity score based on the feature information and performs matching with the person who lost the item. For example, by vectorizing feature information such as shape, color, and size and calculating cosine similarity or Euclidean distance, the similarity between the information of the lost item and the person who lost it can be evaluated. It can also perform matching using a matching algorithm. For example, by using a machine learning algorithm, past matching data can be learned and new information of the lost item and the person who lost it can be efficiently matched. Furthermore, the matching unit can also apply a matching algorithm based on the feature information to perform matching with the person who lost the item. For example, a neural network model can be constructed that takes the feature information of the lost item as input and outputs the information of the person who lost it, thereby achieving highly accurate matching. By utilizing these technologies, the matching unit can quickly and accurately match the information of the lost item and the person who lost it and hand it over to the return unit.

[0033] The return department returns lost items that have been matched by the matching department. The return department can return lost items using, for example, a notification function and a QR code. Specifically, it sends a notification to the person who lost the item and returns the item when they present the QR code. For example, by sending a notification to the person's smartphone and having them display the QR code, the retrieval of the lost item can be made smoother. The return department can also return lost items using methods such as mail or drone delivery. For example, by mailing the lost item to the person's address, it can be returned quickly even to people who have lost their items in remote locations. Furthermore, the return department can also return lost items in person. For example, by handing the lost item directly to the person at a designated location such as a lost and found center or police station, the return can be made more secure. By combining these methods, the return department can provide the person who lost their item with the most suitable return method and efficiently return lost items.

[0034] The tracking unit tracks the location of lost items returned by the return unit. The tracking unit can, for example, track the location of lost items using GPS data. Specifically, it can acquire location information of lost items using a GPS module and track them. For example, it can acquire location information from a GPS device attached to the lost item and track it in real time. The tracking unit can also track the location of lost items using Wi-Fi location information. For example, it can use the location information of a Wi-Fi access point to determine the approximate location of the lost item. Furthermore, the tracking unit can also track the location of lost items using beacons. For example, it can receive signals emitted from a beacon attached to the lost item and acquire location information to determine the location of the lost item. By utilizing these technologies, the tracking unit can accurately track the location of returned lost items and provide peace of mind to the person who lost their item. Furthermore, the tracking unit can store the location information in a database so that the person who lost their item can check the location of their item at any time. This allows the tracking unit to accurately grasp the location of returned lost items and provide peace of mind to the person who lost their item.

[0035] The collection unit can collect images of lost items and register detailed information. For example, the collection unit can take photographs of lost items and save them as digital data. Then, the collection unit can register detailed information about the lost items. For example, it can register information such as a description of the lost item, the place where it was found, and the date and time it was found. The collection unit can also shoot videos of lost items and record detailed information. For example, it can shoot videos and record the characteristics and condition of the lost items in detail. Furthermore, the collection unit can acquire scanned images of lost items and save them as digital data. For example, it can acquire scanned images and record the shape and size of the lost items in detail. This makes management easier by registering detailed information about lost items. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can take photographs of lost items and automatically register detailed information using AI.

[0036] The analysis unit can analyze the collected images and extract features. For example, the analysis unit can extract features such as shape, color, and size. For example, the analysis unit can use image recognition technology to analyze the shape of the lost item and extract its features. The analysis unit can also use color recognition technology to analyze the color of the lost item and extract its features. Furthermore, the analysis unit can use size recognition technology to analyze the size of the lost item and extract its features. This allows for accurate extraction of the features of the lost item through image analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected images into AI and use AI to extract features.

[0037] The matching unit can perform matching with the person who lost their belongings based on characteristic information. The matching unit can perform matching using, for example, a similarity score. The matching unit can calculate a similarity score based on the characteristic information and perform matching with the person who lost their belongings. The matching unit can also perform matching using a matching algorithm. Furthermore, the matching unit can apply a matching algorithm based on the characteristic information and perform matching with the person who lost their belongings. This improves the accuracy of matching with the person who lost their belongings by using matching based on characteristic information. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input characteristic information into AI and perform matching using AI.

[0038] The return unit can return lost items using a notification function and a QR code. For example, the return unit can return the lost item by sending a notification to the person who lost it and presenting the QR code. The return unit can send notifications to the person who lost it using methods such as email notifications, SMS notifications, and app notifications. The return unit can also return the lost item by generating a QR code and presenting it to the person who lost it. Furthermore, the return unit can return lost items using methods such as postal mail or drone delivery. For example, the return unit can send the lost item to the person who lost it by mail. The return unit can also deliver the lost item to the person who lost it using a drone. This enables the rapid return of lost items by using a notification function and a QR code. Some or all of the above processes in the return unit may be performed using AI, for example, or not using AI. For example, the return unit can automate the notification function using AI and generate the QR code using AI.

[0039] The tracking unit can track the location of lost items using GPS data. For example, the tracking unit can acquire location information of lost items using a GPS module and perform tracking. For example, the tracking unit can acquire GPS data in real time and track the location of lost items. The tracking unit can also track the location of lost items using Wi-Fi location information. For example, it can use a Wi-Fi network to identify and track the location of lost items. Furthermore, the tracking unit can also track the location of lost items using beacons. For example, it can use beacon signals to identify and track the location of lost items. This allows for accurate tracking of the location of lost items using GPS data. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input GPS data into AI, use AI to analyze the location information, and perform tracking.

[0040] The collection unit can install AI-enabled kiosk terminals, taking into consideration their use in public facilities and events. For example, the collection unit can install AI-enabled kiosk terminals in public facilities and event venues such as train stations, airports, and concert halls. The collection unit can install kiosk terminals equipped with image recognition capabilities to collect images of lost items. The collection unit can also install kiosk terminals with touch panel operation capabilities, allowing users to input information about lost items. Furthermore, the collection unit can install AI-enabled kiosk terminals to perform immediate search and organization. For example, the collection unit can collect images of lost items using kiosk terminals and automatically classify them using AI. This makes the management of lost items more efficient when used in public facilities and events. Some or all of the above-described processes in the collection unit may be performed using AI, or not. For example, the collection unit can control kiosk terminals with AI to automate the collection and classification of images of lost items.

[0041] The analysis unit can analyze the trends and timing of lost items and propose improvements. For example, the analysis unit can analyze seasonal trends in lost items. For example, the analysis unit can identify seasons or times when lost items occur frequently and propose improvement measures. The analysis unit can also analyze trends by time of day. For example, it can identify time of day when lost items occur frequently and propose improvement measures. Furthermore, the analysis unit can analyze trends by type of lost item. For example, it can identify trends in specific types of lost items and propose improvement measures. In this way, management can be improved by analyzing the trends and timing of lost items. 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 input lost item data into AI, use AI to analyze trends and timing, and propose improvement measures.

[0042] The matching unit can provide user support in chat format. For example, the matching unit can provide user support using text chat. For example, the matching unit can provide answers via text chat to questions entered by the user. The matching unit can also provide user support using voice chat. For example, the user enters a question by voice, and the matching unit provides an answer by voice. Furthermore, the matching unit can also provide user support using video chat. For example, the user enters a question via video, and the matching unit provides an answer via video. This allows for a quick response to user inquiries through chat-based user support. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's question into AI, use AI to generate an answer, and provide it in chat format.

[0043] The return unit can match lost items with their owners while protecting the privacy of the information. The return unit can protect the privacy of the information, for example, by using data encryption technology. For example, the return unit can encrypt the information of lost items to protect privacy. The return unit can also protect the privacy of the information, for example, by restricting which users can access the information of lost items to protect privacy. Furthermore, the return unit can also protect the privacy of the information, for example, by anonymizing the information of lost items to protect privacy. This enables efficient matching while protecting the privacy of the information. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the information of lost items into AI and use AI to encrypt or anonymize it to protect privacy.

