System

Abstract

To provide a system that enables even a user, who does not know how to look for a lost object, to efficiently find the lost object.SOLUTION: A system comprises a reception part, a search part, a collation part, and a specification part. The reception part receives detailed information on a lost object from a user. The search part analyzes the information received by the reception part to search a database at a police office. The collation part collates a lost object, delivered newly to the police office, to find whether the lost object matches information in the database. The specification part specifies the owner of the lost object based upon My-Number card information.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem that it was a lot of work for users who did not know how to search for lost items and for the police, and that lost items often reached the storage limit.

[0005] The system according to the embodiment aims to enable even a user who does not know how to find lost items to find them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a collation unit, and an identification unit. The reception unit receives detailed information about a lost item from a user. The search unit analyzes the information received by the reception unit and searches a police station database. The collation unit checks whether a newly arrived lost item at the police station matches information in an existing database. The identification unit identifies the owner based on the My Number card information. Effect of the Invention

[0007] The system according to the embodiment allows even a user who does not know how to find a lost item to find it efficiently. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The lost property management system according to the embodiment of the present invention is a system for efficiently managing and returning lost property stored at a police station. In this system, a user inputs detailed information about a lost property using ChatBot, which then uses natural language processing technology to analyze the input information and search the police station's database. If the lost property is found, the information is provided to the user. In addition, an AI matching system is used to automatically check whether a lost property newly received at a police station matches the information in the existing database. For example, if a user loses a smartphone, an AI checks whether the information on the smartphone newly received at the police station matches the information registered in the database, and if they match, an automatic notification is sent to the user's smartphone. Furthermore, by introducing a matching system with My Number card information, the owner of the lost property can be identified. For example, if a My Number card is found in a wallet, the police station can identify the owner of the lost property based on that information and contact the owner. This system allows users to easily search for lost property and reduces the burden on the police station. In addition, the AI ​​matching system and the My Number card information matching system allow the owner of the lost property to be found before the storage period of the lost property is reached, improving storage efficiency. For example, a user uses ChatBot to input information such as "I lost a black wallet. It contains credit cards and cash." This information is analyzed by ChatBot and searched for in the police station's database. Then, the AI ​​matching system is used to automatically check whether a newly arrived lost item at the police station matches the information in the existing database. For example, the AI ​​checks whether the information of a newly arrived black wallet matches the information registered in the database. If there is a match, an automatic notification is sent to the user's smartphone. Furthermore, by introducing a matching system with My Number card information, the owner of the lost item can be identified. For example, if a My Number card is found in the wallet, the police station can identify the owner based on that information and contact them. This system allows users to easily search for lost items and reduces the burden on the police station.In addition, the AI ​​matching system and the matching system for My Number card information can find the owner of the lost item before the storage period expires, improving the efficiency of storage. As a result, the lost item management system can improve the efficiency of the management and return of lost items stored at police stations.

[0029] The lost property management system according to the embodiment includes a reception unit, a search unit, a collation unit, and an identification unit. The reception unit receives detailed information on the lost property from the user. The detailed information on the lost property from the user includes, for example, the type, color, shape, and characteristics of the item, but is not limited to such examples. The reception unit receives detailed information on the lost property from the user using, for example, a ChatBot. The ChatBot uses a dialogue type AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. For example, when the user inputs "I lost my black wallet. It contains a credit card and cash," the ChatBot analyzes the information and searches the police station's database. The search unit analyzes the user's input using natural language processing technology and searches the police station's database. The natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and the like, but is not limited to such examples. For example, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, the search unit can also use semantic analysis to analyze the meaning of the user's input and provide highly relevant search results. The matching unit uses image recognition technology to analyze the characteristics of the newly received lost item and match it with the database. Image recognition technology includes, for example, object detection, face recognition, feature extraction, etc., but is not limited to such examples. For example, the matching unit uses object detection technology to analyze the image of the newly received black wallet and match it with the information of the black wallet registered in the database. The matching unit can also use face recognition technology to analyze the characteristics of the lost item and match it with the database. Furthermore, the matching unit can also use feature extraction technology to analyze the characteristics of the lost item in detail and improve matching accuracy. The identification unit uses encryption technology to safely match the My Number card information and identify the owner. Encryption technology includes, for example, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), hash function, etc., but is not limited to such examples. For example, the identification unit encrypts the My Number card information with AES and safely matches it with the database.The identification unit can also encrypt the My Number card information using RSA and verify it. Furthermore, the identification unit can hash the My Number card information using a hash function and verify it securely. This allows the lost item management system according to the embodiment to efficiently accept detailed information on lost items from users and to analyze, search, verify, and identify them.

[0030] The reception unit receives detailed information about the lost item from the user. The detailed information about the lost item from the user includes, but is not limited to, the type, color, shape, and features of the item. The reception unit receives detailed information about the lost item from the user using, for example, ChatBot. ChatBot uses conversational AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. Specifically, ChatBot analyzes the text entered by the user in real time and extracts the features of the item. For example, if the user enters "I lost my black wallet. It contains a credit card and cash," ChatBot extracts keywords such as "black wallet," "credit card," and "cash," and registers this information in the database. In addition, ChatBot can also collect additional information through dialogue with the user. For example, it automatically generates questions such as "What brand is the wallet?" and "What else is in the wallet?" and complements the information based on the user's answer. This allows the reception unit to efficiently collect detailed information from the user and register it in the database. ChatBot can also support multiple languages, and can accurately accept information from users who speak different languages. This allows the reception unit to accommodate a variety of users and to centrally manage information on lost items.

[0031] The search unit uses natural language processing technology to analyze the user's input and search the police station's database. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Specifically, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. For example, from the input "I lost my black wallet," keywords such as "black" and "wallet" are extracted, and the database is searched based on these keywords. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. For example, from the input "There are credit cards and cash in the wallet," highly relevant keywords such as "wallet," "credit card," and "cash" are extracted, and a search is performed by combining these. Furthermore, the search unit can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. For example, from the input "I lost my black wallet," similar lost items are searched for based on color, shape, and characteristics, and presented to the user. This allows the search unit to analyze the user's input from multiple angles and provide highly accurate search results. In addition, the search unit updates the database in real time, and newly registered lost property information can be reflected in the search results immediately. This allows the search unit to always perform searches based on the latest information, enabling the provision of fast and accurate information to users.

