system

Abstract

The system according to this embodiment aims to automate the management of lost items and their return to their owners, enabling a rapid and efficient process. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reception unit, a matching unit, and a notification unit. The collection unit collects data on lost items using cameras and sensors. The analysis unit analyzes the data collected by the collection unit and registers it in a database. The reception unit receives the characteristics of the lost items entered by the owner through an online form or app. The matching unit matches the characteristics received by the reception unit with the data registered by the analysis unit using image recognition and data matching. The notification unit notifies the owner of the item identified by the matching unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the management of lost items and the return to the owner are performed manually, which takes time and effort.

[0005] The system according to the embodiment aims to automate the management of lost items and the return to the owner, and perform them quickly and efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a reception unit, a matching unit, and a notification unit. The collection unit collects data on lost items using cameras and sensors. The analysis unit analyzes the data collected by the collection unit and registers it in a database. The reception unit receives the characteristics of the lost items entered by the owner through an online form or app. The matching unit compares the characteristics received by the reception unit with the data registered by the analysis unit using image recognition and data matching. The notification unit notifies the owner of the item identified by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the management of lost items and their return to their owners, enabling it to be done quickly and efficiently. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The lost and found management system according to an embodiment of the present invention is a system that automates the lost and found management process at train stations and aims for the rapid reunification of owners with their lost items. This lost and found management system analyzes data on lost items collected by cameras and sensors and registers it in a database. When the owner enters the characteristics of the lost item through an online form or app, the system instantly identifies the correct item through image recognition and data matching and notifies the owner. In addition, an AI chatbot handles inquiries about lost items, reducing the burden on human resources and contributing to improved user satisfaction. For example, in the lost and found management system, cameras installed in the station's lost and found center capture images of lost items, and sensors detect their characteristics. This data is analyzed by AI and registered in a database. When the owner enters the characteristics of the lost item through an online form or app, that information is sent to the AI. The AI ​​compares the lost and found data in the database with the entered characteristics using image recognition and data matching technology to identify the correct item. The identified item is then notified to the owner. Upon receiving the notification through the app, the owner can go to the lost and found center to pick up their item. This enables the rapid reunification of owners with their lost items. Furthermore, an AI chatbot handles inquiries about lost items. When an owner asks the chatbot, "I want to know the status of my lost item," the chatbot searches the database and informs the owner of the status. This reduces the burden on human resources and improves user satisfaction. The lost and found management system delivers concrete benefits such as improved return rates for lost items, reduced inquiry response times, and increased customer satisfaction. It also features advanced image recognition technology and data matching algorithms, a user-friendly interface, and 24 / 7 customer support via an AI chatbot. This meets the needs of users who want to find lost items quickly and reliably, and also addresses the needs of operators seeking to reduce and streamline human resources. The lost and found management system delivers a creative solution that improves the user experience by innovating the lost and found management process through the combination of AI and IoT technologies. The target audience includes commuters and travelers of all ages, railway companies, transportation companies, and large-scale facility operators such as shopping malls.This system addresses the inefficiencies and customer dissatisfaction associated with the lost and found management and return process, enabling rapid and accurate identification and notification of lost items through an AI-powered lost and found management system. This allows the system to automate the entire process, from data collection to notification to the owner, facilitating quick reunification.

[0029] The lost and found management system according to this embodiment comprises a collection unit, an analysis unit, a reception unit, a matching unit, and a notification unit. The collection unit collects data on lost items using cameras and sensors. The collection unit can, for example, collect data on lost items using cameras and sensors installed at a lost and found center in a train station. The collection unit can, for example, capture images of lost items using surveillance cameras and detect features of lost items using infrared sensors. The collection unit can, for example, identify the shape and color of lost items by analyzing image data captured by cameras. The analysis unit analyzes the data collected by the collection unit and registers it in a database. The analysis unit can, for example, extract features of lost items using image analysis algorithms and register them in a database. The analysis unit can, for example, automate the procedure for registering collected data in a database. The analysis unit can, for example, classify collected data and register it in a database. The reception unit accepts features of lost items entered by the owner through online forms or apps. The reception unit can, for example, accept features of lost items entered by the owner in an online form. The reception unit can, for example, receive the characteristics of lost items entered by the owner through the app. The reception unit can, for example, register the characteristics of lost items entered by the owner in a database. The matching unit uses image recognition and data matching technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use deep learning-based image recognition technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use computer vision technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use similarity calculation to match the characteristics received by the reception unit with the data registered by the analysis unit. The notification unit notifies the owner of the item identified by the matching unit. The notification unit can, for example, notify the owner of the item identified using email notification. The notification unit can, for example, notify the owner of the item identified using push notification.The notification unit can, for example, notify the owner of an identified item using SMS notification. This allows the lost and found management system to automate the process from collecting data on lost items to notifying the owner, enabling rapid reconnection.

[0030] The collection unit collects data on lost items using cameras and sensors. For example, the collection unit can collect data on lost items using cameras and sensors installed in a lost and found center at a train station. Specifically, high-resolution cameras installed in the station premises, platforms, and ticket gates capture images of lost items. Multiple cameras are installed to cover a wide area, allowing for accurate identification of the location of lost items. In addition, by using infrared sensors and ultrasonic sensors in combination, the unit can detect the characteristics of lost items, such as their shape, material, and size, in detail. For example, infrared sensors measure the surface temperature of lost items and are useful for identifying materials such as metal or fabric. Ultrasonic sensors measure the distance and shape of lost items, allowing for the acquisition of three-dimensional data. This data is collected in real time and transmitted to a central database. The collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and matching units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit and registers it in the database. For example, the analysis unit can extract the characteristics of lost items using image analysis algorithms and register them in the database. Specifically, it applies deep learning-based image recognition technology to the collected image data to automatically extract characteristics such as the shape, color, size, and material of the lost items. For example, it can accurately identify lost items of various shapes and colors, such as folded umbrellas or dropped wallets. Furthermore, the analysis unit can automate the process of classifying the collected data and registering it in the database. For example, it can set categories for each type of lost item and classify them into categories such as umbrellas, wallets, and mobile phones and register them in the database. This allows the subsequent matching unit to efficiently search and match the data. In addition, the analysis unit can utilize past data and statistical information to continuously train models for more accurate extraction of lost item characteristics. As a result, the analysis unit can improve the reliability and efficiency of the entire system by quickly and accurately analyzing the collected data and registering it in the database.

[0032] The reception department accepts descriptions of lost items entered by the owner through online forms or apps. For example, the reception department can accept descriptions of lost items entered by the owner through an online form. Specifically, it provides a form where the owner can enter detailed descriptions of the lost item, such as the type of item, color, shape, location of loss, and date and time of loss. This allows the owner to accurately describe the characteristics of the lost item. The reception department can also accept descriptions of lost items entered by the owner through an app. The app provides a user-friendly interface, making it easy for the owner to enter information about the lost item. For example, it provides a function to upload photos and a function to select the type of lost item using a pull-down menu. The reception department can register the descriptions of lost items entered by the owner in a database. This ensures that the information provided by the owner is centrally managed within the system, allowing the subsequent matching department to efficiently search and match the data. The reception department also has a function to verify the accuracy of the information entered by the owner, and can send a notification to the owner prompting them to correct any errors in the input. This allows the reception department to receive information from owners accurately and quickly, improving the reliability and efficiency of the entire system.

[0033] The matching unit uses image recognition and data matching technologies to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use deep learning-based image recognition technology to compare features received by the reception unit with data registered by the analysis unit. Specifically, it compares the features of lost items provided by the owner with the data of lost items collected and analyzed by the collection and analysis units to identify matches. For example, based on features such as color, shape, and size of the lost item entered by the owner, it compares them with lost item data in the database to identify items with a high degree of similarity. The matching unit can identify the most similar lost item by using computer vision technology to extract features from image data and perform similarity calculations. For example, if the owner enters the features of a lost red wallet, the matching unit searches the database for image data of red wallets and identifies the most similar one. Furthermore, the matching unit can combine multiple features to improve the reliability of the matching results. For example, by combining multiple features such as color, shape, size, and material, more accurate results can be obtained. This allows the matching unit to quickly and accurately compare the information provided by the owner with the collected data, assisting in the reassembly of lost items.