[0044] The collection unit can analyze the user's past lost item submission history and select the optimal collection method when collecting images of lost items. For example, the collection unit can suggest the optimal collection method based on the types of lost items the user has submitted in the past. The collection unit can also analyze the user's past submission history and perform collection at a specific time. For example, it can suggest the optimal collection time based on the time periods when the user previously submitted lost items. Furthermore, the collection unit can analyze the user's past submission history and select the most efficient collection method. For example, it can suggest the optimal collection method based on the methods used to submit lost items the user previously submitted. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past submission history data into AI and use AI to select the optimal collection method.

[0045] The collection unit can filter images of lost items based on the user's current location and areas of interest. For example, the collection unit can prioritize collecting lost items that are close to the user's current location. For example, it can use GPS data to identify the user's current location and prioritize collecting nearby lost items. The collection unit can also prioritize collecting lost items related to the user's areas of interest. For example, it can filter and prioritize collecting related lost items based on the user's areas of interest. Furthermore, the collection unit can suggest optimal collection points based on the user's current location. For example, it can identify and suggest optimal collection points based on the user's current location. This enables efficient image collection by filtering based on the current location and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's location information and areas of interest data into AI, use AI to filter, and suggest the optimal collection method.

[0046] The collection unit can prioritize the collection of highly relevant lost items by considering the user's geographical location information when collecting images of lost items. For example, the collection unit can prioritize the collection of lost items located close to the user's current location. For example, the collection unit can use GPS data to identify the user's current location and prioritize the collection of nearby lost items. The collection unit can also prioritize the collection of highly relevant lost items based on the user's geographical location information. For example, it can identify and prioritize the collection of highly relevant lost items based on the user's current location. Furthermore, the collection unit can suggest the optimal collection point by considering the user's geographical location information. For example, it can identify and suggest the optimal collection point based on the user's current location. This allows for the priority collection of highly relevant lost items by considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into AI, use AI to identify highly relevant lost items, and prioritize their collection.

[0047] The collection unit can analyze the user's social media activity when collecting images of lost items and collect related lost items. For example, the collection unit can prioritize the collection of related lost items based on the user's social media activity. For example, the collection unit can analyze the user's social media posts to identify related lost items. The collection unit can also analyze the user's social media activity and suggest the optimal collection method. For example, it can analyze the user's follower information to identify and collect related lost items. Furthermore, the collection unit can collect related lost items based on the user's social media activity. For example, it can analyze the user's past posts to identify and collect related lost items. This allows for the efficient collection of related lost items by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into AI and use AI to identify and collect related lost items.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the lost item during image analysis. For example, the analysis unit performs a detailed analysis in the case of an important lost item. For example, the analysis unit evaluates the importance based on the value and rarity of the lost item and performs a detailed analysis. The analysis unit can also perform a standard analysis in the case of an ordinary lost item. For example, if the importance of the lost item is moderate, a standard analysis is performed. Furthermore, the analysis unit can perform a simplified analysis in the case of a lost item of low importance. For example, if the importance of the lost item is low, a simplified analysis is performed. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the lost item. 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 input the importance data of the lost item into AI, use AI to evaluate the importance, and adjust the level of detail of the analysis.

[0049] The analysis unit can apply different analysis algorithms depending on the category of the lost item during image analysis. For example, in the case of electronic devices, the analysis unit applies a specific analysis algorithm. For example, the analysis unit applies a specific analysis algorithm based on the characteristics of the electronic device. The analysis unit can also apply a different analysis algorithm in the case of clothing. For example, it applies a different analysis algorithm based on the characteristics of the clothing. Furthermore, the analysis unit can apply yet another analysis algorithm in the case of valuables. For example, it applies yet another analysis algorithm based on the characteristics of the valuables. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of the lost item. 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 input the category data of the lost item into AI and use AI to apply an appropriate analysis algorithm.

[0050] The analysis unit can determine the priority of analysis based on the submission date of the lost item during image analysis. For example, the analysis unit may prioritize the analysis of recently submitted lost items. The analysis unit may also determine the priority based on the submission date and time of the lost item. Furthermore, the analysis unit may postpone the analysis of older lost items. For example, older lost items may be given a lower priority. In addition, the analysis unit may adjust the priority of analysis based on the submission date. For example, it may adjust the order of analysis according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the data on the submission date of the lost item into AI, use AI to determine the priority, and perform the analysis.

[0051] The analysis unit can adjust the order of analysis based on the relevance of lost items during image analysis. For example, the analysis unit prioritizes the analysis of highly relevant lost items. For example, the analysis unit evaluates relevance and determines priority based on the similarity of the contents of the lost items or related keywords. The analysis unit can also postpone the analysis of less relevant lost items. For example, less relevant lost items are given a lower priority. Furthermore, the analysis unit can adjust the order of analysis based on relevance. For example, it adjusts the order of analysis according to relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 input the relevance data of lost items into AI, use AI to adjust the order, and perform the analysis.

[0052] The matching unit can improve the accuracy of matching by considering the interrelationships between lost items during the matching process. For example, the matching unit can associate and match lost items found in the same location. For example, the matching unit can evaluate interrelationships and perform matching based on the location and time of discovery of the lost items. The matching unit can also associate and match lost items submitted around the same time. For example, it can evaluate interrelationships and perform matching based on the submission time of the lost items. Furthermore, the matching unit can improve the accuracy of matching by considering the interrelationships between lost items. For example, it can evaluate interrelationships and perform matching based on common characteristics of the lost items. This improves the accuracy of matching by considering the interrelationships between lost items. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of lost items into AI, use AI to evaluate the interrelationships, and perform matching.

[0053] The matching unit can perform matching by considering the attribute information of the person who submitted the lost item. For example, the matching unit can perform matching by considering the submitter's age and gender. For example, the matching unit can evaluate attribute information based on the submitter's age and gender and perform matching. The matching unit can also perform matching by considering the submitter's occupation and hobbies. For example, it can evaluate attribute information based on the submitter's occupation and hobbies and perform matching. Furthermore, the matching unit can perform optimal matching based on the submitter's attribute information. For example, it can perform optimal matching based on the submitter's attribute information. This makes optimal matching possible by considering the submitter's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the submitter's attribute information data into AI, use AI to evaluate the attribute information, and perform matching.

[0054] The matching unit can perform matching while considering the geographical distribution of lost items. For example, the matching unit can associate and match lost items found in the same area. For example, the matching unit can evaluate the geographical distribution and perform matching based on the location where the lost items were found and the geographical distance. The matching unit can also associate and match lost items found in geographically close locations. For example, it can evaluate the geographical distribution and perform matching based on the location where the lost items were found. Furthermore, the matching unit can perform optimal matching by considering the geographical distribution of lost items. For example, it can perform optimal matching based on the geographical distribution of lost items. This makes optimal matching possible by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of lost items into AI, use AI to evaluate the geographical distribution, and perform matching.

[0055] The matching unit can improve the accuracy of matching by referring to relevant literature on lost items during the matching process. For example, the matching unit performs matching by referring to literature related to lost items. For example, the matching unit improves the accuracy of matching by referring to relevant literature based on the characteristics of lost items. The matching unit can also perform optimal matching based on the relevant literature on lost items. For example, it performs optimal matching by referring to relevant literature based on the characteristics of lost items. This improves the accuracy of matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input data on relevant literature on lost items into AI, use AI to refer to relevant literature, and perform matching.

[0056] The return unit can automatically evaluate the condition of lost items and determine the return priority upon return. For example, if the lost item is in good condition, the return unit will prioritize its return. The return unit evaluates the condition and determines the priority based on factors such as whether the lost item is damaged and whether it is usable. The return unit can also postpone the return of lost items in poor condition. For example, if the lost item is in poor condition, it will be given a lower priority. Furthermore, the return unit can automatically evaluate the condition of lost items and suggest the optimal return method. For example, it can suggest the optimal return method based on the condition of the lost item. This enables efficient returns by automatically evaluating the condition of lost items. Some or all of the above processes in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input lost item condition data into AI, use AI to evaluate the condition, and determine the return priority.