[0032] The matching unit uses image recognition technology to analyze the characteristics of the newly received lost item and match it with the database. Image recognition technology includes, but is not limited to, object detection, face recognition, feature extraction, and the like. Specifically, the matching unit uses object detection technology to analyze the image of the newly received black wallet and match it with the information of the black wallet registered in the database. Object detection technology can identify a specific object in an image and specify its position and shape. For example, the outline and characteristic pattern of the wallet are detected from the image of the black wallet, and based on this information, the information is matched with the information in the database. The matching unit can also use face recognition technology to analyze the characteristics of the lost item and match it with the database. For example, the owner can be identified by analyzing the photograph or face image on the ID card attached to the wallet and matching it with the face image in the database. Furthermore, the matching unit can also use feature extraction technology to analyze the characteristics of the lost item in detail and improve the matching accuracy. Feature extraction technology quantifies specific features in the image and performs matching based on these features. For example, features such as the color, shape, pattern, and material of the wallet are quantified, and based on these features, the information in the database is matched. This allows the matching unit to perform detailed analysis of the characteristics of newly received lost items, enabling highly accurate matching. The matching unit also updates the database in real time, allowing newly registered lost item information to be reflected immediately in the matching results. This allows the matching unit to always perform matching based on the latest information, enabling the provision of fast and accurate information to users.

[0033] The identification unit safely collates the My Number card information using encryption technology and identifies the owner. Examples of encryption technology include, but are not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and hash functions. Specifically, the identification unit encrypts the My Number card information with AES and safely collates it with the database. AES is a fast and secure encryption algorithm, and can encrypt the My Number card information and store it in the database. The identification unit can also encrypt and collate the My Number card information using RSA. RSA is an encryption algorithm that uses a public key cryptosystem, and can safely encrypt the My Number card information and collate it with the database. Furthermore, the identification unit can hash the My Number card information using a hash function and safely collate it. A hash function is an algorithm that converts input data into a fixed-length hash value, and can hash the My Number card information and store it in the database. This allows the identification unit to safely manage the My Number card information and identify the owner. The identification unit also implements multi-layered security measures to prevent unauthorized access and data leakage. For example, access to the database is strictly limited, and only authorized users can access it. This allows the identification department to quickly and accurately identify the owner while maintaining a high level of security.

[0034] The reception unit can use ChatBot to receive detailed information about the lost item from the user. ChatBot uses conversational AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. For example, if the user inputs "I lost a black wallet. It contains a credit card and cash," ChatBot analyzes the information and searches the police station's database. ChatBot performs morphological analysis on the user's input, extracts keywords, and searches the database. ChatBot can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, ChatBot can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. This makes it easier for users to input information using ChatBot. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI execute analysis of the input data.

[0035] The search unit can analyze the user's input using natural language processing technology and search the police station database. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. For example, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, the search unit can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. As a result, by using natural language processing technology, the user's input information is accurately analyzed and search accuracy is improved. Part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's input data to the generation AI and cause the generation AI to execute analysis of the input data.

[0036] The matching unit can analyze the characteristics of the newly received lost item using image recognition technology and match it with the database. Image recognition technology includes, for example, object detection, face recognition, feature extraction, etc., but is not limited to such examples. For example, the matching unit can analyze the image of the newly received black wallet using object detection technology and match it with the information of the black wallet registered in the database. The matching unit can also analyze the characteristics of the lost item using face recognition technology and match it with the database. Furthermore, the matching unit can also analyze the characteristics of the lost item in detail using feature extraction technology to improve the matching accuracy. As a result, the matching accuracy of the lost item is improved by using the image recognition technology. Part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI or may be performed without using AI. For example, the matching unit can input image data of the newly received lost item to the generation AI and cause the generation AI to execute the analysis of the image data.

[0037] The identification unit can safely compare the My Number card information using encryption technology and identify the owner. Encryption technology includes, but is not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), hash functions, etc. For example, the identification unit encrypts the My Number card information with AES and safely compares it with a database. The identification unit can also encrypt the My Number card information using RSA and compare it. Furthermore, the identification unit can hash the My Number card information using a hash function and safely compare it. This makes it possible to safely compare the My Number card information by using encryption technology. A part or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the My Number card information to the generation AI and have the generation AI execute the comparison of the information.

[0038] When a matching lost item is found, the matching unit can automatically send a notification to the user's smartphone. The automatic notification includes, for example, the timing of the notification, the notification content, and the notification means, but is not limited to such examples. For example, when a matching lost item is found, the matching unit immediately sends a notification to the user's smartphone. In addition, the matching unit can include detailed information about the lost item and a method of receiving the lost item as the notification content. Furthermore, the matching unit can use push notification, SMS, e-mail, etc. as the notification means. This allows a notification to be sent quickly to the user when a matching lost item is found. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the matching result to the generation AI and cause the generation AI to generate the notification content.

[0039] The reception unit can analyze the user's past inquiry history and provide an optimal information input format. For example, the reception unit can automatically display the types of lost items that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest an input format to be used in a specific time period from the user's past inquiry history. This improves the user's input efficiency by providing an optimal input format based on the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's inquiry history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input format.

[0040] The reception unit can provide input assistance based on the user's current situation or environment when inputting detailed information about the lost item. For example, when the user is out, the reception unit prioritizes voice input to allow the user to input information quickly. In addition, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is on the move, the reception unit can provide a simple interface and minimize input procedures. This improves input efficiency by providing input assistance according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's situation data to the generation AI and cause the generation AI to execute the input assistance method.

[0041] When inputting detailed information about a lost item, the reception unit can preferentially accept information that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially accepts information about lost items related to the area. In addition, when the user is moving, the reception unit can preferentially accept information about lost items related to the current location. Furthermore, when the user is in a specific location, the reception unit can preferentially accept information about lost items related to the location. This improves input efficiency by preferentially accepting information that is highly relevant based on geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute preferential acceptance of information that is highly relevant.

[0042] The reception unit can analyze the user's social media activity and complement related information when inputting detailed information about the lost item. The reception unit complements the detailed information about the lost item based on, for example, information shared by the user on social media. The reception unit can also automatically extract related information from the user's social media activity and assist in input. Furthermore, the reception unit can complement the detailed information about the lost item based on location information posted by the user on social media. This improves the accuracy of input by complementing related information based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute completion of related information.

[0043] The search unit can apply an optimal search algorithm by referring to past search history at the time of search. The search unit can apply an optimal search algorithm based on, for example, keywords that the user has frequently searched for in the past. The search unit can also preferentially display highly relevant search results from the user's past search history. Furthermore, the search unit can analyze the user's past search patterns and apply an optimal search algorithm. This improves search accuracy by applying an optimal search algorithm based on the past search history. A part or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can input the user's search history data to the generation AI and cause the generation AI to apply the optimal search algorithm.