[0034] The notification unit notifies the owner of an item identified by the matching unit. For example, the notification unit can notify the owner of an identified item using email notifications. Specifically, it sends information about the lost item identified by the matching unit to the owner via email, notifying them of details such as the storage location and how to retrieve the item. The notification unit can also notify the owner of an identified item using push notifications. For example, it sends a push notification to the owner's smartphone informing them that the lost item has been found. Furthermore, the notification unit can notify the owner of an identified item using SMS notifications. For example, it sends an SMS to the owner's mobile phone providing information about the lost item. By using a combination of these notification methods, the notification unit can ensure that information is reliably delivered to the owner. For example, by using both email and push notifications, it can prevent the owner from missing the notification. The notification unit can also collect feedback from the owner and continuously improve the accuracy and effectiveness of the notification content. For example, after the owner has received the lost item, it can send a questionnaire regarding the notification content to collect feedback. This allows the notification unit to provide information to the owner quickly and reliably and assist in the reassembly of lost items.

[0035] The data collection unit can collect data on lost items using cameras and sensors installed at the station's lost and found center. The data collection unit can, for example, capture images of lost items using surveillance cameras installed at the station's lost and found center. The data collection unit can, for example, detect the characteristics of lost items using infrared sensors installed at the station's lost and found center. The data collection unit can, for example, identify the shape and color of lost items by analyzing image data captured by cameras installed at the station's lost and found center. This makes the collection of data on lost items more efficient by using cameras and sensors installed at the station's lost and found center. Cameras and sensors include, but are not limited to, surveillance cameras and infrared sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data captured by a camera into a generating AI and have the generating AI perform the process of extracting the characteristics of lost items from the image data.

[0036] The analysis unit can analyze the collected data and register it in a database. The analysis unit can, for example, analyze the collected data using an image analysis algorithm and extract the characteristics of lost items. The analysis unit can, for example, automate the procedure for registering the collected data in a database. The analysis unit can, for example, classify the collected data and register it in a database. This makes it easier to manage lost items by analyzing the collected data and registering it in a database. The database includes, but is not limited to, SQL databases and NoSQL databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the process of registering it in a database.

[0037] The reception unit can receive the characteristics of lost items entered by the owner through an online form or app. For example, the reception unit can receive the characteristics of lost items entered by the owner through an online form. For example, the reception unit can receive the characteristics of lost items entered by the owner through an app. For example, the reception unit can register the characteristics of lost items entered by the owner in a database. This allows for the rapid identification of lost items by receiving the characteristics of lost items entered by the owner through an online form or app. Online forms and apps include, but are not limited to, input fields and user interface designs. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the characteristics of lost items entered by the owner into a generating AI and have the generating AI perform the process of registering them in a database.

[0038] The matching unit can compare features received by the reception unit with data registered by the analysis unit using image recognition and data matching technologies. For example, the matching unit can use deep learning-based image recognition technology to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use computer vision technology to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use similarity calculation to compare features received by the reception unit with data registered by the analysis unit. This improves the accuracy of identifying lost items by using image recognition and data matching technologies. Image recognition and data matching technologies include, but are not limited to, machine learning algorithms and pattern recognition technologies. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input features received by the reception unit and data registered by the analysis unit into a generating AI and have the generating AI perform the matching process.

[0039] The notification unit can notify the owner of an identified item. The notification unit can notify the owner of an identified item using, for example, email notifications. The notification unit can notify the owner of an identified item using, for example, push notifications. The notification unit can notify the owner of an identified item using, for example, SMS notifications. This enables rapid reconnection by notifying the owner of an identified item. Notifications include, but are not limited to, email notifications, push notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the notification of an identified item into a generating AI and have the generating AI execute the notification processing.

[0040] The AI ​​chatbot unit can handle inquiries about lost items. For example, if an owner asks the chatbot, "I want to know the status of my lost item," the chatbot can search its database and inform the owner of the status. For example, if an owner inputs the characteristics of the lost item into the chatbot, the chatbot can search its database and provide the owner with information about the identified item. For example, if an owner asks the chatbot about the procedure for returning a lost item, the chatbot can guide the owner through the details of the procedure. This reduces the burden on human resources by having the AI ​​chatbot unit handle inquiries about lost items. The AI ​​chatbot includes, but is not limited to, natural language processing technology and dialogue management systems. Some or all of the above-described processes in the AI ​​chatbot unit may be performed using AI, for example, or not using AI. For example, the AI ​​chatbot unit can input the owner's inquiry into a generating AI and have the generating AI execute the response processing.

[0041] The collection unit may incorporate additional sensors to record the condition and characteristics of lost items in detail. For example, the collection unit may use a high-resolution camera to record the details of an item. For example, the collection unit may use a weight sensor to record the weight of an item. For example, the collection unit may use a material sensor to identify the material of an item. Thus, by incorporating additional sensors, the condition and characteristics of an item can be recorded in detail. Additional sensors include, but are not limited to, temperature sensors, humidity sensors, and pressure sensors. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit may input data acquired from additional sensors into a generating AI and have the generating AI perform the process of recording the condition and characteristics of the item.

[0042] The collection unit can apply different collection algorithms to lost items based on the material and shape of the items. For example, in the case of metal products, the collection unit can use a magnetic sensor for collection. For example, in the case of plastic products, the collection unit can use an optical sensor for collection. For example, in the case of textile products, the collection unit can use a tactile sensor for collection. This improves collection accuracy by applying different collection algorithms based on the material and shape of the items. Collection algorithms include, but are not limited to, material-specific algorithms and shape-specific algorithms. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input material and shape data of the items into a generating AI and have the generating AI execute the process of applying the collection algorithm.

[0043] The collection unit can optimize the efficiency of collecting lost items by taking into account the congestion level of the station. For example, the collection unit can distribute collection work during peak hours. For example, the collection unit can concentrate collection work during less crowded times. For example, the collection unit can change the collection route according to the congestion level. This optimizes the efficiency of collection by taking into account the congestion level of the station. The congestion level is evaluated based on criteria such as the number of people or the congestion index. 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 congestion data into a generating AI and have the generating AI perform the collection efficiency optimization process.

[0044] The collection unit can improve the accuracy of lost item collection by analyzing ambient sounds. For example, the collection unit can analyze the surrounding noise level and perform collection work in a quiet location. For example, the collection unit can detect human voices from ambient sounds and pinpoint the location of lost items. For example, the collection unit can detect changes in ambient sounds and adjust the timing of collection work. This improves the accuracy of collection by analyzing ambient sounds. The analysis of ambient sounds includes, but is not limited to, noise levels and types of sounds. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input ambient sound data into a generating AI and have the generating AI perform the ambient sound analysis processing.

[0045] The analysis unit can optimize its analysis algorithm by referring to past lost item data during analysis. For example, the analysis unit can identify similar lost items from past data and adjust the analysis algorithm. For example, the analysis unit can improve the accuracy of the analysis based on past data. For example, the analysis unit can optimize the efficiency of the analysis by referring to past data. This optimizes the analysis algorithm by referring to past lost item data. Past lost item data includes, but is not limited to, database queries and how historical data is used. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past lost item data into a generating AI and have the generating AI perform the optimization process of the analysis algorithm.

[0046] The analysis unit can apply different analysis methods to each category of item during analysis. For example, in the case of electronic equipment, the analysis unit can apply a method to analyze the internal structure. For example, in the case of clothing, the analysis unit can apply a method to analyze the material and design. For example, in the case of books, the analysis unit can apply a method to analyze the cover and content. By applying different analysis methods to each category of item, the accuracy of the analysis is improved. Different analysis methods for each category include, but are not limited to, analysis methods for clothing and analysis methods for electronic equipment. 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 item category data into a generating AI and have the generating AI execute the process of applying the analysis method.

[0047] The analysis unit can determine the priority of analysis based on the submission date of the items during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted items. For example, the analysis unit can postpone the analysis of older items. For example, the analysis unit can adjust the analysis schedule according to the submission date. As a result, by determining the priority of analysis based on the submission date of the items, recently submitted items are analyzed preferentially. The evaluation of the submission date is performed based on criteria such as the submission date and time, and the time elapsed since submission. 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 submission date data of the items into a generating AI and have the generating AI perform the analysis priority determination process.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the item during the analysis. For example, the analysis unit can perform the analysis by referring to technical literature on the item. For example, the analysis unit can perform the analysis by referring to the instruction manual for use of the item. For example, the analysis unit can perform the analysis by referring to information on the manufacturer of the item. This improves the accuracy of the analysis by referring to relevant literature on the item. Relevant literature includes, but is not limited to, academic papers and technical reports. 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 relevant literature data on the item into a generating AI and have the generating AI perform the analysis processing.