[0057] The return unit can select the optimal return method by referring to the user's past return history when an item is returned. For example, the return unit can suggest the optimal return method based on the return methods the user has used in the past. For example, the return unit can analyze the user's past return history and select the fastest return method. The return unit can also analyze the user's past return history and select the most efficient return method. For example, it can suggest the optimal return method based on the user's past return history. This allows the optimal return method to be selected by referring to past return history. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the user's past return history data into AI and use AI to select the optimal return method.

[0058] The return unit can select the optimal return method when returning lost items, taking into account the geographical location information of the lost items. For example, the return unit may suggest a return method close to the user's current location. For example, the return unit may select the optimal return method based on the geographical location information of the lost items. The return unit can also suggest the optimal return point, taking into account the user's geographical location information. For example, it may identify and suggest the optimal return point based on the user's current location. This allows the optimal return method to be selected by considering geographical location information. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the geographical location data of the lost items into AI and use AI to select the optimal return method.

[0059] The return unit can analyze the user's social media activity and propose a return method at the time of return. For example, the return unit can propose the optimal return method based on the user's social media activity. For example, the return unit can analyze the user's social media posts to identify the optimal return method. The return unit can also analyze the user's social media activity and select a quick return method. For example, it can analyze the user's follower information to identify and propose the optimal return method. Furthermore, the return unit can propose the optimal return method based on the user's social media activity. For example, it can analyze the user's past posts to identify and propose the optimal return method. In this way, the optimal return method can be proposed by analyzing social media activity. Some or all of the above processing in the return unit may be performed using AI, for example, or not using AI. For example, the return unit can input the user's social media activity data into AI and use AI to identify and propose the optimal return method.

[0060] The tracking unit can automatically update the location information of the lost item during tracking to improve tracking accuracy. For example, the tracking unit can update the location information of the lost item in real time to improve tracking accuracy. For example, the tracking unit can acquire and update the location information of the lost item in real time using GPS data. The tracking unit can also periodically update the location information of the lost item and suggest the optimal tracking method. For example, it can periodically acquire and update the location information of the lost item. Furthermore, the tracking unit can automatically update the location information of the lost item to perform rapid tracking. For example, it can automatically acquire and update the location information of the lost item. This improves tracking accuracy by automatically updating the location information. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information data of the lost item into AI, use AI to update the location information, and perform tracking.

[0061] The tracking unit can select the optimal tracking method by referring to the user's past tracking history during tracking. For example, the tracking unit can suggest the optimal tracking method based on the tracking methods the user has used in the past. For example, the tracking unit can analyze the user's past tracking history and select a fast tracking method. The tracking unit can also analyze the user's past tracking history and select the most efficient tracking method. For example, it can suggest the optimal tracking method based on the user's past tracking history. This allows the optimal tracking method to be selected by referring to past tracking history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past tracking history data into AI and use AI to select the optimal tracking method.

[0062] The tracking unit can select the optimal tracking method during tracking, taking into account the geographical location information of the lost item. For example, the tracking unit can propose the optimal tracking method based on the geographical location information of the lost item. For example, the tracking unit can acquire the geographical location information of the lost item using GPS data and select the optimal tracking method. The tracking unit can also propose a tracking method close to the user's current location. For example, it can identify and propose the optimal tracking method based on the user's current location. Furthermore, the tracking unit can propose the optimal tracking points, taking into account the geographical location information of the lost item. For example, it can identify and propose the optimal tracking points based on the geographical location information of the lost item. This allows for the selection of the optimal tracking method by considering geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the geographical location information data of the lost item into AI and use AI to select the optimal tracking method.

[0063] The tracking unit can analyze the user's social media activity during tracking and propose tracking methods. For example, the tracking unit can propose the optimal tracking method based on the user's social media activity. For example, the tracking unit can analyze the user's social media posts to identify the optimal tracking method. The tracking unit can also analyze the user's social media activity and select a rapid tracking method. For example, it can analyze the user's follower information to identify and propose the optimal tracking method. Furthermore, the tracking unit can propose the optimal tracking method based on the user's social media activity. For example, it can analyze the user's past posts to identify and propose the optimal tracking method. In this way, the optimal tracking method can be proposed by analyzing social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into AI and use AI to identify and propose the optimal tracking method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The collection unit can analyze the user's past lost item submission history to select the optimal collection method when collecting images of lost items. For example, it can suggest the optimal collection method based on the types of lost items the user has submitted in the past. The collection unit can also analyze the user's past submission history to perform collection at specific time periods. For example, it can suggest the optimal collection time based on the time periods when the user has submitted lost items in the past. Furthermore, the collection unit can analyze the user's past submission history to select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing past submission history.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the lost item when analyzing collected images and extracting features. For example, in the case of important lost items, a detailed analysis can be performed. The analysis unit can evaluate the importance of the lost item based on its value and rarity and perform a detailed analysis. In addition, a standard analysis can be performed for ordinary lost items. Furthermore, a simplified analysis can be performed for lost items of low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the lost item.

[0067] The matching unit can improve the accuracy of matching lost items by considering the interrelationships between the lost items when matching them with the person who found the item based on characteristic information. For example, it can match lost items found in the same location by associating them. The matching unit can evaluate the interrelationships based on the location and time of discovery of the lost items and perform matching. It can also match lost items submitted around the same time by associating them. In this way, the accuracy of matching is improved by considering the interrelationships between the lost items.

[0068] The return unit can select the optimal return method when returning lost items using a notification function and QR code, by referring to the user's past return history. For example, it can suggest the optimal return method based on the return methods the user has used in the past. The return unit can analyze the user's past return history and select the quickest return method. It can also analyze the user's past return history and select the most efficient return method. In this way, the optimal return method can be selected by referring to past return history.

[0069] The tracking unit can automatically update the location information of lost items using GPS data, thereby improving tracking accuracy. For example, it can update the location information of lost items in real time to improve tracking accuracy. The tracking unit can acquire and update the location information of lost items in real time using GPS data. It can also periodically update the location information of lost items and suggest the optimal tracking method. As a result, tracking accuracy is improved by automatically updating the location information.

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

[0071] Step 1: The collection unit collects images of the lost item. The collection unit can collect, for example, photographs, videos, and scanned images of the lost item. The collection unit takes photographs of the lost item and saves them as digital data. The collection unit can also take videos of the lost item and record detailed information. Furthermore, the collection unit can acquire scanned images of the lost item and save them as digital data. Step 2: The analysis unit analyzes the images collected by the collection unit and extracts features. The analysis unit can extract features such as shape, color, and size. The analysis unit uses image recognition technology to analyze the shape of the lost item and extract features. It can also use color recognition technology to analyze the color of the lost item and extract features. Furthermore, it can use size recognition technology to analyze the size of the lost item and extract features. Step 3: The matching unit performs matching with the lost person based on the feature information extracted by the analysis unit. The matching unit can, for example, perform matching using a similarity score. The matching unit calculates a similarity score based on the feature information and performs matching with the lost person. It can also perform matching using a matching algorithm. Step 4: The return unit returns the lost item that has been matched by the matching unit. The return unit can return the lost item using, for example, a notification function and a 2D code (e.g., a QR code). The return unit sends a notification to the person who lost the item and returns the item when they present the 2D code. It can also return the lost item using methods such as mail or drone delivery. Furthermore, it can return the lost item in person. Step 5: The tracking unit tracks the location of the lost item returned by the return unit. The tracking unit can, for example, track the location of the lost item using GPS data. The tracking unit obtains location information of the lost item using a GPS module and performs tracking. It can also track the location of the lost item using Wi-Fi location information. Furthermore, it can also track the location of the lost item using beacons.