[0044] The search unit can apply different search methods depending on the category of the lost item during the search. For example, in the case of electronic devices, the search unit searches based on serial numbers and model numbers. In addition, in the case of clothing, the search unit can search based on brands and sizes. Furthermore, in the case of valuables, the search unit can search based on characteristic marks and engravings. This improves the accuracy of the search by applying a search method according to the category. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input category data of the lost item to the generation AI and cause the generation AI to apply the optimal search method.

[0045] The search unit can adjust the priority of search results based on the submission time of the lost item during the search. For example, the search unit can preferentially display recently submitted lost items. Also, the search unit can postpone old submitted lost items. Furthermore, the search unit can preferentially display highly relevant information based on the submission time. As a result, by adjusting the priority of search results based on the submission time, highly relevant information is preferentially displayed. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input submission time data to the generation AI and cause the generation AI to adjust the priority of the search results.

[0046] The search unit can adjust the order of search results based on the relevance of the lost items during the search. For example, the search unit can preferentially display items that match the characteristics of the lost items. The search unit can also preferentially display items that match the categories of the lost items. Furthermore, the search unit can preferentially display items that match the submission locations of the lost items. This improves the accuracy of the search by adjusting the order of the search results based on the relevance. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the relevance data of the lost items to the generation AI and cause the generation AI to adjust the order of the search results.

[0047] The matching unit can improve the accuracy of matching by analyzing the characteristics of the lost item in detail during matching. The matching unit can improve the accuracy of matching by analyzing, for example, the color and shape of the lost item in detail. The matching unit can also improve the accuracy of matching by analyzing the material and texture of the lost item. Furthermore, the matching unit can improve the accuracy of matching by analyzing the size and weight of the lost item. As a result, the accuracy of matching is improved by analyzing the characteristics in detail. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the characteristic data of the lost item to the generation AI and cause the generation AI to perform a detailed analysis of the characteristics.

[0048] The matching unit can perform matching while taking into account the geographical distribution of the lost items. For example, the matching unit can perform matching based on the location where the lost items were submitted. Also, the matching unit can perform matching based on the location where the lost items were found. Furthermore, the matching unit can perform matching based on the path of movement of the lost items. This improves the accuracy of matching by taking the geographical distribution into account. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input geographical distribution data of the lost items to the generation AI and cause the generation AI to perform matching.

[0049] The matching unit can improve the accuracy of matching by referring to related literature on the lost item during matching. The matching unit can improve the accuracy of matching by referring to related literature on the characteristics of the lost item, for example. The matching unit can also improve the accuracy of matching by referring to related literature on the material and manufacturing method of the lost item. Furthermore, the matching unit can improve the accuracy of matching by referring to related literature on the method of using and storing the lost item. As a result, the accuracy of matching is improved by referring to the related literature. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input related literature data to the generation AI and have the generation AI perform matching.

[0050] The identification unit can improve the accuracy of identification by analyzing the details of the My Number card information at the time of identification. The identification unit can, for example, analyze the My Number card number and name in detail to improve the accuracy of identification. The identification unit can also analyze the issue date and expiration date of the My Number card to improve the accuracy of identification. Furthermore, the identification unit can analyze the address information of the My Number card to improve the accuracy of identification. As a result, the accuracy of identification is improved by analyzing the details of the My Number card information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the My Number card information to the generation AI and cause the generation AI to perform a detailed analysis of the information.

[0051] The identification unit can identify the lost item by considering attribute information of the person who submitted the lost item. The identification unit can identify the lost item by considering, for example, the age and gender of the person who submitted the lost item. The identification unit can also identify the lost item by considering the occupation and hobbies of the person who submitted the lost item. The identification unit can also identify the lost item by considering the place of residence and commuting route of the person who submitted the lost item. This improves the accuracy of identification by considering the attribute information of the person who submitted the lost item. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the attribute information of the person who submitted the lost item to the generation AI and have the generation AI perform the identification.

[0052] The identification unit can perform identification while taking into consideration the geographical distribution of the lost items. For example, the identification unit can perform identification based on the location where the lost items were submitted. The identification unit can also perform identification based on the location where the lost items were found. Furthermore, the identification unit can perform identification based on the path of movement of the lost items. This improves the accuracy of identification by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input geographical distribution data of the lost items to the generation AI and have the generation AI perform identification.

[0053] The identification unit can improve the accuracy of identification by referring to related literature on the lost item at the time of identification. The identification unit can improve the accuracy of identification by referring to related literature on the characteristics of the lost item, for example. The identification unit can also improve the accuracy of identification by referring to related literature on the material and manufacturing method of the lost item. Furthermore, the identification unit can improve the accuracy of identification by referring to related literature on the method of using and storing the lost item. As a result, the accuracy of identification is improved by referring to the related literature. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input related literature data to the generation AI and have the generation AI perform the identification.

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

[0055] The reception unit can analyze the user's past behavior history and suggest the optimal information input method. For example, it preferentially suggests input methods (such as voice and text) that the user has used frequently in the past. The reception unit can also analyze patterns of information that the user has input in the past and provide an auto-completion function to reduce the effort required for input. Furthermore, the reception unit can predict and suggest the optimal input method for a specific time period from the user's past behavior history. This improves the user's input efficiency by providing the optimal input method based on the user's past behavior history.

[0056] The search unit can prioritize displaying highly relevant search results based on the user's current geographical location information. For example, when the user is in a specific area, information on lost items related to that area is displayed preferentially. In addition, when the user is moving, the search unit can prioritize displaying highly relevant information based on the current location. Furthermore, when the user is in a specific location, the search unit can prioritize displaying information on lost items related to that location. This improves search efficiency by preferentially displaying highly relevant information based on geographical location information.

[0057] The search unit can analyze the user's past search history and apply the optimal search algorithm. For example, the optimal search algorithm can be applied based on keywords that the user has frequently searched for in the past. Also, it is possible to preferentially display highly relevant search results based on the user's past search history. Furthermore, it is possible to analyze the user's past search patterns and apply the optimal search algorithm. This improves search accuracy by applying the optimal search algorithm based on the past search history.

[0058] During matching, the matching unit can improve the accuracy of matching by analyzing the characteristics of the lost item in detail. For example, the color and shape of the lost item can be analyzed in detail to improve the accuracy of matching. The material and texture of the lost item can also be analyzed to improve the accuracy of matching. Furthermore, the size and weight of the lost item can be analyzed to improve the accuracy of matching. In this way, the accuracy of matching is improved by analyzing the characteristics in detail.