[0049] The reception unit can select the optimal reception method by referring to the user's past lost item submission history at the time of submission. The reception unit can, for example, automatically display as suggestions the departure and destination locations that the user has frequently entered in the past. The reception unit can, for example, prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can, for example, predict and suggest departure and destination locations to be used during a specific time period based on the user's past input history. In this way, the optimal reception method is selected by referring to the user's past lost item submission history. Past lost item submission history includes, for example, the submission date and time, the type of item submitted, etc., but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input past lost item submission history data into a generating AI and have the generating AI perform the process of selecting the optimal reception method.

[0050] The reception unit can filter the user's current situation and areas of interest at the time of reception. For example, if the user is in a hurry, the reception unit can suggest a reception method that allows for a quick response. For example, if the user has a specific area of ​​interest, the reception unit can prioritize displaying information related to that area. For example, the reception unit can select the optimal reception method according to the user's current situation. This ensures that the optimal reception method is provided by filtering based on the user's current situation and areas of interest. The evaluation of the current situation and areas of interest includes, but is not limited to, current activities and topics of interest. Some or all of the processing described above in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering process.

[0051] The reception unit can prioritize the reception of highly relevant lost items by considering the user's geographical location information at the time of reception. For example, the reception unit can prioritize the reception of lost items that are close to the user's current location. For example, the reception unit can prioritize the reception of lost items that are related to places the user frequently visits. For example, the reception unit can select the optimal reception method based on the user's geographical location information. This ensures that highly relevant lost items are prioritized by considering the user's geographical location information. The evaluation of geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of determining the priority of highly relevant lost items.

[0052] The reception department can analyze the user's social media activity at the time of receipt and accept related lost items. The reception department can, for example, extract information about lost items from the user's social media posts. The reception department can, for example, analyze the user's social media activity and prioritize the acceptance of related lost items. The reception department can, for example, select the optimal acceptance method based on the user's social media activity. As a result, by analyzing the user's social media activity, related lost items are accepted preferentially. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI execute the acceptance process for related lost items.

[0053] The matching unit can improve the accuracy of the matching process by considering the interrelationships between lost items. For example, the matching unit can associate and match lost items found in the same location. For example, the matching unit can associate and match lost items found at the same time. For example, the matching unit can associate and match lost items that have the same characteristics. This improves the accuracy of the matching process by considering the interrelationships between lost items. The evaluation of the interrelationships between lost items includes, but is not limited to, relevance scores and co-occurrence relationships. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input data on the interrelationships of lost items into a generating AI and have the generating AI perform the matching process.

[0054] The matching unit can perform matching while considering the attribute information of the person who submitted the lost item. For example, the matching unit can perform matching while considering the age and gender of the person who submitted the item. For example, the matching unit can perform matching while considering the occupation and hobbies of the person who submitted the item. For example, the matching unit can perform matching while considering the past history of the person who submitted the lost item. This improves the accuracy of matching by considering the attribute information of the person who submitted the lost item. The evaluation of the attribute information of the person who submitted the item includes, but is not limited to, age, gender, and occupation. 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 attribute information data of the person who submitted the item into a generating AI and have the generating AI perform the matching process.

[0055] The matching unit can perform matching while considering the geographical distribution of lost items. For example, the matching unit can perform matching based on geographical information of the location where the lost item was found. For example, the matching unit can perform matching while considering information about the vicinity of the location where the lost item was found. For example, the matching unit can perform matching while considering the geographical characteristics of the location where the lost item was found. This improves the accuracy of matching by considering the geographical distribution of lost items. Examples of geographical distribution evaluation include, but are not limited to, regional distribution and location information heatmaps. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input geographical distribution data of lost items into a generating AI and have the generating AI perform the matching process.

[0056] The matching unit can improve the accuracy of the matching process by referring to relevant literature on the lost item during the matching process. For example, the matching unit can perform the matching by referring to technical literature on the lost item. For example, the matching unit can perform the matching by referring to the user manual for the lost item. For example, the matching unit can perform the matching by referring to information on the manufacturer of the lost item. This improves the accuracy of the matching process by referring to relevant literature on the lost item. Relevant literature includes, but is not limited to, academic papers and technical reports. 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 data on relevant literature on the lost item into a generating AI and have the generating AI perform the matching process.

[0057] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has preferred to use in the past. For example, the notification unit can select the optimal notification timing from the user's past notification history. For example, the notification unit can customize the notification content based on the user's past notification history. This ensures that the optimal notification method is selected by referring to the user's past notification history. Past notification history includes, but is not limited to, notification date and time, notification content, etc. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0058] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the notification unit can prioritize voice notifications if the user is on the move. For example, the notification unit can prioritize vibration notifications if the user is in a meeting. For example, the notification unit can prioritize email notifications if the user is at home. This allows for more appropriate notifications by customizing the notification method based on the user's current situation. The evaluation of the current situation includes, but is not limited to, current activity status and location information. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the notification method customization process.

[0059] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can be set to receive notifications in locations close to the user's current location. For example, the notification unit can be set to receive notifications in locations the user frequently visits. For example, the notification unit can select the optimal notification method based on the user's geographical location information. This ensures that the optimal notification method is selected by considering the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0060] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. For example, the notification unit can suggest a suitable notification method from the user's social media posts. For example, the notification unit can analyze the user's social media activity and suggest the optimal notification method. For example, the notification unit can customize the notification content based on the user's social media activity. This allows the optimal notification method to be suggested by analyzing the user's social media activity. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI perform the notification method suggestion process.

[0061] The AI ​​chatbot unit can select the optimal response method by referring to past inquiry history when the chatbot responds. For example, the AI ​​chatbot unit can prioritize selecting a response method that the user has previously preferred. For example, the AI ​​chatbot unit can select the optimal response timing from the user's past inquiry history. For example, the AI ​​chatbot unit can customize the response content based on the user's past inquiry history. This allows the optimal response method to be selected by referring to past inquiry history. Past inquiry history includes, but is not limited to, the date and time of the inquiry and the content of the inquiry. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input past inquiry history data into a generating AI and have the generating AI execute the process of selecting the optimal response method.

[0062] The AI ​​chatbot unit can customize the means of response based on the user's current situation when the chatbot responds. For example, the AI ​​chatbot unit can prioritize voice responses if the user is on the move. For example, the AI ​​chatbot unit can prioritize text responses if the user is in a meeting. For example, the AI ​​chatbot unit can prioritize detailed text responses if the user is at home. This allows for more appropriate responses by customizing the means of response based on the user's current situation. The evaluation of the current situation includes, but is not limited to, current activity status and location information. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's current situation data into a generating AI and have the generating AI perform the process of customizing the means of response.

[0063] The AI ​​chatbot unit can select the optimal response method when the chatbot responds, taking into account the user's geographical location information. For example, the AI ​​chatbot unit can be configured to receive responses from locations close to the user's current location. For example, the AI ​​chatbot unit can be configured to receive responses from locations the user frequently visits. For example, the AI ​​chatbot unit can select the optimal response method based on the user's geographical location information. This ensures that the optimal response method is selected by considering the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of selecting the optimal response method.

[0064] The AI ​​chatbot unit can analyze the user's social media activity and suggest a response method when the chatbot responds. For example, the AI ​​chatbot unit can suggest a suitable response method from the user's social media posts. For example, the AI ​​chatbot unit can analyze the user's social media activity and suggest the optimal response method. For example, the AI ​​chatbot unit can customize the response content based on the user's social media activity. In this way, the optimal response method is suggested by analyzing the user's social media activity. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's social media activity data into a generating AI and have the generating AI perform the response method suggestion process.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The collection unit can simultaneously collect environmental data when gathering data on lost items. For example, the collection unit can collect environmental data such as temperature, humidity, and illuminance at the location where the lost item was found. This allows for an understanding of the environmental conditions under which the lost item was found, which can be used to identify the condition and characteristics of the lost item. Furthermore, the collection unit transmits the environmental data to the analysis unit, which can estimate the deterioration and preservation status of the lost item based on the environmental data. This enables more precise management of lost items.