[0072] (Example of form 2) The lost and found management system according to an embodiment of the present invention is a system that utilizes AI to streamline the management of lost items. This lost and found management system can automate and streamline processes such as classification, searching, matching, and return of lost items. For example, the lost and found management system classifies and digitizes lost items. It automatically classifies lost items using image recognition technology and registers detailed information. This eliminates the need for manual classification work and can significantly reduce working hours. Next, the lost and found management system streamlines searching and matching. It uses natural language processing technology to quickly search and perform feature matching based on feature information entered by the user. For example, in response to the input "I lost a blue backpack near the station," it can quickly suggest the corresponding item. This improves the accuracy of matching with the person who lost their item and increases owner satisfaction. Furthermore, the lost and found management system enhances matching with the person who lost their item. It uses a notification function and a 2D code (e.g., a QR code) to achieve smooth matching with the person who lost their item. For example, if a lost item is found, a notification is sent to the person who lost it, and by presenting the 2D code, the lost item can be returned quickly. Furthermore, the lost and found management system will improve the return process. By introducing an automated evaluation and reservation system, it will achieve efficient returns. For example, by automatically evaluating the condition of lost items and making return reservations, the return process can be carried out smoothly. In addition, the lost and found management system will be integrated with location information. By using GPS data to track the location of lost items, the recovery rate will be improved. This will enable the rapid recovery of lost items. The lost and found management system is also being considered for use in public facilities and events. By installing AI-enabled kiosk terminals and performing immediate searches and organization, congestion in public facilities will be alleviated and a quick response will be achieved. The lost and found management system will analyze trends and timing of lost items through statistics and analysis and propose improvements. This will improve the efficiency of lost and found management. The lost and found management system will also provide FAQs. User support will be provided in a chat format to quickly respond to user questions. Finally, the lost and found management system will introduce an anonymization function. It will match lost items with the person who lost them while protecting the privacy of the information.This enables efficient lost and found management while protecting user privacy. As a result, the lost and found management system can streamline the management of lost items and improve the accuracy of matching them with the person who lost them.

[0073] The lost property management system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, a return unit, and a tracking unit. The collection unit collects images of lost property. The collection unit can collect, for example, photographs, videos, or scanned images of lost property. For example, the collection unit can take a photograph of lost property and save it as digital data. The collection unit can also take a video of lost property and record detailed information. Furthermore, the collection unit can acquire scanned images of lost property and save them as digital data. The analysis unit analyzes the images collected by the collection unit and extracts features. For example, the analysis unit can extract features such as shape, color, and size. For example, the analysis unit can use image recognition technology to analyze the shape of lost property and extract features. The analysis unit can also use color recognition technology to analyze the color of lost property and extract features. Furthermore, the analysis unit can use size recognition technology to analyze the size of lost property and extract features. The matching unit matches the lost property with the person who lost it based on the feature information extracted by the analysis unit. The matching unit can perform matching using, for example, a similarity score. The matching unit can calculate a similarity score based on feature information and perform matching with the person who lost their item. The matching unit can also perform matching using a matching algorithm. Furthermore, the matching unit can apply a matching algorithm based on feature information and perform matching with the person who lost their item. The return unit returns the lost item that has been matched by the matching unit. The return unit can return the lost item using, for example, a notification function and a QR code. The return unit can return the lost item by, for example, sending a notification to the person who lost their item and having them present a QR code. Furthermore, the return unit can return the lost item using methods such as mail or drone delivery. Furthermore, the return unit can return the lost item in person. The tracking unit tracks the location of the lost item that has been returned by the return unit. The tracking unit can track the location of the lost item using, for example, GPS data. The tracking unit can acquire location information of the lost item using, for example, a GPS module and perform tracking. Furthermore, the tracking unit can track the location of the lost item using Wi-Fi location information. Furthermore, the tracking unit can also use beacons to track the location of lost items.As a result, the lost property management system according to this embodiment can streamline the management of lost property and improve the accuracy of matching it with the person who lost it.

[0074] The collection unit collects images of lost items. For example, it can collect photographs, videos, and scanned images of lost items. Specifically, when a lost item is found, it uses a dedicated device or smartphone to take high-resolution photographs and saves them as digital data. This allows for accurate recording of the detailed appearance and characteristics of the lost item. Video recording also allows for recording the overall appearance of the lost item and its surroundings, enabling the collection of more information. Furthermore, it is possible to obtain detailed images of lost items using a scanner and save them as digital data. This allows for accurate recording of lost items in paper format and items with intricate characteristics. The collection unit centrally manages this data and stores it on a cloud server, enabling quick access by the analysis and matching units. The collection unit can also adjust the data collection frequency and resolution, allowing for flexible responses to specific situations and conditions. For example, lost items found in public places or on public transportation require rapid data collection and immediate registration in the system. This allows the collection unit to efficiently and effectively collect data, improving the overall performance of the lost item management system.

[0075] The analysis unit analyzes images collected by the collection unit and extracts features. The analysis unit can extract features such as shape, color, and size. Specifically, it can analyze the shape of lost items and extract features using image recognition technology. For example, it can automatically identify the shape of lost items and extract features using an image classification model based on deep learning. It can also analyze the color of lost items and extract features using color recognition technology. For example, by performing color space transformation and emphasizing specific color components, the color of lost items can be accurately identified. Furthermore, it can analyze the size of lost items and extract features using size recognition technology. For example, by calculating the number of pixels of an object in an image and converting it to its actual size, the size of lost items can be accurately determined. The analysis unit stores this feature information in a database, allowing the matching unit to efficiently search for it. The analysis unit can also evaluate the quality of the collected data and provide feedback to the collection unit to supplement missing or inaccurate information. This allows the analysis unit to quickly and accurately analyze the collected data and gain a detailed understanding of the characteristics of lost items.

[0076] The matching unit performs matching with the person who lost the item based on the feature information extracted by the analysis unit. The matching unit can perform matching using, for example, a similarity score. Specifically, it calculates a similarity score based on the feature information and performs matching with the person who lost the item. For example, by vectorizing feature information such as shape, color, and size and calculating cosine similarity or Euclidean distance, the similarity between the information of the lost item and the person who lost it can be evaluated. It can also perform matching using a matching algorithm. For example, by using a machine learning algorithm, past matching data can be learned and new information of the lost item and the person who lost it can be efficiently matched. Furthermore, the matching unit can also apply a matching algorithm based on the feature information to perform matching with the person who lost the item. For example, a neural network model can be constructed that takes the feature information of the lost item as input and outputs the information of the person who lost it, thereby achieving highly accurate matching. By utilizing these technologies, the matching unit can quickly and accurately match the information of the lost item and the person who lost it and hand it over to the return unit.

[0077] The return department returns lost items that have been matched by the matching department. The return department can return lost items using, for example, a notification function and a QR code. Specifically, it sends a notification to the person who lost the item and returns the item when they present the QR code. For example, by sending a notification to the person's smartphone and having them display the QR code, the retrieval of the lost item can be made smoother. The return department can also return lost items using methods such as mail or drone delivery. For example, by mailing the lost item to the person's address, it can be returned quickly even to people who have lost their items in remote locations. Furthermore, the return department can also return lost items in person. For example, by handing the lost item directly to the person at a designated location such as a lost and found center or police station, the return can be made more secure. By combining these methods, the return department can provide the person who lost their item with the most suitable return method and efficiently return lost items.

[0078] The tracking unit tracks the location of lost items returned by the return unit. The tracking unit can, for example, track the location of lost items using GPS data. Specifically, it can acquire location information of lost items using a GPS module and track them. For example, it can acquire location information from a GPS device attached to the lost item and track it in real time. The tracking unit can also track the location of lost items using Wi-Fi location information. For example, it can use the location information of a Wi-Fi access point to determine the approximate location of the lost item. Furthermore, the tracking unit can also track the location of lost items using beacons. For example, it can receive signals emitted from a beacon attached to the lost item and acquire location information to determine the location of the lost item. By utilizing these technologies, the tracking unit can accurately track the location of returned lost items and provide peace of mind to the person who lost their item. Furthermore, the tracking unit can store the location information in a database so that the person who lost their item can check the location of their item at any time. This allows the tracking unit to accurately grasp the location of returned lost items and provide peace of mind to the person who lost their item.