[0059] When identifying the lost item, the identification unit can identify the lost item by taking into consideration attribute information of the person who submitted the lost item. For example, the identification unit can identify the lost item by taking into consideration the age and gender of the person who submitted the lost item. The identification unit can also identify the lost item by taking into consideration the job and hobby of the person who submitted the lost item. Furthermore, the identification unit can identify the lost item by taking into consideration the place of residence and commute route of the person who submitted the lost item. In this way, the accuracy of identification is improved by taking into consideration the attribute information of the person who submitted the lost item.

[0060] The reception unit can analyze the user's social media activity and complement related information. For example, detailed information about the lost item can be complemented based on information shared by the user on social media. Also, related information can be automatically extracted from the user's social media activity to assist in input. Furthermore, detailed information about the lost item can be complemented based on location information posted by the user on social media. This improves the accuracy of input by complementing related information based on social media activity.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives detailed information about the lost item from the user. The detailed information about the lost item from the user includes the type, color, shape, and characteristics of the item. For example, a ChatBot is used to receive detailed information about the lost item from the user. The ChatBot uses conversational AI to converse with the user and analyzes the user's input using natural language processing technology. Step 2: The search unit analyzes the information received by the reception unit and searches the police station database. The search unit uses natural language processing technology to analyze the user's input and performs morphological analysis, grammatical analysis, semantic analysis, etc. For example, the user's input is morphologically analyzed, keywords are extracted, and the database is searched. Grammatical analysis can also be used to analyze the grammatical structure of the user's input to improve search accuracy. Furthermore, semantic analysis can be used to analyze the meaning of the user's input to provide highly relevant search results. Step 3: The matching unit checks whether the newly received lost item at the police station matches the information in the existing database. Image recognition technology is used to analyze the characteristics of the newly received lost item and match it with the database. For example, technologies such as object detection, facial recognition, and feature extraction are used. The image of the newly received black wallet is analyzed using object detection technology and matched with the information on the black wallet registered in the database. In addition, facial recognition technology and feature extraction technology can be used to analyze the characteristics of the lost item in detail, improving the accuracy of matching. Step 4: The identification unit identifies the owner based on the My Number Card information. The My Number Card information is securely matched using encryption technology. For example, technologies such as AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and hash functions are used. The My Number Card information is encrypted using AES and securely matched with a database. It is also possible to encrypt and match the My Number Card information using RSA. Furthermore, it is also possible to hash the My Number Card information using a hash function and securely match it.

[0063] (Example 2) The lost property management system according to the embodiment of the present invention is a system for efficiently managing and returning lost property stored at a police station. In this system, a user inputs detailed information about a lost property using ChatBot, which then uses natural language processing technology to analyze the input information and search the police station's database. If the lost property is found, the information is provided to the user. In addition, an AI matching system is used to automatically check whether a lost property newly received at a police station matches the information in the existing database. For example, if a user loses a smartphone, an AI checks whether the information on the smartphone newly received at the police station matches the information registered in the database, and if they match, an automatic notification is sent to the user's smartphone. Furthermore, by introducing a matching system with My Number card information, the owner of the lost property can be identified. For example, if a My Number card is found in a wallet, the police station can identify the owner of the lost property based on that information and contact the owner. This system allows users to easily search for lost property and reduces the burden on the police station. In addition, the AI ​​matching system and the My Number card information matching system allow the owner of the lost property to be found before the storage period of the lost property is reached, improving storage efficiency. For example, a user uses ChatBot to input information such as "I lost a black wallet. It contains credit cards and cash." This information is analyzed by ChatBot and searched for in the police station's database. Then, the AI ​​matching system is used to automatically check whether a newly arrived lost item at the police station matches the information in the existing database. For example, the AI ​​checks whether the information of a newly arrived black wallet matches the information registered in the database. If there is a match, an automatic notification is sent to the user's smartphone. Furthermore, by introducing a matching system with My Number card information, the owner of the lost item can be identified. For example, if a My Number card is found in the wallet, the police station can identify the owner based on that information and contact them. This system allows users to easily search for lost items and reduces the burden on the police station.In addition, the AI ​​matching system and the matching system for My Number card information can find the owner of the lost item before the storage period expires, improving the efficiency of storage. As a result, the lost item management system can improve the efficiency of the management and return of lost items stored at police stations.

[0064] The lost property management system according to the embodiment includes a reception unit, a search unit, a collation unit, and an identification unit. The reception unit receives detailed information on the lost property from the user. The detailed information on the lost property from the user includes, for example, the type, color, shape, and characteristics of the item, but is not limited to such examples. The reception unit receives detailed information on the lost property from the user using, for example, a ChatBot. The ChatBot uses a dialogue type AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. For example, when the user inputs "I lost my black wallet. It contains a credit card and cash," the ChatBot analyzes the information and searches the police station's database. The search unit analyzes the user's input using natural language processing technology and searches the police station's database. The natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and the like, but is not limited to such examples. For example, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, the search unit can also use semantic analysis to analyze the meaning of the user's input and provide highly relevant search results. The matching unit uses image recognition technology to analyze the characteristics of the newly received lost item and match it with the database. Image recognition technology includes, for example, object detection, face recognition, feature extraction, etc., but is not limited to such examples. For example, the matching unit uses object detection technology to analyze the image of the newly received black wallet and match it with the information of the black wallet registered in the database. The matching unit can also use face recognition technology to analyze the characteristics of the lost item and match it with the database. Furthermore, the matching unit can also use feature extraction technology to analyze the characteristics of the lost item in detail and improve matching accuracy. The identification unit uses encryption technology to safely match the My Number card information and identify the owner. Encryption technology includes, for example, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), hash function, etc., but is not limited to such examples. For example, the identification unit encrypts the My Number card information with AES and safely matches it with the database.The identification unit can also encrypt the My Number card information using RSA and verify it. Furthermore, the identification unit can hash the My Number card information using a hash function and verify it securely. This allows the lost item management system according to the embodiment to efficiently accept detailed information on lost items from users and to analyze, search, verify, and identify them.

[0065] The reception unit receives detailed information about the lost item from the user. The detailed information about the lost item from the user includes, but is not limited to, the type, color, shape, and features of the item. The reception unit receives detailed information about the lost item from the user using, for example, ChatBot. ChatBot uses conversational AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. Specifically, ChatBot analyzes the text entered by the user in real time and extracts the features of the item. For example, if the user enters "I lost my black wallet. It contains a credit card and cash," ChatBot extracts keywords such as "black wallet," "credit card," and "cash," and registers this information in the database. In addition, ChatBot can also collect additional information through dialogue with the user. For example, it automatically generates questions such as "What brand is the wallet?" and "What else is in the wallet?" and complements the information based on the user's answer. This allows the reception unit to efficiently collect detailed information from the user and register it in the database. ChatBot can also support multiple languages, and can accurately accept information from users who speak different languages. This allows the reception unit to accommodate a variety of users and to centrally manage information on lost items.