[0067] The analysis unit can estimate the usage history of lost items when analyzing data on lost items. For example, the analysis unit can analyze the wear and tear and degree of soiling of lost items to estimate how much they have been used. This allows for an understanding of the frequency and condition of use of lost items, which can be used to identify the owner. Furthermore, the analysis unit can register the usage history data in a database and analyze its relationship with other lost items. This improves the accuracy of identifying lost items and allows for their return to the owner more quickly.

[0068] The reception desk can suggest similar lost items based on the characteristics of the lost item entered by the owner. For example, the reception desk can search its database for similar lost items based on the characteristics entered by the owner and present them as candidates. This allows for the quick identification of a lost item that matches the characteristics entered by the owner. Furthermore, the reception desk can provide additional information when the owner selects the correct lost item from the presented candidates. This helps the owner identify the correct lost item and ensures a smooth return process.

[0069] The matching unit can consider the geographical characteristics of the location where the lost item was found when matching it. For example, the matching unit can improve the accuracy of identifying the lost item based on the geographical characteristics of the location where the item was found (such as the structure of the station or surrounding facilities). This allows for matching that takes into account where the lost item was found, enabling a quicker return to the owner. Furthermore, the matching unit can transmit geographical characteristic data to the analysis unit, which can then improve the accuracy of identifying the location where the lost item was found based on the geographical characteristics.

[0070] The notification unit can customize the content of notifications sent to the owner. For example, the notification unit can provide notifications in various formats such as text, images, and audio, according to the owner's preferences. This ensures that the owner receives notifications in the format that is most convenient for them, and the return process proceeds smoothly. Furthermore, the notification unit can refer to the owner's past notification history and select the most suitable notification method. This ensures that the owner receives notifications most effectively and that the lost item is returned quickly.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The collection unit collects data on lost items using cameras and sensors. For example, data on lost items can be collected using cameras and sensors installed at a lost and found center in a train station. Images of lost items can be captured using surveillance cameras, and the characteristics of lost items can be detected using infrared sensors. By analyzing the image data captured by the cameras, the shape and color of the lost items can be identified. Step 2: The analysis unit analyzes the data collected by the collection unit and registers it in the database. For example, it can extract the characteristics of lost items using an image analysis algorithm and register them in the database. The procedure for registering collected data in the database can be automated. The collected data can be classified and registered in the database. Step 3: The reception desk accepts the description of the lost item entered by the owner via an online form or app. For example, it can accept the description of the lost item entered by the owner via an online form. It can accept the description of the lost item entered by the owner via an app. It can register the description of the lost item entered by the owner in a database. Step 4: The matching unit uses image recognition and data matching technologies to compare the features received by the reception unit with the data registered by the analysis unit. For example, deep learning-based image recognition technology or computer vision technology can be used to compare the features received by the reception unit with the data registered by the analysis unit. Similarity calculation can also be used to compare the features received by the reception unit with the data registered by the analysis unit. Step 5: The notification unit notifies the owner of the item identified by the matching unit. For example, the owner can be notified of the identified item using email, push notification, or SMS notification.

[0073] (Example of form 2) The lost and found management system according to an embodiment of the present invention is a system that automates the lost and found management process at train stations and aims for the rapid reunification of owners with their lost items. This lost and found management system analyzes data on lost items collected by cameras and sensors and registers it in a database. When the owner enters the characteristics of the lost item through an online form or app, the system instantly identifies the correct item through image recognition and data matching and notifies the owner. In addition, an AI chatbot handles inquiries about lost items, reducing the burden on human resources and contributing to improved user satisfaction. For example, in the lost and found management system, cameras installed in the station's lost and found center capture images of lost items, and sensors detect their characteristics. This data is analyzed by AI and registered in a database. When the owner enters the characteristics of the lost item through an online form or app, that information is sent to the AI. The AI ​​compares the lost and found data in the database with the entered characteristics using image recognition and data matching technology to identify the correct item. The identified item is then notified to the owner. Upon receiving the notification through the app, the owner can go to the lost and found center to pick up their item. This enables the rapid reunification of owners with their lost items. Furthermore, an AI chatbot handles inquiries about lost items. When an owner asks the chatbot, "I want to know the status of my lost item," the chatbot searches the database and informs the owner of the status. This reduces the burden on human resources and improves user satisfaction. The lost and found management system delivers concrete benefits such as improved return rates for lost items, reduced inquiry response times, and increased customer satisfaction. It also features advanced image recognition technology and data matching algorithms, a user-friendly interface, and 24 / 7 customer support via an AI chatbot. This meets the needs of users who want to find lost items quickly and reliably, and also addresses the needs of operators seeking to reduce and streamline human resources. The lost and found management system delivers a creative solution that improves the user experience by innovating the lost and found management process through the combination of AI and IoT technologies. The target audience includes commuters and travelers of all ages, railway companies, transportation companies, and large-scale facility operators such as shopping malls.This system addresses the inefficiencies and customer dissatisfaction associated with the lost and found management and return process, enabling rapid and accurate identification and notification of lost items through an AI-powered lost and found management system. This allows the system to automate the entire process, from data collection to notification to the owner, facilitating quick reunification.

[0074] The lost and found management system according to this embodiment comprises a collection unit, an analysis unit, a reception unit, a matching unit, and a notification unit. The collection unit collects data on lost items using cameras and sensors. The collection unit can, for example, collect data on lost items using cameras and sensors installed at a lost and found center in a train station. The collection unit can, for example, capture images of lost items using surveillance cameras and detect features of lost items using infrared sensors. The collection unit can, for example, identify the shape and color of lost items by analyzing image data captured by cameras. The analysis unit analyzes the data collected by the collection unit and registers it in a database. The analysis unit can, for example, extract features of lost items using image analysis algorithms and register them in a database. The analysis unit can, for example, automate the procedure for registering collected data in a database. The analysis unit can, for example, classify collected data and register it in a database. The reception unit accepts features of lost items entered by the owner through online forms or apps. The reception unit can, for example, accept features of lost items entered by the owner in an online form. The reception unit can, for example, receive the characteristics of lost items entered by the owner through the app. The reception unit can, for example, register the characteristics of lost items entered by the owner in a database. The matching unit uses image recognition and data matching technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use deep learning-based image recognition technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use computer vision technology to match the characteristics received by the reception unit with the data registered by the analysis unit. The matching unit can, for example, use similarity calculation to match the characteristics received by the reception unit with the data registered by the analysis unit. The notification unit notifies the owner of the item identified by the matching unit. The notification unit can, for example, notify the owner of the item identified using email notification. The notification unit can, for example, notify the owner of the item identified using push notification.The notification unit can, for example, notify the owner of an identified item using SMS notification. This allows the lost and found management system to automate the process from collecting data on lost items to notifying the owner, enabling rapid reconnection.

[0075] The collection unit collects data on lost items using cameras and sensors. For example, the collection unit can collect data on lost items using cameras and sensors installed in a lost and found center at a train station. Specifically, high-resolution cameras installed in the station premises, platforms, and ticket gates capture images of lost items. Multiple cameras are installed to cover a wide area, allowing for accurate identification of the location of lost items. In addition, by using infrared sensors and ultrasonic sensors in combination, the unit can detect the characteristics of lost items, such as their shape, material, and size, in detail. For example, infrared sensors measure the surface temperature of lost items and are useful for identifying materials such as metal or fabric. Ultrasonic sensors measure the distance and shape of lost items, allowing for the acquisition of three-dimensional data. This data is collected in real time and transmitted to a central database. The collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and matching units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0076] The analysis unit analyzes the data collected by the collection unit and registers it in the database. For example, the analysis unit can extract the characteristics of lost items using image analysis algorithms and register them in the database. Specifically, it applies deep learning-based image recognition technology to the collected image data to automatically extract characteristics such as the shape, color, size, and material of the lost items. For example, it can accurately identify lost items of various shapes and colors, such as folded umbrellas or dropped wallets. Furthermore, the analysis unit can automate the process of classifying the collected data and registering it in the database. For example, it can set categories for each type of lost item and classify them into categories such as umbrellas, wallets, and mobile phones and register them in the database. This allows the subsequent matching unit to efficiently search and match the data. In addition, the analysis unit can utilize past data and statistical information to continuously train models for more accurate extraction of lost item characteristics. As a result, the analysis unit can improve the reliability and efficiency of the entire system by quickly and accurately analyzing the collected data and registering it in the database.