[0079] The collection unit can collect images of lost items and register detailed information. For example, the collection unit can take photographs of lost items and save them as digital data. Then, the collection unit can register detailed information about the lost items. For example, it can register information such as a description of the lost item, the place where it was found, and the date and time it was found. The collection unit can also shoot videos of lost items and record detailed information. For example, it can shoot videos and record the characteristics and condition of the lost items in detail. Furthermore, the collection unit can acquire scanned images of lost items and save them as digital data. For example, it can acquire scanned images and record the shape and size of the lost items in detail. This makes management easier by registering detailed information about lost items. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can take photographs of lost items and automatically register detailed information using AI.

[0080] The analysis unit can analyze the collected images and extract features. For example, the analysis unit can extract features such as shape, color, and size. For example, the analysis unit can use image recognition technology to analyze the shape of the lost item and extract its features. The analysis unit can also use color recognition technology to analyze the color of the lost item and extract its features. Furthermore, the analysis unit can use size recognition technology to analyze the size of the lost item and extract its features. This allows for accurate extraction of the features of the lost item through image analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected images into AI and use AI to extract features.

[0081] The matching unit can perform matching with the person who lost their belongings based on characteristic information. The matching unit can perform matching using, for example, a similarity score. The matching unit can calculate a similarity score based on the characteristic information and perform matching with the person who lost their belongings. The matching unit can also perform matching using a matching algorithm. Furthermore, the matching unit can apply a matching algorithm based on the characteristic information and perform matching with the person who lost their belongings. This improves the accuracy of matching with the person who lost their belongings by using matching based on characteristic information. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input characteristic information into AI and perform matching using AI.

[0082] The return unit can return lost items using a notification function and a QR code. For example, the return unit can return the lost item by sending a notification to the person who lost it and presenting the QR code. The return unit can send notifications to the person who lost it using methods such as email notifications, SMS notifications, and app notifications. The return unit can also return the lost item by generating a QR code and presenting it to the person who lost it. Furthermore, the return unit can return lost items using methods such as postal mail or drone delivery. For example, the return unit can send the lost item to the person who lost it by mail. The return unit can also deliver the lost item to the person who lost it using a drone. This enables the rapid return of lost items by using a notification function and a QR code. Some or all of the above processes in the return unit may be performed using AI, for example, or not using AI. For example, the return unit can automate the notification function using AI and generate the QR code using AI.

[0083] The tracking unit can track the location of lost items using GPS data. For example, the tracking unit can acquire location information of lost items using a GPS module and perform tracking. For example, the tracking unit can acquire GPS data in real time and track the location of lost items. The tracking unit can also track the location of lost items using Wi-Fi location information. For example, it can use a Wi-Fi network to identify and track the location of lost items. Furthermore, the tracking unit can also track the location of lost items using beacons. For example, it can use beacon signals to identify and track the location of lost items. This allows for accurate tracking of the location of lost items using GPS data. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input GPS data into AI, use AI to analyze the location information, and perform tracking.

[0084] The collection unit can install AI-enabled kiosk terminals, taking into consideration their use in public facilities and events. For example, the collection unit can install AI-enabled kiosk terminals in public facilities and event venues such as train stations, airports, and concert halls. The collection unit can install kiosk terminals equipped with image recognition capabilities to collect images of lost items. The collection unit can also install kiosk terminals with touch panel operation capabilities, allowing users to input information about lost items. Furthermore, the collection unit can install AI-enabled kiosk terminals to perform immediate search and organization. For example, the collection unit can collect images of lost items using kiosk terminals and automatically classify them using AI. This makes the management of lost items more efficient when used in public facilities and events. Some or all of the above-described processes in the collection unit may be performed using AI, or not. For example, the collection unit can control kiosk terminals with AI to automate the collection and classification of images of lost items.

[0085] The analysis unit can analyze the trends and timing of lost items and propose improvements. For example, the analysis unit can analyze seasonal trends in lost items. For example, the analysis unit can identify seasons or times when lost items occur frequently and propose improvement measures. The analysis unit can also analyze trends by time of day. For example, it can identify time of day when lost items occur frequently and propose improvement measures. Furthermore, the analysis unit can analyze trends by type of lost item. For example, it can identify trends in specific types of lost items and propose improvement measures. In this way, management can be improved by analyzing the trends and timing of lost items. 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 input lost item data into AI, use AI to analyze trends and timing, and propose improvement measures.

[0086] The matching unit can provide user support in chat format. For example, the matching unit can provide user support using text chat. For example, the matching unit can provide answers via text chat to questions entered by the user. The matching unit can also provide user support using voice chat. For example, the user enters a question by voice, and the matching unit provides an answer by voice. Furthermore, the matching unit can also provide user support using video chat. For example, the user enters a question via video, and the matching unit provides an answer via video. This allows for a quick response to user inquiries through chat-based user support. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's question into AI, use AI to generate an answer, and provide it in chat format.

[0087] The return unit can match lost items with their owners while protecting the privacy of the information. The return unit can protect the privacy of the information, for example, by using data encryption technology. For example, the return unit can encrypt the information of lost items to protect privacy. The return unit can also protect the privacy of the information, for example, by restricting which users can access the information of lost items to protect privacy. Furthermore, the return unit can also protect the privacy of the information, for example, by anonymizing the information of lost items to protect privacy. This enables efficient matching while protecting the privacy of the information. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the information of lost items into AI and use AI to encrypt or anonymize it to protect privacy.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of image collection of lost items based on the estimated emotions. For example, if the user is anxious, the data collection unit will quickly start collecting images. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the image collection timing accordingly. The data collection unit can also collect images at an appropriate time if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the data collection unit can provide reassurance by continuously notifying the user of the progress of image collection. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for efficient image collection by adjusting the image collection timing 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI, use AI to estimate emotions, and adjust the image collection timing.

[0089] The collection unit can analyze the user's past lost item submission history and select the optimal collection method when collecting images of lost items. For example, the collection unit can suggest the optimal collection method based on the types of lost items the user has submitted in the past. The collection unit can also analyze the user's past submission history and perform collection at a specific time. For example, it can suggest the optimal collection time based on the time periods when the user previously submitted lost items. Furthermore, the collection unit can analyze the user's past submission history and select the most efficient collection method. For example, it can suggest the optimal collection method based on the methods used to submit lost items the user previously submitted. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past submission history data into AI and use AI to select the optimal collection method.

[0090] The collection unit can filter images of lost items based on the user's current location and areas of interest. For example, the collection unit can prioritize collecting lost items that are close to the user's current location. For example, it can use GPS data to identify the user's current location and prioritize collecting nearby lost items. The collection unit can also prioritize collecting lost items related to the user's areas of interest. For example, it can filter and prioritize collecting related lost items based on the user's areas of interest. Furthermore, the collection unit can suggest optimal collection points based on the user's current location. For example, it can identify and suggest optimal collection points based on the user's current location. This enables efficient image collection by filtering based on the current location and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's location information and areas of interest data into AI, use AI to filter, and suggest the optimal collection method.

[0091] The collection unit can estimate the user's emotions and determine the priority of lost items to collect based on the estimated emotions. For example, if the user is anxious, the collection unit will prioritize collecting important lost items. For example, the collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and determine the priority of lost items to collect. The collection unit can also collect items with normal priority if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the collection unit can prioritize collecting important lost items to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the collection unit to prioritize important lost items by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into an AI, use the AI ​​to estimate emotions, and determine the priority of lost items to collect.

[0092] The collection unit can prioritize the collection of highly relevant lost items by considering the user's geographical location information when collecting images of lost items. For example, the collection unit can prioritize the collection of lost items located close to the user's current location. For example, the collection unit can use GPS data to identify the user's current location and prioritize the collection of nearby lost items. The collection unit can also prioritize the collection of highly relevant lost items based on the user's geographical location information. For example, it can identify and prioritize the collection of highly relevant lost items based on the user's current location. Furthermore, the collection unit can suggest the optimal collection point by considering the user's geographical location information. For example, it can identify and suggest the optimal collection point based on the user's current location. This allows for the priority collection of highly relevant lost items by considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into AI, use AI to identify highly relevant lost items, and prioritize their collection.