[0066] The search unit uses natural language processing technology to analyze the user's input and search the police station's database. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Specifically, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. For example, from the input "I lost my black wallet," keywords such as "black" and "wallet" are extracted, and the database is searched based on these keywords. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. For example, from the input "There are credit cards and cash in the wallet," highly relevant keywords such as "wallet," "credit card," and "cash" are extracted, and a search is performed by combining these. Furthermore, the search unit can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. For example, from the input "I lost my black wallet," similar lost items are searched for based on color, shape, and characteristics, and presented to the user. This allows the search unit to analyze the user's input from multiple angles and provide highly accurate search results. In addition, the search unit updates the database in real time, and newly registered lost property information can be reflected in the search results immediately. This allows the search unit to always perform searches based on the latest information, enabling the provision of fast and accurate information to users.

[0067] The matching unit uses image recognition technology to analyze the characteristics of the newly received lost item and match it with the database. Image recognition technology includes, but is not limited to, object detection, face recognition, feature extraction, and the like. Specifically, the matching unit uses object detection technology to analyze the image of the newly received black wallet and match it with the information of the black wallet registered in the database. Object detection technology can identify a specific object in an image and specify its position and shape. For example, the outline and characteristic pattern of the wallet are detected from the image of the black wallet, and based on this information, the information is matched with the information in the database. The matching unit can also use face recognition technology to analyze the characteristics of the lost item and match it with the database. For example, the owner can be identified by analyzing the photograph or face image on the ID card attached to the wallet and matching it with the face image in the database. Furthermore, the matching unit can also use feature extraction technology to analyze the characteristics of the lost item in detail and improve the matching accuracy. Feature extraction technology quantifies specific features in the image and performs matching based on these features. For example, features such as the color, shape, pattern, and material of the wallet are quantified, and based on these features, the information in the database is matched. This allows the matching unit to perform detailed analysis of the characteristics of newly received lost items, enabling highly accurate matching. The matching unit also updates the database in real time, allowing newly registered lost item information to be reflected immediately in the matching results. This allows the matching unit to always perform matching based on the latest information, enabling the provision of fast and accurate information to users.

[0068] The identification unit safely collates the My Number card information using encryption technology and identifies the owner. Examples of encryption technology include, but are not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and hash functions. Specifically, the identification unit encrypts the My Number card information with AES and safely collates it with the database. AES is a fast and secure encryption algorithm, and can encrypt the My Number card information and store it in the database. The identification unit can also encrypt and collate the My Number card information using RSA. RSA is an encryption algorithm that uses a public key cryptosystem, and can safely encrypt the My Number card information and collate it with the database. Furthermore, the identification unit can hash the My Number card information using a hash function and safely collate it. A hash function is an algorithm that converts input data into a fixed-length hash value, and can hash the My Number card information and store it in the database. This allows the identification unit to safely manage the My Number card information and identify the owner. The identification unit also has multi-layered security measures in place to prevent unauthorized access and data leakage. For example, access to the database is strictly limited, and only authorized users can access it. This allows the identification department to quickly and accurately identify the owner while maintaining a high level of security.

[0069] The reception unit can use ChatBot to receive detailed information about the lost item from the user. ChatBot uses conversational AI to have a dialogue with the user and uses natural language processing technology to analyze the user's input. For example, if the user inputs "I lost a black wallet. It contains a credit card and cash," ChatBot analyzes the information and searches the police station's database. ChatBot performs morphological analysis on the user's input, extracts keywords, and searches the database. ChatBot can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, ChatBot can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. This makes it easier for users to input information using ChatBot. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI execute analysis of the input data.

[0070] The search unit can analyze the user's input using natural language processing technology and search the police station database. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. For example, the search unit performs morphological analysis on the user's input, extracts keywords, and searches the database. The search unit can also analyze the grammatical structure of the user's input using grammatical analysis to improve search accuracy. Furthermore, the search unit can analyze the meaning of the user's input using semantic analysis to provide highly relevant search results. As a result, by using natural language processing technology, the user's input information is accurately analyzed and search accuracy is improved. Part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's input data to the generation AI and cause the generation AI to execute analysis of the input data.

[0071] The matching unit can analyze the characteristics of the newly received lost item using image recognition technology and match it with the database. Image recognition technology includes, for example, object detection, face recognition, feature extraction, etc., but is not limited to such examples. For example, the matching unit can analyze the image of the newly received black wallet using object detection technology and match it with the information of the black wallet registered in the database. The matching unit can also analyze the characteristics of the lost item using face recognition technology and match it with the database. Furthermore, the matching unit can also analyze the characteristics of the lost item in detail using feature extraction technology to improve the matching accuracy. As a result, the matching accuracy of the lost item is improved by using the image recognition technology. Part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI or may be performed without using AI. For example, the matching unit can input image data of the newly received lost item to the generation AI and cause the generation AI to execute the analysis of the image data.

[0072] The identification unit can safely compare the My Number card information using encryption technology and identify the owner. Encryption technology includes, but is not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), hash functions, etc. For example, the identification unit encrypts the My Number card information with AES and safely compares it with a database. The identification unit can also encrypt the My Number card information using RSA and compare it. Furthermore, the identification unit can hash the My Number card information using a hash function and safely compare it. This makes it possible to safely compare the My Number card information by using encryption technology. A part or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the My Number card information to the generation AI and have the generation AI execute the comparison of the information.

[0073] When a matching lost item is found, the matching unit can automatically send a notification to the user's smartphone. The automatic notification includes, for example, the timing of the notification, the notification content, and the notification means, but is not limited to such examples. For example, when a matching lost item is found, the matching unit immediately sends a notification to the user's smartphone. In addition, the matching unit can include detailed information about the lost item and a method of receiving the lost item as the notification content. Furthermore, the matching unit can use push notification, SMS, e-mail, etc. as the notification means. This allows a notification to be sent quickly to the user when a matching lost item is found. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the matching result to the generation AI and cause the generation AI to generate the notification content.

[0074] The reception unit can estimate the user's emotion and adjust the input method of the detailed information of the lost item based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the detailed information of the lost item to be input quickly. In this way, by adjusting the input method according to the user's emotion, the user's stress is reduced and the input efficiency is improved. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past inquiry history and provide an optimal information input format. For example, the reception unit can automatically display the types of lost items that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest an input format to be used in a specific time period from the user's past inquiry history. This improves the user's input efficiency by providing an optimal input format based on the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's inquiry history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input format.