[0077] The reception department accepts descriptions of lost items entered by the owner through online forms or apps. For example, the reception department can accept descriptions of lost items entered by the owner through an online form. Specifically, it provides a form where the owner can enter detailed descriptions of the lost item, such as the type of item, color, shape, location of loss, and date and time of loss. This allows the owner to accurately describe the characteristics of the lost item. The reception department can also accept descriptions of lost items entered by the owner through an app. The app provides a user-friendly interface, making it easy for the owner to enter information about the lost item. For example, it provides a function to upload photos and a function to select the type of lost item using a pull-down menu. The reception department can register the descriptions of lost items entered by the owner in a database. This ensures that the information provided by the owner is centrally managed within the system, allowing the subsequent matching department to efficiently search and match the data. The reception department also has a function to verify the accuracy of the information entered by the owner, and can send a notification to the owner prompting them to correct any errors in the input. This allows the reception department to receive information from owners accurately and quickly, improving the reliability and efficiency of the entire system.

[0078] The matching unit uses image recognition and data matching technologies to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use deep learning-based image recognition technology to compare features received by the reception unit with data registered by the analysis unit. Specifically, it compares the features of lost items provided by the owner with the data of lost items collected and analyzed by the collection and analysis units to identify matches. For example, based on features such as color, shape, and size of the lost item entered by the owner, it compares them with lost item data in the database to identify items with a high degree of similarity. The matching unit can identify the most similar lost item by using computer vision technology to extract features from image data and perform similarity calculations. For example, if the owner enters the features of a lost red wallet, the matching unit searches the database for image data of red wallets and identifies the most similar one. Furthermore, the matching unit can combine multiple features to improve the reliability of the matching results. For example, by combining multiple features such as color, shape, size, and material, more accurate results can be obtained. This allows the matching unit to quickly and accurately compare the information provided by the owner with the collected data, assisting in the reassembly of lost items.

[0079] The notification unit notifies the owner of an item identified by the matching unit. For example, the notification unit can notify the owner of an identified item using email notifications. Specifically, it sends information about the lost item identified by the matching unit to the owner via email, notifying them of details such as the storage location and how to retrieve the item. The notification unit can also notify the owner of an identified item using push notifications. For example, it sends a push notification to the owner's smartphone informing them that the lost item has been found. Furthermore, the notification unit can notify the owner of an identified item using SMS notifications. For example, it sends an SMS to the owner's mobile phone providing information about the lost item. By using a combination of these notification methods, the notification unit can ensure that information is reliably delivered to the owner. For example, by using both email and push notifications, it can prevent the owner from missing the notification. The notification unit can also collect feedback from the owner and continuously improve the accuracy and effectiveness of the notification content. For example, after the owner has received the lost item, it can send a questionnaire regarding the notification content to collect feedback. This allows the notification unit to provide information to the owner quickly and reliably and assist in the reassembly of lost items.

[0080] The data collection unit can collect data on lost items using cameras and sensors installed at the station's lost and found center. The data collection unit can, for example, capture images of lost items using surveillance cameras installed at the station's lost and found center. The data collection unit can, for example, detect the characteristics of lost items using infrared sensors installed at the station's lost and found center. The data collection unit can, for example, identify the shape and color of lost items by analyzing image data captured by cameras installed at the station's lost and found center. This makes the collection of data on lost items more efficient by using cameras and sensors installed at the station's lost and found center. Cameras and sensors include, but are not limited to, surveillance cameras and infrared sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data captured by a camera into a generating AI and have the generating AI perform the process of extracting the characteristics of lost items from the image data.

[0081] The analysis unit can analyze the collected data and register it in a database. The analysis unit can, for example, analyze the collected data using an image analysis algorithm and extract the characteristics of lost items. The analysis unit can, for example, automate the procedure for registering the collected data in a database. The analysis unit can, for example, classify the collected data and register it in a database. This makes it easier to manage lost items by analyzing the collected data and registering it in a database. The database includes, but is not limited to, SQL databases and NoSQL databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the process of registering it in a database.

[0082] The reception unit can receive the characteristics of lost items entered by the owner through an online form or app. For example, the reception unit can receive the characteristics of lost items entered by the owner through an online form. For example, the reception unit can receive the characteristics of lost items entered by the owner through an app. For example, the reception unit can register the characteristics of lost items entered by the owner in a database. This allows for the rapid identification of lost items by receiving the characteristics of lost items entered by the owner through an online form or app. Online forms and apps include, but are not limited to, input fields and user interface designs. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the characteristics of lost items entered by the owner into a generating AI and have the generating AI perform the process of registering them in a database.

[0083] The matching unit can compare features received by the reception unit with data registered by the analysis unit using image recognition and data matching technologies. For example, the matching unit can use deep learning-based image recognition technology to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use computer vision technology to compare features received by the reception unit with data registered by the analysis unit. For example, the matching unit can use similarity calculation to compare features received by the reception unit with data registered by the analysis unit. This improves the accuracy of identifying lost items by using image recognition and data matching technologies. Image recognition and data matching technologies include, but are not limited to, machine learning algorithms and pattern recognition technologies. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input features received by the reception unit and data registered by the analysis unit into a generating AI and have the generating AI perform the matching process.

[0084] The notification unit can notify the owner of an identified item. The notification unit can notify the owner of an identified item using, for example, email notifications. The notification unit can notify the owner of an identified item using, for example, push notifications. The notification unit can notify the owner of an identified item using, for example, SMS notifications. This enables rapid reconnection by notifying the owner of an identified item. Notifications include, but are not limited to, email notifications, push notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the notification of an identified item into a generating AI and have the generating AI execute the notification processing.

[0085] The AI ​​chatbot unit can handle inquiries about lost items. For example, if an owner asks the chatbot, "I want to know the status of my lost item," the chatbot can search its database and inform the owner of the status. For example, if an owner inputs the characteristics of the lost item into the chatbot, the chatbot can search its database and provide the owner with information about the identified item. For example, if an owner asks the chatbot about the procedure for returning a lost item, the chatbot can guide the owner through the details of the procedure. This reduces the burden on human resources by having the AI ​​chatbot unit handle inquiries about lost items. The AI ​​chatbot includes, but is not limited to, natural language processing technology and dialogue management systems. Some or all of the above-described processes in the AI ​​chatbot unit may be performed using AI, for example, or not using AI. For example, the AI ​​chatbot unit can input the owner's inquiry into a generating AI and have the generating AI execute the response processing.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection for lost items based on the estimated emotions. For example, if the user is anxious, the data collection unit can start data collection quickly. For example, if the user is relaxed, the data collection unit can collect data at the normal timing. For example, if the user is feeling anxious, the data collection unit can prioritize data collection. By adjusting the timing of data collection based on the user's emotions, data for lost items can be collected at a more appropriate time. The estimation of the user's emotions is performed using technologies such as facial recognition, voice analysis, and text analysis. Emotion estimation is implemented using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0087] The collection unit may incorporate additional sensors to record the condition and characteristics of lost items in detail. For example, the collection unit may use a high-resolution camera to record the details of an item. For example, the collection unit may use a weight sensor to record the weight of an item. For example, the collection unit may use a material sensor to identify the material of an item. Thus, by incorporating additional sensors, the condition and characteristics of an item can be recorded in detail. Additional sensors include, but are not limited to, temperature sensors, humidity sensors, and pressure sensors. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit may input data acquired from additional sensors into a generating AI and have the generating AI perform the process of recording the condition and characteristics of the item.

[0088] The collection unit can apply different collection algorithms to lost items based on the material and shape of the items. For example, in the case of metal products, the collection unit can use a magnetic sensor for collection. For example, in the case of plastic products, the collection unit can use an optical sensor for collection. For example, in the case of textile products, the collection unit can use a tactile sensor for collection. This improves collection accuracy by applying different collection algorithms based on the material and shape of the items. Collection algorithms include, but are not limited to, material-specific algorithms and shape-specific algorithms. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input material and shape data of the items into a generating AI and have the generating AI execute the process of applying the collection algorithm.