[0093] The collection unit can analyze the user's social media activity when collecting images of lost items and collect related lost items. For example, the collection unit can prioritize the collection of related lost items based on the user's social media activity. For example, the collection unit can analyze the user's social media posts to identify related lost items. The collection unit can also analyze the user's social media activity and suggest the optimal collection method. For example, it can analyze the user's follower information to identify and collect related lost items. Furthermore, the collection unit can collect related lost items based on the user's social media activity. For example, it can analyze the user's past posts to identify and collect related lost items. This allows for the efficient collection of related lost items by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into AI and use AI to identify and collect related lost items.

[0094] The analysis unit can estimate the user's emotions and adjust the image analysis representation based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the image analysis representation. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, it can record the user's voice and estimate emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the analysis unit can provide detailed analysis results to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows the system to provide appropriate analysis results by adjusting the image analysis representation 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, use AI to estimate emotions, and adjust the representation method of the image analysis.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the lost item during image analysis. For example, the analysis unit performs a detailed analysis in the case of an important lost item. For example, the analysis unit evaluates the importance based on the value and rarity of the lost item and performs a detailed analysis. The analysis unit can also perform a standard analysis in the case of an ordinary lost item. For example, if the importance of the lost item is moderate, a standard analysis is performed. Furthermore, the analysis unit can perform a simplified analysis in the case of a lost item of low importance. For example, if the importance of the lost item is low, a simplified analysis is performed. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the lost item. 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 input the importance data of the lost item into AI, use AI to evaluate the importance, and adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the category of the lost item during image analysis. For example, in the case of electronic devices, the analysis unit applies a specific analysis algorithm. For example, the analysis unit applies a specific analysis algorithm based on the characteristics of the electronic device. The analysis unit can also apply a different analysis algorithm in the case of clothing. For example, it applies a different analysis algorithm based on the characteristics of the clothing. Furthermore, the analysis unit can apply yet another analysis algorithm in the case of valuables. For example, it applies yet another analysis algorithm based on the characteristics of the valuables. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of the lost item. 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 input the category data of the lost item into AI and use AI to apply an appropriate analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the display method of the analysis results. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, it can record the user's voice and estimate emotions using voice analysis technology. In addition, if the user is in a hurry, the analysis unit can provide a concise display method. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows for the provision of appropriate information by adjusting the display method of the analysis results 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, use AI to estimate emotions, and adjust the display method of the analysis results.

[0098] The analysis unit can determine the priority of analysis based on the submission date of the lost item during image analysis. For example, the analysis unit may prioritize the analysis of recently submitted lost items. The analysis unit may also determine the priority based on the submission date and time of the lost item. Furthermore, the analysis unit may postpone the analysis of older lost items. For example, older lost items may be given a lower priority. In addition, the analysis unit may adjust the priority of analysis based on the submission date. For example, it may adjust the order of analysis according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the data on the submission date of the lost item into AI, use AI to determine the priority, and perform the analysis.

[0099] The analysis unit can adjust the order of analysis based on the relevance of lost items during image analysis. For example, the analysis unit prioritizes the analysis of highly relevant lost items. For example, the analysis unit evaluates relevance and determines priority based on the similarity of the contents of the lost items or related keywords. The analysis unit can also postpone the analysis of less relevant lost items. For example, less relevant lost items are given a lower priority. Furthermore, the analysis unit can adjust the order of analysis based on relevance. For example, it adjusts the order of analysis according to relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 input the relevance data of lost items into AI, use AI to adjust the order, and perform the analysis.

[0100] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is anxious, the matching unit will perform a quick match. For example, the matching unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the matching criteria. The matching unit can also perform a match using normal criteria if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the matching unit can adjust the matching criteria to provide a sense of security. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into AI, use AI to estimate emotions, and adjust the matching criteria.

[0101] The matching unit can improve the accuracy of matching by considering the interrelationships between lost items during the matching process. For example, the matching unit can associate and match lost items found in the same location. For example, the matching unit can evaluate interrelationships and perform matching based on the location and time of discovery of the lost items. The matching unit can also associate and match lost items submitted around the same time. For example, it can evaluate interrelationships and perform matching based on the submission time of the lost items. Furthermore, the matching unit can improve the accuracy of matching by considering the interrelationships between lost items. For example, it can evaluate interrelationships and perform matching based on common characteristics of the lost items. This improves the accuracy of matching by considering the interrelationships between lost items. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of lost items into AI, use AI to evaluate the interrelationships, and perform matching.

[0102] The matching unit can perform matching by considering the attribute information of the person who submitted the lost item. For example, the matching unit can perform matching by considering the submitter's age and gender. For example, the matching unit can evaluate attribute information based on the submitter's age and gender and perform matching. The matching unit can also perform matching by considering the submitter's occupation and hobbies. For example, it can evaluate attribute information based on the submitter's occupation and hobbies and perform matching. Furthermore, the matching unit can perform optimal matching based on the submitter's attribute information. For example, it can perform optimal matching based on the submitter's attribute information. This makes optimal matching possible by considering the submitter's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the submitter's attribute information data into AI, use AI to evaluate the attribute information, and perform matching.

[0103] The matching unit can estimate the user's emotions and adjust the display order of matching results based on the estimated emotions. For example, if the user is anxious, the matching unit will prioritize displaying important matching results. The matching unit can, for example, capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the display order. The matching unit can also display results in the normal order if the user is relaxed. For example, it can record the user's voice and estimate emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the matching unit can prioritize displaying important matching results to provide a sense of security. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows the system to provide appropriate matching results by adjusting the display order 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-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into AI, use AI to estimate emotions, and adjust the display order.

[0104] The matching unit can perform matching while considering the geographical distribution of lost items. For example, the matching unit can associate and match lost items found in the same area. For example, the matching unit can evaluate the geographical distribution and perform matching based on the location where the lost items were found and the geographical distance. The matching unit can also associate and match lost items found in geographically close locations. For example, it can evaluate the geographical distribution and perform matching based on the location where the lost items were found. Furthermore, the matching unit can perform optimal matching by considering the geographical distribution of lost items. For example, it can perform optimal matching based on the geographical distribution of lost items. This makes optimal matching possible by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of lost items into AI, use AI to evaluate the geographical distribution, and perform matching.

[0105] The matching unit can improve the accuracy of matching by referring to relevant literature on lost items during the matching process. For example, the matching unit performs matching by referring to literature related to lost items. For example, the matching unit improves the accuracy of matching by referring to relevant literature based on the characteristics of lost items. The matching unit can also perform optimal matching based on the relevant literature on lost items. For example, it performs optimal matching by referring to relevant literature based on the characteristics of lost items. This improves the accuracy of matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input data on relevant literature on lost items into AI, use AI to refer to relevant literature, and perform matching.

[0106] The return unit can estimate the user's emotions and adjust the return process based on those emotions. For example, if the user is anxious, the return unit will expedite the return process. For example, the return unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the return process accordingly. The return unit can also proceed with the normal return process if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the return unit can adjust the return process to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for appropriate returns by adjusting the return process 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 processing described above in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input user emotion data into AI, use AI to estimate emotions, and adjust the return process.

[0107] The return unit can automatically evaluate the condition of lost items and determine the return priority upon return. For example, if the lost item is in good condition, the return unit will prioritize its return. The return unit evaluates the condition and determines the priority based on factors such as whether the lost item is damaged and whether it is usable. The return unit can also postpone the return of lost items in poor condition. For example, if the lost item is in poor condition, it will be given a lower priority. Furthermore, the return unit can automatically evaluate the condition of lost items and suggest the optimal return method. For example, it can suggest the optimal return method based on the condition of the lost item. This enables efficient returns by automatically evaluating the condition of lost items. Some or all of the above processes in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input lost item condition data into AI, use AI to evaluate the condition, and determine the return priority.