[0076] The reception unit can provide input assistance based on the user's current situation or environment when inputting detailed information about the lost item. For example, when the user is out, the reception unit prioritizes voice input to allow the user to input information quickly. In addition, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is on the move, the reception unit can provide a simple interface and minimize input procedures. This improves input efficiency by providing input assistance according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's situation data to the generation AI and cause the generation AI to execute the input assistance method.

[0077] The reception unit can estimate the user's emotion and determine the priority of the input information based on the estimated user's emotion. For example, when the user is nervous, the reception unit urges the user to input important information with priority. Furthermore, when the user is relaxed, the reception unit can urge the user to input detailed information with priority. Furthermore, when the user is in a hurry, the reception unit can urge the user to input the minimum necessary information with priority. In this way, important information is processed with priority by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0078] When inputting detailed information about a lost item, the reception unit can preferentially accept information that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially accepts information about lost items related to the area. In addition, when the user is moving, the reception unit can preferentially accept information about lost items related to the current location. Furthermore, when the user is in a specific location, the reception unit can preferentially accept information about lost items related to the location. This improves input efficiency by preferentially accepting information that is highly relevant based on geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute preferential acceptance of information that is highly relevant.

[0079] The reception unit can analyze the user's social media activity and complement related information when inputting detailed information about the lost item. The reception unit complements the detailed information about the lost item based on, for example, information shared by the user on social media. The reception unit can also automatically extract related information from the user's social media activity and assist in input. Furthermore, the reception unit can complement the detailed information about the lost item based on location information posted by the user on social media. This improves the accuracy of input by complementing related information based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute completion of related information.

[0080] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, when the user is nervous, the search unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the search unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the search unit can provide a display method that focuses on the main points. This reduces the user's stress and improves search efficiency by adjusting the display method of the search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the search results.

[0081] The search unit can apply an optimal search algorithm by referring to past search history at the time of search. The search unit can apply an optimal search algorithm based on, for example, keywords that the user has frequently searched for in the past. The search unit can also preferentially display highly relevant search results from the user's past search history. Furthermore, the search unit can analyze the user's past search patterns and apply an optimal search algorithm. This improves search accuracy by applying an optimal search algorithm based on the past search history. A part or all of the above-mentioned processing in the search unit can be performed, for example, using AI or can be performed without using AI. For example, the search unit can input the user's search history data to the generation AI and cause the generation AI to apply the optimal search algorithm.

[0082] The search unit can apply different search methods depending on the category of the lost item during the search. For example, in the case of electronic devices, the search unit searches based on serial numbers and model numbers. In addition, in the case of clothing, the search unit can search based on brands and sizes. Furthermore, in the case of valuables, the search unit can search based on characteristic marks and engravings. This improves the accuracy of the search by applying a search method according to the category. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input category data of the lost item to the generation AI and cause the generation AI to apply the optimal search method.

[0083] The search unit can estimate the user's emotion and determine the priority of search results based on the estimated user's emotion. For example, when the user is nervous, the search unit can display important information with priority. Also, when the user is relaxed, the search unit can display detailed information with priority. Furthermore, when the user is in a hurry, the search unit can display the minimum necessary information with priority. As a result, important information is displayed with priority by determining the priority of search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of search results.

[0084] The search unit can adjust the priority of search results based on the submission time of the lost item during the search. For example, the search unit can preferentially display recently submitted lost items. Also, the search unit can postpone old submitted lost items. Furthermore, the search unit can preferentially display highly relevant information based on the submission time. As a result, by adjusting the priority of search results based on the submission time, highly relevant information is preferentially displayed. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input submission time data to the generation AI and cause the generation AI to adjust the priority of the search results.

[0085] The search unit can adjust the order of search results based on the relevance of the lost items during the search. For example, the search unit can preferentially display items that match the characteristics of the lost items. The search unit can also preferentially display items that match the categories of the lost items. Furthermore, the search unit can preferentially display items that match the submission locations of the lost items. This improves the accuracy of the search by adjusting the order of the search results based on the relevance. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the relevance data of the lost items to the generation AI and cause the generation AI to adjust the order of the search results.

[0086] The collation unit can estimate the user's emotion and adjust the display method of the collation result based on the estimated user's emotion. For example, when the user is nervous, the collation unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the collation unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the collation unit can provide a display method that focuses on the main points. This reduces the user's stress and improves the efficiency of collation by adjusting the display method of the collation result according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the collation result.

[0087] The matching unit can improve the accuracy of matching by analyzing the characteristics of the lost item in detail during matching. The matching unit can improve the accuracy of matching by analyzing, for example, the color and shape of the lost item in detail. The matching unit can also improve the accuracy of matching by analyzing the material and texture of the lost item. Furthermore, the matching unit can improve the accuracy of matching by analyzing the size and weight of the lost item. As a result, the accuracy of matching is improved by analyzing the characteristics in detail. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using AI or can be performed without using AI. For example, the matching unit can input the characteristic data of the lost item to the generation AI and cause the generation AI to perform a detailed analysis of the characteristics.

[0088] The collation unit can estimate the user's emotion and determine the priority of the collation results based on the estimated user's emotion. For example, when the user is nervous, the collation unit can display important information with priority. Also, when the user is relaxed, the collation unit can display detailed information with priority. Furthermore, when the user is in a hurry, the collation unit can display the minimum necessary information with priority. As a result, important information is displayed with priority by determining the priority of the collation results according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the collation results.

[0089] The matching unit can perform matching while taking into account the geographical distribution of the lost items. For example, the matching unit can perform matching based on the location where the lost items were submitted. Also, the matching unit can perform matching based on the location where the lost items were found. Furthermore, the matching unit can perform matching based on the path of movement of the lost items. This improves the accuracy of matching by taking the geographical distribution into account. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the matching unit can input geographical distribution data of the lost items to the generation AI and cause the generation AI to perform matching.

[0090] The matching unit can improve the accuracy of matching by referring to related literature on the lost item during matching. The matching unit can improve the accuracy of matching by referring to related literature on the characteristics of the lost item, for example. The matching unit can also improve the accuracy of matching by referring to related literature on the material and manufacturing method of the lost item. Furthermore, the matching unit can improve the accuracy of matching by referring to related literature on the method of using and storing the lost item. As a result, the accuracy of matching is improved by referring to the related literature. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input related literature data to the generation AI and have the generation AI perform matching.

[0091] The identification unit can estimate the user's emotion and adjust the display method of the identified result based on the estimated user's emotion. For example, when the user is nervous, the identification unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the identification unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This reduces the user's stress and improves the efficiency of identification by adjusting the display method of the identified result according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the identified result.