[0089] 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 can prioritize collecting important lost items. For example, if the user is relaxed, the collection unit can collect items with normal priority. For example, if the user is feeling uneasy, the collection unit can prioritize collecting valuables. This allows for the priority collection of important lost items by determining the priority of lost items based on the user's emotions. Prioritization is based on criteria such as importance score and urgency assessment. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0090] The collection unit can optimize the efficiency of collecting lost items by taking into account the congestion level of the station. For example, the collection unit can distribute collection work during peak hours. For example, the collection unit can concentrate collection work during less crowded times. For example, the collection unit can change the collection route according to the congestion level. This optimizes the efficiency of collection by taking into account the congestion level of the station. The congestion level is evaluated based on criteria such as the number of people or the congestion index. 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 congestion data into a generating AI and have the generating AI perform the collection efficiency optimization process.

[0091] The collection unit can improve the accuracy of lost item collection by analyzing ambient sounds. For example, the collection unit can analyze the surrounding noise level and perform collection work in a quiet location. For example, the collection unit can detect human voices from ambient sounds and pinpoint the location of lost items. For example, the collection unit can detect changes in ambient sounds and adjust the timing of collection work. This improves the accuracy of collection by analyzing ambient sounds. The analysis of ambient sounds includes, but is not limited to, noise levels and types of sounds. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input ambient sound data into a generating AI and have the generating AI perform the ambient sound analysis processing.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is anxious, the analysis unit can prioritize the analysis of important lost items. For example, if the user is relaxed, the analysis unit can perform the analysis with the normal priority. For example, if the user is feeling anxious, the analysis unit can prioritize the analysis of valuables. This ensures that the analysis of important lost items is prioritized by adjusting the analysis priority based on the user's emotions. The determination of analysis priority is based on criteria such as importance score and urgency assessment. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0093] The analysis unit can optimize its analysis algorithm by referring to past lost item data during analysis. For example, the analysis unit can identify similar lost items from past data and adjust the analysis algorithm. For example, the analysis unit can improve the accuracy of the analysis based on past data. For example, the analysis unit can optimize the efficiency of the analysis by referring to past data. This optimizes the analysis algorithm by referring to past lost item data. Past lost item data includes, but is not limited to, database queries and how historical data is used. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past lost item data into a generating AI and have the generating AI perform the optimization process of the analysis algorithm.

[0094] The analysis unit can apply different analysis methods to each category of item during analysis. For example, in the case of electronic equipment, the analysis unit can apply a method to analyze the internal structure. For example, in the case of clothing, the analysis unit can apply a method to analyze the material and design. For example, in the case of books, the analysis unit can apply a method to analyze the cover and content. By applying different analysis methods to each category of item, the accuracy of the analysis is improved. Different analysis methods for each category include, but are not limited to, analysis methods for clothing and analysis methods for electronic equipment. 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 item category data into a generating AI and have the generating AI execute the process of applying the analysis method.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display that is easy for the user to understand is provided. Display methods of analysis results include, but are not limited to, graph displays, list displays, and highlight displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0096] The analysis unit can determine the priority of analysis based on the submission date of the items during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted items. For example, the analysis unit can postpone the analysis of older items. For example, the analysis unit can adjust the analysis schedule according to the submission date. As a result, by determining the priority of analysis based on the submission date of the items, recently submitted items are analyzed preferentially. The evaluation of the submission date is performed based on criteria such as the submission date and time, and the time elapsed since submission. 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 submission date data of the items into a generating AI and have the generating AI perform the analysis priority determination process.

[0097] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the item during the analysis. For example, the analysis unit can perform the analysis by referring to technical literature on the item. For example, the analysis unit can perform the analysis by referring to the instruction manual for use of the item. For example, the analysis unit can perform the analysis by referring to information on the manufacturer of the item. This improves the accuracy of the analysis by referring to relevant literature on the item. Relevant literature includes, but is not limited to, academic papers and technical reports. 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 relevant literature data on the item into a generating AI and have the generating AI perform the analysis processing.

[0098] The reception desk can estimate the user's emotions and adjust its interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of origin and destination. By adjusting the reception desk interface based on the user's emotions, a user-friendly interface is provided. Interface adjustments include, but are not limited to, user interface design and methods for improving usability. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0099] The reception unit can select the optimal reception method by referring to the user's past lost item submission history at the time of submission. The reception unit can, for example, automatically display as suggestions the departure and destination locations that the user has frequently entered in the past. The reception unit can, for example, prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can, for example, predict and suggest departure and destination locations to be used during a specific time period based on the user's past input history. In this way, the optimal reception method is selected by referring to the user's past lost item submission history. Past lost item submission history includes, for example, the submission date and time, the type of item submitted, etc., but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input past lost item submission history data into a generating AI and have the generating AI perform the process of selecting the optimal reception method.

[0100] The reception unit can filter the user's current situation and areas of interest at the time of reception. For example, if the user is in a hurry, the reception unit can suggest a reception method that allows for a quick response. For example, if the user has a specific area of ​​interest, the reception unit can prioritize displaying information related to that area. For example, the reception unit can select the optimal reception method according to the user's current situation. This ensures that the optimal reception method is provided by filtering based on the user's current situation and areas of interest. The evaluation of the current situation and areas of interest includes, but is not limited to, current activities and topics of interest. Some or all of the processing described above in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering process.

[0101] The reception desk can estimate the user's emotions and determine the priority of lost items to be received based on the estimated emotions. For example, if the user is anxious, the reception desk can prioritize important lost items. For example, if the user is relaxed, the reception desk can prioritize items with normal priority. For example, if the user is feeling uneasy, the reception desk can prioritize valuables. This allows for prioritizing important lost items based on the user's emotions. Prioritization is based on criteria such as importance score and urgency assessment. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0102] The reception unit can prioritize the reception of highly relevant lost items by considering the user's geographical location information at the time of reception. For example, the reception unit can prioritize the reception of lost items that are close to the user's current location. For example, the reception unit can prioritize the reception of lost items that are related to places the user frequently visits. For example, the reception unit can select the optimal reception method based on the user's geographical location information. This ensures that highly relevant lost items are prioritized by considering the user's geographical location information. The evaluation of geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of determining the priority of highly relevant lost items.

[0103] The reception department can analyze the user's social media activity at the time of receipt and accept related lost items. The reception department can, for example, extract information about lost items from the user's social media posts. The reception department can, for example, analyze the user's social media activity and prioritize the acceptance of related lost items. The reception department can, for example, select the optimal acceptance method based on the user's social media activity. As a result, by analyzing the user's social media activity, related lost items are accepted preferentially. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI execute the acceptance process for related lost items.

[0104] 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 can set criteria for rapid matching. For example, if the user is relaxed, the matching unit can perform matching using normal criteria. For example, if the user is feeling anxious, the matching unit can perform matching using strict criteria. By adjusting the matching criteria based on the user's emotions, more appropriate matching can be achieved. Adjustment of matching criteria includes, but is not limited to, similarity scores and matching algorithms. 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) and multimodal generation AI. 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 user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0105] The matching unit can improve the accuracy of the matching process by considering the interrelationships between lost items. For example, the matching unit can associate and match lost items found in the same location. For example, the matching unit can associate and match lost items found at the same time. For example, the matching unit can associate and match lost items that have the same characteristics. This improves the accuracy of the matching process by considering the interrelationships between lost items. The evaluation of the interrelationships between lost items includes, but is not limited to, relevance scores and co-occurrence relationships. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input data on the interrelationships of lost items into a generating AI and have the generating AI perform the matching process.

[0106] The matching unit can perform matching while considering the attribute information of the person who submitted the lost item. For example, the matching unit can perform matching while considering the age and gender of the person who submitted the item. For example, the matching unit can perform matching while considering the occupation and hobbies of the person who submitted the item. For example, the matching unit can perform matching while considering the past history of the person who submitted the lost item. This improves the accuracy of matching by considering the attribute information of the person who submitted the lost item. The evaluation of the attribute information of the person who submitted the item includes, but is not limited to, age, gender, and occupation. 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 attribute information data of the person who submitted the item into a generating AI and have the generating AI perform the matching process.

[0107] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. For example, if the user is anxious, the matching unit can prioritize displaying important matching results. For example, if the user is relaxed, the matching unit can display matching results in the normal order. For example, if the user is feeling anxious, the matching unit can prioritize displaying matching results for valuables. In this way, by adjusting the display order of matching results based on the user's emotions, important matching results are displayed preferentially. Adjustment of the display order of matching results includes, but is not limited to, sorting by importance or relevance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 the user's emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0108] The matching unit can perform matching while considering the geographical distribution of lost items. For example, the matching unit can perform matching based on geographical information of the location where the lost item was found. For example, the matching unit can perform matching while considering information about the vicinity of the location where the lost item was found. For example, the matching unit can perform matching while considering the geographical characteristics of the location where the lost item was found. This improves the accuracy of matching by considering the geographical distribution of lost items. Examples of geographical distribution evaluation include, but are not limited to, regional distribution and location information heatmaps. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input geographical distribution data of lost items into a generating AI and have the generating AI perform the matching process.