[0108] The return unit can select the optimal return method by referring to the user's past return history when an item is returned. For example, the return unit can suggest the optimal return method based on the return methods the user has used in the past. For example, the return unit can analyze the user's past return history and select the fastest return method. The return unit can also analyze the user's past return history and select the most efficient return method. For example, it can suggest the optimal return method based on the user's past return history. This allows the optimal return method to be selected by referring to past return history. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the user's past return history data into AI and use AI to select the optimal return method.

[0109] The return unit can estimate the user's emotions and adjust the timing of the return notification based on the estimated emotions. For example, if the user is anxious, the return unit will send a return notification quickly. The return unit can, for example, capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression and adjust the timing of the return notification. The return unit can also send a return notification at the normal timing if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the return unit can adjust the timing of the return notification to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for appropriate return notifications by adjusting the notification timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input user emotion data into AI, use AI to estimate emotions, and adjust the timing of the return notification.

[0110] The return unit can select the optimal return method when returning lost items, taking into account the geographical location information of the lost items. For example, the return unit may suggest a return method close to the user's current location. For example, the return unit may select the optimal return method based on the geographical location information of the lost items. The return unit can also suggest the optimal return point, taking into account the user's geographical location information. For example, it may identify and suggest the optimal return point based on the user's current location. This allows the optimal return method to be selected by considering geographical location information. Some or all of the above processing in the return unit may be performed using AI, for example, or without AI. For example, the return unit can input the geographical location data of the lost items into AI and use AI to select the optimal return method.

[0111] The return unit can analyze the user's social media activity and propose a return method at the time of return. For example, the return unit can propose the optimal return method based on the user's social media activity. For example, the return unit can analyze the user's social media posts to identify the optimal return method. The return unit can also analyze the user's social media activity and select a quick return method. For example, it can analyze the user's follower information to identify and propose the optimal return method. Furthermore, the return unit can propose the optimal return method based on the user's social media activity. For example, it can analyze the user's past posts to identify and propose the optimal return method. In this way, the optimal return method can be proposed by analyzing social media activity. Some or all of the above processing in the return unit may be performed using AI, for example, or not using AI. For example, the return unit can input the user's social media activity data into AI and use AI to identify and propose the optimal return method.

[0112] The tracking unit can estimate the user's emotions and adjust the tracking process based on the estimated emotions. For example, if the user is anxious, the tracking unit will expedite the tracking process. For example, the tracking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the tracking process accordingly. The tracking unit can also proceed with the normal tracking process if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the tracking unit can adjust the tracking process to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for appropriate tracking by adjusting the tracking process 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-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into AI, use AI to estimate emotions, and adjust the tracking process.

[0113] The tracking unit can automatically update the location information of the lost item during tracking to improve tracking accuracy. For example, the tracking unit can update the location information of the lost item in real time to improve tracking accuracy. For example, the tracking unit can acquire and update the location information of the lost item in real time using GPS data. The tracking unit can also periodically update the location information of the lost item and suggest the optimal tracking method. For example, it can periodically acquire and update the location information of the lost item. Furthermore, the tracking unit can automatically update the location information of the lost item to perform rapid tracking. For example, it can automatically acquire and update the location information of the lost item. This improves tracking accuracy by automatically updating the location information. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information data of the lost item into AI, use AI to update the location information, and perform tracking.

[0114] The tracking unit can select the optimal tracking method by referring to the user's past tracking history during tracking. For example, the tracking unit can suggest the optimal tracking method based on the tracking methods the user has used in the past. For example, the tracking unit can analyze the user's past tracking history and select a fast tracking method. The tracking unit can also analyze the user's past tracking history and select the most efficient tracking method. For example, it can suggest the optimal tracking method based on the user's past tracking history. This allows the optimal tracking method to be selected by referring to past tracking history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past tracking history data into AI and use AI to select the optimal tracking method.

[0115] The tracking unit can estimate the user's emotions and adjust the timing of tracking notifications based on the estimated emotions. For example, if the user is anxious, the tracking unit will send a tracking notification quickly. For example, the tracking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the timing of tracking notifications. The tracking unit can also send tracking notifications at normal timings if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is feeling anxious, the tracking unit can adjust the timing of tracking notifications to provide reassurance. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for appropriate tracking notifications by adjusting the notification timing 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-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into AI, use AI to estimate emotions, and adjust the timing of tracking notifications.

[0116] The tracking unit can select the optimal tracking method during tracking, taking into account the geographical location information of the lost item. For example, the tracking unit can propose the optimal tracking method based on the geographical location information of the lost item. For example, the tracking unit can acquire the geographical location information of the lost item using GPS data and select the optimal tracking method. The tracking unit can also propose a tracking method close to the user's current location. For example, it can identify and propose the optimal tracking method based on the user's current location. Furthermore, the tracking unit can propose the optimal tracking points, taking into account the geographical location information of the lost item. For example, it can identify and propose the optimal tracking points based on the geographical location information of the lost item. This allows for the selection of the optimal tracking method by considering geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the geographical location information data of the lost item into AI and use AI to select the optimal tracking method.

[0117] The tracking unit can analyze the user's social media activity during tracking and propose tracking methods. For example, the tracking unit can propose the optimal tracking method based on the user's social media activity. For example, the tracking unit can analyze the user's social media posts to identify the optimal tracking method. The tracking unit can also analyze the user's social media activity and select a rapid tracking method. For example, it can analyze the user's follower information to identify and propose the optimal tracking method. Furthermore, the tracking unit can propose the optimal tracking method based on the user's social media activity. For example, it can analyze the user's past posts to identify and propose the optimal tracking method. In this way, the optimal tracking method can be proposed by analyzing social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into AI and use AI to identify and propose the optimal tracking method.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The collection unit can analyze the user's past lost item submission history to select the optimal collection method when collecting images of lost items. For example, it can suggest the optimal collection method based on the types of lost items the user has submitted in the past. The collection unit can also analyze the user's past submission history to perform collection at specific time periods. For example, it can suggest the optimal collection time based on the time periods when the user has submitted lost items in the past. Furthermore, the collection unit can analyze the user's past submission history to select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing past submission history.

[0120] The analysis unit can adjust the level of detail of the analysis based on the importance of the lost item when analyzing collected images and extracting features. For example, in the case of important lost items, a detailed analysis can be performed. The analysis unit can evaluate the importance of the lost item based on its value and rarity and perform a detailed analysis. In addition, a standard analysis can be performed for ordinary lost items. Furthermore, a simplified analysis can be performed for lost items of low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the lost item.

[0121] The matching unit can improve the accuracy of matching lost items by considering the interrelationships between the lost items when matching them with the person who found the item based on characteristic information. For example, it can match lost items found in the same location by associating them. The matching unit can evaluate the interrelationships based on the location and time of discovery of the lost items and perform matching. It can also match lost items submitted around the same time by associating them. In this way, the accuracy of matching is improved by considering the interrelationships between the lost items.

[0122] The return unit can select the optimal return method when returning lost items using a notification function and a QR code, by referring to the user's past return history. For example, it can suggest the optimal return method based on the return methods the user has used in the past. The return unit can analyze the user's past return history and select the quickest return method. It can also analyze the user's past return history and select the most efficient return method. In this way, the optimal return method can be selected by referring to past return history.

[0123] The tracking unit can automatically update the location information of lost items using GPS data, thereby improving tracking accuracy. For example, it can update the location information of lost items in real time to improve tracking accuracy. The tracking unit can acquire and update the location information of lost items in real time using GPS data. It can also periodically update the location information of lost items and suggest the optimal tracking method. As a result, tracking accuracy is improved by automatically updating the location information.