[0092] The identification unit can improve the accuracy of identification by analyzing the details of the My Number card information at the time of identification. The identification unit can, for example, analyze the My Number card number and name in detail to improve the accuracy of identification. The identification unit can also analyze the issue date and expiration date of the My Number card to improve the accuracy of identification. Furthermore, the identification unit can analyze the address information of the My Number card to improve the accuracy of identification. As a result, the accuracy of identification is improved by analyzing the details of the My Number card information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the My Number card information to the generation AI and cause the generation AI to perform a detailed analysis of the information.

[0093] The identification unit can identify the lost item by considering attribute information of the person who submitted the lost item. The identification unit can identify the lost item by considering, for example, the age and gender of the person who submitted the lost item. The identification unit can also identify the lost item by considering the occupation and hobbies of the person who submitted the lost item. The identification unit can also identify the lost item by considering the place of residence and commuting route of the person who submitted the lost item. This improves the accuracy of identification by considering the attribute information of the person who submitted the lost item. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the attribute information of the person who submitted the lost item to the generation AI and have the generation AI perform the identification.

[0094] The identification unit can estimate the user's emotion and determine the priority of the identification results based on the estimated user's emotion. For example, when the user is nervous, the identification unit can preferentially display important information. Also, when the user is relaxed, the identification unit can preferentially display detailed information. Furthermore, when the user is in a hurry, the identification unit can preferentially display the minimum necessary information. As a result, important information is preferentially displayed by determining the priority of the identification results according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the identification results.

[0095] The identification unit can perform identification while taking into consideration the geographical distribution of the lost items. For example, the identification unit can perform identification based on the location where the lost items were submitted. The identification unit can also perform identification based on the location where the lost items were found. Furthermore, the identification unit can perform identification based on the path of movement of the lost items. This improves the accuracy of identification by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input geographical distribution data of the lost items to the generation AI and have the generation AI perform identification.

[0096] The identification unit can improve the accuracy of identification by referring to related literature on the lost item at the time of identification. The identification unit can improve the accuracy of identification by referring to related literature on the characteristics of the lost item, for example. The identification unit can also improve the accuracy of identification by referring to related literature on the material and manufacturing method of the lost item. Furthermore, the identification unit can improve the accuracy of identification by referring to related literature on the method of using and storing the lost item. As a result, the accuracy of identification is improved by referring to the related literature. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input related literature data to the generation AI and have the generation AI perform the identification.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the user's past behavior history and suggest the optimal information input method. For example, it preferentially suggests input methods (such as voice and text) that the user has used frequently in the past. The reception unit can also analyze patterns of information that the user has input in the past and provide an auto-completion function to reduce the effort required for input. Furthermore, the reception unit can predict and suggest the optimal input method for a specific time period from the user's past behavior history. This improves the user's input efficiency by providing the optimal input method based on the user's past behavior history.

[0099] The search unit can prioritize displaying highly relevant search results based on the user's current geographical location information. For example, when the user is in a specific area, information on lost items related to that area is displayed preferentially. In addition, when the user is moving, the search unit can prioritize displaying highly relevant information based on the current location. Furthermore, when the user is in a specific location, the search unit can prioritize displaying information on lost items related to that location. This improves search efficiency by preferentially displaying highly relevant information based on geographical location information.

[0100] The matching unit can estimate the user's emotions and adjust the display method of the matching results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the matching results according to the user's emotions, the user's stress can be reduced and the efficiency of matching can be improved.

[0101] The identification unit can estimate the user's emotion and determine the priority of the identification results based on the estimated user's emotion. For example, when the user is nervous, important information can be displayed with priority. When the user is relaxed, detailed information can be displayed with priority. When the user is in a hurry, the minimum necessary information can be displayed with priority. Thus, by determining the priority of the identification results according to the user's emotion, important information is displayed with priority.

[0102] The reception unit can estimate the user's emotion and adjust the input method of the detailed information of the lost item based on the estimated emotion of the user. For example, when the user is stressed, a simple interface can be provided and the input procedure can be minimized. When the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to allow the detailed information of the lost item to be input quickly. In this way, by adjusting the input method according to the user's emotion, the user's stress can be reduced and the input efficiency can be improved.

[0103] The search unit can analyze the user's past search history and apply the optimal search algorithm. For example, the optimal search algorithm can be applied based on keywords that the user has frequently searched for in the past. Also, it is possible to preferentially display highly relevant search results based on the user's past search history. Furthermore, it is possible to analyze the user's past search patterns and apply the optimal search algorithm. This improves search accuracy by applying the optimal search algorithm based on the past search history.

[0104] During matching, the matching unit can improve the accuracy of matching by analyzing the characteristics of the lost item in detail. For example, the color and shape of the lost item can be analyzed in detail to improve the accuracy of matching. The material and texture of the lost item can also be analyzed to improve the accuracy of matching. Furthermore, the size and weight of the lost item can be analyzed to improve the accuracy of matching. In this way, the accuracy of matching is improved by analyzing the characteristics in detail.

[0105] When identifying the lost item, the identification unit can identify the lost item by taking into consideration attribute information of the person who submitted the lost item. For example, the identification unit can identify the lost item by taking into consideration the age and gender of the person who submitted the lost item. The identification unit can also identify the lost item by taking into consideration the job and hobby of the person who submitted the lost item. Furthermore, the identification unit can identify the lost item by taking into consideration the place of residence and commute route of the person who submitted the lost item. In this way, the accuracy of identification is improved by taking into consideration the attribute information of the person who submitted the lost item.

[0106] The reception unit can analyze the user's social media activity and complement related information. For example, detailed information about the lost item can be complemented based on information shared by the user on social media. Also, related information can be automatically extracted from the user's social media activity to assist in input. Furthermore, detailed information about the lost item can be complemented based on location information posted by the user on social media. This improves the accuracy of input by complementing related information based on social media activity.