[0109] The matching unit can improve the accuracy of the matching process by referring to relevant literature on the lost item during the matching process. For example, the matching unit can perform the matching by referring to technical literature on the lost item. For example, the matching unit can perform the matching by referring to the user manual for the lost item. For example, the matching unit can perform the matching by referring to information on the manufacturer of the lost item. This improves the accuracy of the matching process by referring to relevant literature on the lost item. Relevant literature includes, but is not limited to, academic papers and technical reports. 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 data on relevant literature on the lost item into a generating AI and have the generating AI perform the matching process.

[0110] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is tense, the notification unit can send a notification in a calm tone. For example, if the user is relaxed, the notification unit can send a notification in a cheerful tone. For example, if the user is in a hurry, the notification unit can send a quick and concise notification. By adjusting the notification method based on the user's emotions, more appropriate notifications can be provided. Adjustments to the notification method include, but are not limited to, email notifications, push notifications, and SMS notifications. 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) and multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0111] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has preferred to use in the past. For example, the notification unit can select the optimal notification timing from the user's past notification history. For example, the notification unit can customize the notification content based on the user's past notification history. This ensures that the optimal notification method is selected by referring to the user's past notification history. Past notification history includes, but is not limited to, notification date and time, notification content, etc. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0112] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, the notification unit can prioritize voice notifications if the user is on the move. For example, the notification unit can prioritize vibration notifications if the user is in a meeting. For example, the notification unit can prioritize email notifications if the user is at home. This allows for more appropriate notifications by customizing the notification method based on the user's current situation. The evaluation of the current situation includes, but is not limited to, current activity status and location information. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation data into a generating AI and have the generating AI perform the notification method customization process.

[0113] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is anxious, the notification unit can prioritize important notifications. For example, if the user is relaxed, the notification unit can deliver notifications with normal priority. For example, if the user is feeling anxious, the notification unit can prioritize notifications about valuables. In this way, important notifications are prioritized by determining the priority of notifications based on the user's emotions. Prioritization includes, but is not limited to, importance scores and urgency ratings. 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 processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0114] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit can be set to receive notifications in locations close to the user's current location. For example, the notification unit can be set to receive notifications in locations the user frequently visits. For example, the notification unit can select the optimal notification method based on the user's geographical location information. This ensures that the optimal notification method is selected by considering the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0115] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. For example, the notification unit can suggest a suitable notification method from the user's social media posts. For example, the notification unit can analyze the user's social media activity and suggest the optimal notification method. For example, the notification unit can customize the notification content based on the user's social media activity. This allows the optimal notification method to be suggested by analyzing the user's social media activity. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI perform the notification method suggestion process.

[0116] The AI ​​chatbot unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the AI ​​chatbot unit can respond in a calm tone. For example, if the user is relaxed, the AI ​​chatbot unit can respond in a cheerful tone. For example, if the user is in a hurry, the AI ​​chatbot unit can provide a quick and concise response. By adjusting the chatbot's response method based on the user's emotions, a more appropriate response can be provided. Adjustment of the response method includes, but is not limited to, text responses, voice responses, and image responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input user emotion data into the generative AI and have the generative AI perform the emotion estimation process.

[0117] The AI ​​chatbot unit can select the optimal response method by referring to past inquiry history when the chatbot responds. For example, the AI ​​chatbot unit can prioritize selecting a response method that the user has previously preferred. For example, the AI ​​chatbot unit can select the optimal response timing from the user's past inquiry history. For example, the AI ​​chatbot unit can customize the response content based on the user's past inquiry history. This allows the optimal response method to be selected by referring to past inquiry history. Past inquiry history includes, but is not limited to, the date and time of the inquiry and the content of the inquiry. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input past inquiry history data into a generating AI and have the generating AI execute the process of selecting the optimal response method.

[0118] The AI ​​chatbot unit can customize the means of response based on the user's current situation when the chatbot responds. For example, the AI ​​chatbot unit can prioritize voice responses if the user is on the move. For example, the AI ​​chatbot unit can prioritize text responses if the user is in a meeting. For example, the AI ​​chatbot unit can prioritize detailed text responses if the user is at home. This allows for more appropriate responses by customizing the means of response based on the user's current situation. The evaluation of the current situation includes, but is not limited to, current activity status and location information. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's current situation data into a generating AI and have the generating AI perform the process of customizing the means of response.

[0119] The AI ​​chatbot unit can estimate the user's emotions and determine the priority of the chatbot's responses based on the estimated emotions. For example, if the user is anxious, the AI ​​chatbot unit can prioritize important responses. For example, if the user is relaxed, the AI ​​chatbot unit can respond with normal priority. For example, if the user is feeling anxious, the AI ​​chatbot unit can prioritize responses regarding valuables. In this way, important responses are prioritized by determining the priority of responses based on the user's emotions. Prioritization includes, but is not limited to, importance scores and urgency assessments. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI or not using AI. For example, the AI ​​chatbot unit can input user emotion data into a generative AI and have the generative AI perform the emotion estimation process.

[0120] The AI ​​chatbot unit can select the optimal response method when the chatbot responds, taking into account the user's geographical location information. For example, the AI ​​chatbot unit can be configured to receive responses from locations close to the user's current location. For example, the AI ​​chatbot unit can be configured to receive responses from locations the user frequently visits. For example, the AI ​​chatbot unit can select the optimal response method based on the user's geographical location information. This ensures that the optimal response method is selected by considering the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of selecting the optimal response method.

[0121] The AI ​​chatbot unit can analyze the user's social media activity and suggest a response method when the chatbot responds. For example, the AI ​​chatbot unit can suggest a suitable response method from the user's social media posts. For example, the AI ​​chatbot unit can analyze the user's social media activity and suggest the optimal response method. For example, the AI ​​chatbot unit can customize the response content based on the user's social media activity. In this way, the optimal response method is suggested by analyzing the user's social media activity. The analysis of social media activity includes, but is not limited to, posts, number of followers, and engagement rate. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI, for example, or without AI. For example, the AI ​​chatbot unit can input the user's social media activity data into a generating AI and have the generating AI perform the response method suggestion process.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The collection unit can simultaneously collect environmental data when gathering data on lost items. For example, the collection unit can collect environmental data such as temperature, humidity, and illuminance at the location where the lost item was found. This allows for an understanding of the environmental conditions under which the lost item was found, which can be used to identify the condition and characteristics of the lost item. Furthermore, the collection unit transmits the environmental data to the analysis unit, which can estimate the deterioration and preservation status of the lost item based on the environmental data. This enables more precise management of lost items.

[0124] The analysis unit can estimate the usage history of lost items when analyzing data on lost items. For example, the analysis unit can analyze the wear and tear and degree of soiling of lost items to estimate how much they have been used. This allows for an understanding of the frequency and condition of use of lost items, which can be used to identify the owner. Furthermore, the analysis unit can register the usage history data in a database and analyze its relationship with other lost items. This improves the accuracy of identifying lost items and allows for their return to the owner more quickly.

[0125] The reception desk can suggest similar lost items based on the characteristics of the lost item entered by the owner. For example, the reception desk can search its database for similar lost items based on the characteristics entered by the owner and present them as candidates. This allows for the quick identification of a lost item that matches the characteristics entered by the owner. Furthermore, the reception desk can provide additional information when the owner selects the correct lost item from the presented candidates. This helps the owner identify the correct lost item and ensures a smooth return process.

[0126] The matching unit can consider the geographical characteristics of the location where the lost item was found when matching it. For example, the matching unit can improve the accuracy of identifying the lost item based on the geographical characteristics of the location where the item was found (such as the structure of the station or surrounding facilities). This allows for matching that takes into account where the lost item was found, enabling a quicker return to the owner. Furthermore, the matching unit can transmit geographical characteristic data to the analysis unit, which can then improve the accuracy of identifying the location where the lost item was found based on the geographical characteristics.