[0124] The collection unit can estimate the user's emotions and adjust the timing of image collection of lost items based on the estimated emotions. For example, if the user is anxious, image collection can be started quickly. The collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the image collection timing accordingly. Also, if the user is relaxed, image collection can be performed at an appropriate time. In this way, efficient image collection becomes possible by adjusting the image collection timing according to the user's emotions.

[0125] The analysis unit can estimate the user's emotions and adjust the image analysis representation based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. The analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the image analysis representation based on that. It can also provide concise analysis results if the user is in a hurry. In this way, by adjusting the image analysis representation according to the user's emotions, appropriate analysis results can be provided.

[0126] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is anxious, it can perform a quick match. The matching unit can also capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the matching criteria accordingly. If the user is relaxed, it can perform a match using the normal criteria. This allows for appropriate matching by adjusting the matching criteria according to the user's emotions.

[0127] The return unit can estimate the user's emotions and adjust the return process based on those emotions. For example, if the user is anxious, the return process can be expedited. The return unit can also capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the return process accordingly. If the user is relaxed, the normal return process can be carried out. This allows for appropriate returns by adjusting the return process according to the user's emotions.

[0128] The tracking unit can estimate the user's emotions and adjust the tracking process based on those emotions. For example, if the user is anxious, the tracking process can be accelerated. The tracking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the tracking process accordingly. If the user is relaxed, the normal tracking process can proceed. This allows for appropriate tracking by adjusting the tracking process according to the user's emotions.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The collection unit collects images of the lost item. The collection unit can collect, for example, photographs, videos, and scanned images of the lost item. The collection unit takes photographs of the lost item and saves them as digital data. The collection unit can also take videos of the lost item and record detailed information. Furthermore, the collection unit can acquire scanned images of the lost item and save them as digital data. Step 2: The analysis unit analyzes the images collected by the collection unit and extracts features. The analysis unit can extract features such as shape, color, and size. The analysis unit uses image recognition technology to analyze the shape of the lost item and extract features. It can also use color recognition technology to analyze the color of the lost item and extract features. Furthermore, it can use size recognition technology to analyze the size of the lost item and extract features. Step 3: The matching unit performs matching with the lost person based on the feature information extracted by the analysis unit. The matching unit can, for example, perform matching using a similarity score. The matching unit calculates a similarity score based on the feature information and performs matching with the lost person. It can also perform matching using a matching algorithm. Step 4: The return unit returns the lost item that has been matched by the matching unit. The return unit can return the lost item using, for example, a notification function and a 2D code (e.g., a QR code). The return unit sends a notification to the person who lost the item and returns the item when they present the 2D code. It can also return the lost item using methods such as mail or drone delivery. Furthermore, it can return the lost item in person. Step 5: The tracking unit tracks the location of the lost item returned by the return unit. The tracking unit can, for example, track the location of the lost item using GPS data. The tracking unit obtains location information of the lost item using a GPS module and performs tracking. It can also track the location of the lost item using Wi-Fi location information. Furthermore, it can also track the location of the lost item using beacons.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, return unit, and tracking unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to collect images of the lost item and stores them as digital data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected images to extract features. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches the lost item with the person who lost it based on the extracted feature information. The return unit is implemented by the control unit 46A of the smart device 14 and returns the lost item using a notification function and a two-dimensional code (e.g., a QR code). The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the location of the returned lost item using GPS data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, return unit, and tracking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to collect images of the lost item and stores them as digital data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected images to extract features. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches the lost item with the person who lost it based on the extracted feature information. The return unit is implemented by the control unit 46A of the smart glasses 214 and returns the lost item using a notification function and a two-dimensional code (e.g., a QR code). The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the location of the returned lost item using GPS data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, return unit, and tracking unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects images of the lost item using the camera 42 of the headset terminal 314 and stores them as digital data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected images to extract features. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches the lost item with the person who lost it based on the extracted feature information. The return unit is implemented by the control unit 46A of the headset terminal 314 and returns the lost item using a notification function and a two-dimensional code (e.g., a QR code). The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12 and tracks the location of the returned lost item using GPS data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, return unit, and tracking unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to collect images of the lost item and stores them as digital data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected images and extracts features. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, which matches the lost item with the person who lost it based on the extracted feature information. The return unit is implemented by the control unit 46A of the robot 414, which returns the lost item using a notification function and a two-dimensional code (e.g., a QR code). The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the location of the returned lost item using GPS data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The collection department collects images of lost items, An analysis unit analyzes the images collected by the aforementioned acquisition unit and extracts features, A matching unit that performs matching with the missing person based on the characteristic information extracted by the analysis unit, A return unit that returns the lost items matched by the matching unit, The system includes a tracking unit that tracks the location of lost items returned by the return unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect images of lost items and register detailed information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected images are analyzed to extract features. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching with the lost person based on their characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned return section is Lost items are returned using a notification function and a QR code. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is Track the location of lost items using GPS data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We will install AI-enabled kiosk terminals, taking into consideration their use in public facilities and events. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We will analyze the trends and timing of lost items and propose improvements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The matching unit is We provide user support in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned return section is Matching information with the person who lost their belongings while protecting their privacy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of image collection for lost items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting images of lost items, the system analyzes the user's past history of submitting lost items to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting images of lost items, filtering is performed based on the user's current location and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is It estimates the user's emotions and determines the priority of lost items to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting images of lost items, the system prioritizes collecting highly relevant items by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting images of lost items, the system analyzes the user's social media activity and collects related lost items. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the image analysis representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During image analysis, the level of detail of the analysis is adjusted based on the importance of the lost item. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During image analysis, different analysis algorithms are applied depending on the category of the lost item. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned analysis unit, During image analysis, the priority of the analysis is determined based on when the lost item was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During image analysis, the order of analysis is adjusted based on the relevance of the lost items. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, the accuracy of the matching is improved by considering the relationships between lost items. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, the attribute information of the person who submitted the lost item will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is It estimates the user's emotions and adjusts the display order of matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the geographical distribution of lost items is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, we refer to relevant literature on lost items to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned return section is It estimates the user's emotions and adjusts the return process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned return section is Upon return, the system automatically assesses the condition of the lost item and determines the priority for return. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned return section is When an item is returned, the system will refer to the user's past return history to select the most suitable return method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned return section is The system estimates the user's emotions and adjusts the timing of return notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned return section is When returning lost items, the most suitable return method will be selected, taking into account the geographical location of the lost items. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned return section is When a product is returned, the system analyzes the user's social media activity and suggests a return method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned tracking unit is During tracking, the system automatically updates the location information of lost items to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned tracking unit is During tracking, the system selects the optimal tracking method by referring to the user's past tracking history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned tracking unit is It estimates the user's emotions and adjusts the timing of tracking notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned tracking unit is During tracking, the most suitable tracking method is selected, taking into account the geographical location information of the lost item. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned tracking unit is During tracking, we analyze the user's social media activity and suggest tracking methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 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. The collection department collects images of lost items, An analysis unit analyzes the images collected by the aforementioned acquisition unit and extracts features, A matching unit that performs matching with the missing person based on the characteristic information extracted by the analysis unit, A return unit that returns the lost items matched by the matching unit, The system includes a tracking unit that tracks the location of lost items returned by the return unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect images of lost items and register detailed information. The system according to feature 1.

3. The aforementioned analysis unit, The collected images are analyzed to extract features. The system according to feature 1.

4. The matching unit is Matching with the lost person based on their characteristics. The system according to feature 1.

5. The aforementioned return section is Lost items are returned using a notification function and a QR code. The system according to feature 1.

6. The aforementioned tracking unit is Track the location of lost items using GPS data. The system according to feature 1.

7. The aforementioned collection unit is We will install AI-enabled kiosk terminals, taking into consideration their use in public facilities and events. The system according to feature 1.

8. The aforementioned analysis unit, We will analyze the trends and timing of lost items and propose improvements. The system according to feature 1.

Images