[0107] The search unit can estimate the user's emotions and determine the priority of search results based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed with priority. Also, if the user is relaxed, detailed information can be displayed with priority. Furthermore, if the user is in a hurry, the minimum necessary information can be displayed with priority. Thus, by determining the priority of search results according to the user's emotions, important information is displayed with priority.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit receives detailed information about the lost item from the user. The detailed information about the lost item from the user includes the type, color, shape, and characteristics of the item. For example, a ChatBot is used to receive detailed information about the lost item from the user. The ChatBot uses conversational AI to converse with the user and analyzes the user's input using natural language processing technology. Step 2: The search unit analyzes the information received by the reception unit and searches the police station database. The search unit uses natural language processing technology to analyze the user's input and performs morphological analysis, grammatical analysis, semantic analysis, etc. For example, the user's input is morphologically analyzed, keywords are extracted, and the database is searched. Grammatical analysis can also be used to analyze the grammatical structure of the user's input to improve search accuracy. Furthermore, semantic analysis can be used to analyze the meaning of the user's input to provide highly relevant search results. Step 3: The matching unit checks whether the newly received lost item at the police station matches the information in the existing database. Image recognition technology is used to analyze the characteristics of the newly received lost item and match it with the database. For example, technologies such as object detection, facial recognition, and feature extraction are used. The image of the newly received black wallet is analyzed using object detection technology and matched with the information on the black wallet registered in the database. In addition, facial recognition technology and feature extraction technology can be used to analyze the characteristics of the lost item in detail, improving the accuracy of matching. Step 4: The identification unit identifies the owner based on the My Number Card information. The My Number Card information is securely matched using encryption technology. For example, technologies such as AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and hash functions are used. The My Number Card information is encrypted using AES and securely matched with a database. It is also possible to encrypt and match the My Number Card information using RSA. Furthermore, it is also possible to hash the My Number Card information using a hash function and securely match it.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned reception unit, search unit, collation unit, and identification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives detailed information on the lost item from the user. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's input using natural language processing technology and searches the police station database. The collation unit analyzes the characteristics of the newly received lost item using, for example, the camera 42 of the smart device 14, and compares it with the database. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and uses encryption technology to safely collate the My Number card information and identify the owner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned reception unit, search unit, collation unit, and identification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives detailed information on the lost item from the user. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's input using natural language processing technology and searches the police station database. The collation unit analyzes the characteristics of the newly received lost item using, for example, the camera 42 of the smart glasses 214, and compares it with the database. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and uses encryption technology to safely collate the My Number card information and identify the owner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned reception unit, search unit, collation unit, and identification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives detailed information on lost items from a user. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's input using natural language processing technology and searches the police station database. The collation unit analyzes the characteristics of a newly received lost item using, for example, the camera 42 of the headset type terminal 314, and compares it with the database. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and uses encryption technology to safely collate My Number card information and identify the owner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned reception unit, search unit, collation unit, and identification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives detailed information on the lost item from the user. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's input using natural language processing technology and searches the police station database. The collation unit analyzes the characteristics of the newly received lost item using, for example, the camera 42 of the robot 414, and compares it with the database. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and uses encryption technology to safely collate the My Number card information and identify the owner. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0172] In addition, 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 upon request from the data processing device 12.

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

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

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

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0181] (Appendix 1) a reception unit for receiving detailed information about the lost item from a user; a search unit that analyzes the information received by the reception unit and searches a database of a police station; A matching unit that checks whether a new lost item received at the police station matches with information in the existing database; An identification unit that identifies the owner of the lost item based on the My Number card information. A system characterized by: (Appendix 2) The reception unit is Use ChatBot to receive details of lost items from users 2. The system of claim 1, (Appendix 3) The search unit is It uses natural language processing technology to analyze user input and search the police department database. 2. The system of claim 1, (Appendix 4) The collation unit is Using image recognition technology, the characteristics of newly received lost items are analyzed and compared with the database. 2. The system of claim 1, (Appendix 5) The identification unit is Using encryption technology, the My Number card information is securely matched to identify the owner 2. The system of claim 1, (Appendix 6) The collation unit is If a matching lost item is found, a notification will be automatically sent to the user's smartphone. 2. The system of claim 1, (Appendix 7) The reception unit is Inferring the user's emotions and adjusting the input method for details of lost items based on the inferred user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past inquiry history and provide the optimal information input format 2. The system of claim 1, (Appendix 9) The reception unit is Provide input assistance based on the user's current situation or environment when entering details about lost items 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize input information based on the estimated user's emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering details of lost items, the system will prioritize relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When you enter details of a lost item, we analyze your social media activity to complete it with relevant information 2. The system of claim 1, (Appendix 13) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The search unit is When searching, it refers to your past search history and applies the most appropriate search algorithm 2. The system of claim 1, (Appendix 15) The search unit is When searching, apply different search techniques depending on the category of lost items 2. The system of claim 1, (Appendix 16) The search unit is Infer user sentiment and prioritize search results based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The search unit is When searching, prioritize search results based on when the lost item was submitted 2. The system of claim 1, (Appendix 18) The search unit is When searching, adjust the order of search results based on the relevance of the lost item 2. The system of claim 1, (Appendix 19) The collation unit is Inferring a user's emotions and adjusting the way in which matching results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The collation unit is When matching, the characteristics of the lost item are analyzed in detail to improve matching accuracy. 2. The system of claim 1, (Appendix 21) The collation unit is A user's emotion is estimated, and a priority order for matching results is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The collation unit is When matching, the geographical distribution of lost items is taken into account. 2. The system of claim 1, (Appendix 23) The collation unit is When matching, refer to related literature on lost items to improve matching accuracy. 2. The system of claim 1, (Appendix 24) The identification unit is Inferring a user's emotion and adjusting the way certain results are displayed based on the inferred user emotion 2. The system of claim 1, (Appendix 25) The identification unit is When identifying, analyze the details of the My Number card information to improve the accuracy of identification. 2. The system of claim 1, (Appendix 26) The identification unit is When identifying a lost item, the attribute information of the person who submitted the lost item is taken into consideration. 2. The system of claim 1, (Appendix 27) The identification unit is Inferring user sentiment and prioritizing specific results based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The identification unit is When identifying lost items, the geographical distribution of the items is taken into account. 2. The system of claim 1, (Appendix 29) The identification unit is When identifying lost items, refer to related literature to improve the accuracy of the identification. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving detailed information about the lost item from a user; a search unit that analyzes the information received by the reception unit and searches a database of a police station; A matching unit that checks whether a new lost item received at the police station matches with information in the existing database; An identification unit that identifies the owner of the lost item based on the My Number card information. A system characterized by:

2. The reception unit is Use ChatBot to receive details of lost items from users 2. The system of claim 1 .

3. The search unit is It uses natural language processing technology to analyze user input and search the police department database.

2. The system of claim 1 .

4. The collation unit is Using image recognition technology, the characteristics of newly received lost items are analyzed and compared with the database.

2. The system of claim 1 .

5. The identification unit is Using encryption technology, the My Number card information is securely matched to identify the owner 2. The system of claim 1 .

6. The collation unit is If a matching lost item is found, a notification will be automatically sent to the user's smartphone.

2. The system of claim 1 .

7. The reception unit is Inferring the user's emotions and adjusting the input method for details of lost items based on the inferred user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past inquiry history and provide the optimal information input format 2. The system of claim 1 .

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