[0127] The notification unit can customize the content of notifications sent to the owner. For example, the notification unit can provide notifications in various formats such as text, images, and audio, according to the owner's preferences. This ensures that the owner receives notifications in the format that is most convenient for them, and the return process proceeds smoothly. Furthermore, the notification unit can refer to the owner's past notification history and select the most suitable notification method. This ensures that the owner receives notifications most effectively and that the lost item is returned quickly.

[0128] The collection unit can estimate the user's emotions and determine the priority of lost items to collect based on those emotions. For example, if the user is anxious, the collection unit can prioritize collecting important lost items. If the user is relaxed, the collection unit can collect items with normal priority. If the user is feeling anxious, the collection unit can prioritize collecting valuables. This allows for the priority of collecting important lost items by determining the priority of lost items based on the user's emotions. Prioritization is based on criteria such as importance score and urgency assessment.

[0129] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is anxious, the analysis unit can prioritize the analysis of important lost items. If the user is relaxed, the analysis unit can perform the analysis with normal priorities. If the user is feeling anxious, the analysis unit can prioritize the analysis of valuables. This means that by adjusting the analysis priority based on the user's emotions, the analysis of important lost items is prioritized. The determination of analysis priority is based on criteria such as importance score and urgency assessment.

[0130] The reception desk can estimate the user's emotions and adjust its interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick entry of origin and destination. By adjusting the reception desk interface based on the user's emotions, a user-friendly interface is provided.

[0131] 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, the matching unit can set criteria for rapid matching. If the user is relaxed, the matching unit can perform matching using normal criteria. If the user is feeling anxious, the matching unit can perform matching using strict criteria. This allows for more appropriate matching by adjusting the matching criteria based on the user's emotions. Adjustments to the matching criteria include, for example, similarity scores and matching algorithms.

[0132] The notification unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, the notification unit can send notifications in a calm tone. If the user is relaxed, the notification unit can send notifications in a cheerful tone. If the user is in a hurry, the notification unit can send quick and concise notifications. This allows for more appropriate notifications by adjusting the notification method based on the user's emotions. Adjustments to the notification method include, for example, email notifications, push notifications, and SMS notifications.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The collection unit collects data on lost items using cameras and sensors. For example, data on lost items can be collected using cameras and sensors installed at a lost and found center in a train station. Images of lost items can be captured using surveillance cameras, and the characteristics of lost items can be detected using infrared sensors. By analyzing the image data captured by the cameras, the shape and color of the lost items can be identified. Step 2: The analysis unit analyzes the data collected by the collection unit and registers it in the database. For example, it can extract the characteristics of lost items using an image analysis algorithm and register them in the database. The procedure for registering collected data in the database can be automated. The collected data can be classified and registered in the database. Step 3: The reception desk accepts the description of the lost item entered by the owner via an online form or app. For example, it can accept the description of the lost item entered by the owner via an online form. It can accept the description of the lost item entered by the owner via an app. It can register the description of the lost item entered by the owner in a database. Step 4: The matching unit uses image recognition and data matching technologies to compare the features received by the reception unit with the data registered by the analysis unit. For example, deep learning-based image recognition technology or computer vision technology can be used to compare the features received by the reception unit with the data registered by the analysis unit. Similarity calculation can also be used to compare the features received by the reception unit with the data registered by the analysis unit. Step 5: The notification unit notifies the owner of the item identified by the matching unit. For example, the owner can be notified of the identified item using email, push notification, or SMS notification.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, reception unit, matching unit, notification unit, and AI chatbot unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on lost items using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and registers it in the database 24. The reception unit receives the characteristics of the lost item entered by the owner using the control unit 46A of the smart device 14. The matching unit matches the characteristics with the data using image recognition and data matching technology using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the owner of the item identified by the control unit 46A of the smart device 14. The AI ​​chatbot unit handles inquiries about lost items using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, reception unit, matching unit, notification unit, and AI chatbot unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on lost items using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and registers it in the database 24. The reception unit receives the characteristics of the lost item entered by the owner using the control unit 46A of the smart glasses 214. The matching unit matches the characteristics with the data using image recognition and data matching technology using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the owner of the item identified by the control unit 46A of the smart glasses 214. The AI ​​chatbot unit handles inquiries about lost items using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, reception unit, matching unit, notification unit, and AI chatbot unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on lost items using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and registers it in the database 24. The reception unit receives the characteristics of the lost item entered by the owner using the control unit 46A of the headset terminal 314. The matching unit uses image recognition and data matching technology to match the characteristics with the data using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the owner of the item identified by the control unit 46A of the headset terminal 314. The AI ​​chatbot unit handles inquiries about lost items using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, reception unit, matching unit, notification unit, and AI chatbot unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on lost items using the camera 42 and sensors of the robot 414. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 and registers it in the database 24. The reception unit receives the characteristics of the lost item entered by the owner by the control unit 46A of the robot 414. The matching unit matches the characteristics with the data using image recognition and data matching technology by the identification processing unit 290 of the data processing unit 12. The notification unit notifies the owner of the item identified by the control unit 46A of the robot 414. The AI ​​chatbot unit handles inquiries about lost items by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) The collection unit collects data on lost items using cameras and sensors, The analysis unit analyzes the data collected by the aforementioned collection unit and registers it in a database, The reception desk accepts descriptions of lost items entered by the owner via online forms or apps, A matching unit compares the features received by the reception unit with the data registered by the analysis unit using image recognition and data matching. The system includes a notification unit that notifies the owner of the item identified by the matching unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The lost and found center at the train station uses cameras and sensors to collect data on lost items. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed and registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is We accept descriptions of lost items entered by the owner via online forms or apps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is Using image recognition and data matching technologies, the features received by the reception unit are compared with the data registered by the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Notify the owner of the identified item. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes an AI chatbot unit for handling inquiries about lost and found items. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection for lost items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting lost items, we will introduce additional sensors to record the condition and characteristics of the items in detail. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting lost items, different collection algorithms are applied based on the material and shape of the items. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and 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 12) The aforementioned collection unit is When collecting lost items, the efficiency of the collection process is optimized by taking into account the congestion level at the station. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyzing ambient sounds during the collection of lost items improves the accuracy of the collection process. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past lost and found data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, different analysis methods are applied to each category of item. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant literature on the items to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is It estimates the user's emotions and adjusts the reception interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is At the time of submission, the system will refer to the user's past lost item submission history to select the most appropriate submission method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of lost items to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is When a lost item is reported, the system prioritizes receiving it based on the user's geographical location, ensuring that the item is as relevant as possible. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is At the time of registration, the system analyzes the user's social media activity and registers any related lost items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is The system estimates the user's emotions and adjusts the matching criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification 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 28) The aforementioned verification 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 29) The aforementioned verification unit is It estimates the user's emotions and adjusts the display order of the matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is During the matching process, the geographical distribution of the lost items will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature on the lost item. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned AI chatbot unit is It estimates the user's emotions and adjusts the chatbot's response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned AI chatbot unit is When the chatbot responds, it refers to past inquiry history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned AI chatbot unit is When a chatbot responds, it customizes the response method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned AI chatbot unit is It estimates the user's emotions and prioritizes the chatbot's responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned AI chatbot unit is When the chatbot responds, it selects the optimal response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned AI chatbot unit is When the chatbot responds, it analyzes the user's social media activity and suggests alternative response methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 unit collects data on lost items using cameras and sensors, The analysis unit analyzes the data collected by the aforementioned collection unit and registers it in a database, The reception desk accepts descriptions of lost items entered by the owner via online forms or apps, A matching unit compares the features received by the reception unit with the data registered by the analysis unit using image recognition and data matching. The system includes a notification unit that notifies the owner of the item identified by the matching unit. A system characterized by the following features.

2. The aforementioned collection unit is The lost and found center at the train station uses cameras and sensors to collect data on lost items. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed and registered in the database. The system according to feature 1.

4. The aforementioned reception unit is We accept descriptions of lost items entered by the owner via online forms or apps. The system according to feature 1.

5. The aforementioned verification unit is Using image recognition and data matching technologies, the features received by the reception unit are compared with the data registered by the analysis unit. The system according to feature 1.

6. The aforementioned notification unit, Notify the owner of the identified item. The system according to feature 1.

7. It includes an AI chatbot unit to handle inquiries about lost and found items. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection for lost items based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting lost items, we will introduce additional sensors to record the condition and characteristics of the items in detail. The system according to feature 1.

Images