System

Abstract

A system is provided.SOLUTION: A system includes a means for a user to input a feature of a lost article, a means for a terminal to transmit input feature information to a server, a means for the server to store the feature information in a database, a means for the server to retrieve a lost article having high similarity based on the stored feature information by using a generation AI, a means for the server to notify a user of a retrieval result, a means for the user to select a corresponding lost article and perform a pick-up procedure, and a means for the server to re-post information of a lost article whose owner is not found even if the lost article is stored for a certain period.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The current lost and found management system has the problem that users have to repeatedly explain things to multiple contact points, which results in lost items often not being found. Another problem is that lost items that cannot be found are often discarded, which significantly increases the company's operational and storage management costs. There is a need to eliminate these inefficiencies and enable owners to be found quickly. [Means for solving the problem]

[0005] The present invention provides a system in which a user inputs the characteristics of a lost item, a terminal sends this information to a server, which stores and searches it, and uses a generative AI to search for similar lost items. The system also includes a series of processes in which the server notifies the user of the search results and the user completes the collection procedure. If the owner cannot be found after a certain period of storage, the system also includes a means for re-posting the information on a third-party platform or social networking site. This reduces the burden on users and allows the owner to be found quickly. It also reduces the company's operational costs and storage management costs.

[0006] A "user" is an individual or group that uses the system, and is responsible for inputting information about lost items and obtaining necessary information.

[0007] A "terminal" is a device operated by a user to input and transmit information about lost items, and includes smartphones, personal computers, tablets, etc.

[0008] A "server" is a computer system that receives and stores information sent from a terminal and performs the necessary processing.

[0009] "Characteristic information" is information relating to the identification of lost items, and is data including specific attributes such as color, shape, size, location, and time.

[0010] A "database" is a data storage system that allows the server to structure and store characteristic information about lost items.

[0011] "Generative AI" is an artificial intelligence technology that uses machine learning technology to compare input feature information with existing data and determine similarities.

[0012] The "search results" are a list of lost items that are determined to be highly similar, obtained after the generation AI compares and analyzes the characteristic information.

[0013] "Notifications" are messages sent from the server to inform users of search results and the status of collection procedures.

[0014] "Pick-up procedure" refers to the process that a user goes through to receive the lost item, and is a procedure that is notified via the server.

[0015] "Reposting" refers to the republication of information about lost items that have been stored for a certain period of time but whose owner has not been found, and can involve the use of third-party platforms or social media.

[0016] "Similarity judgment" is the process in which the generation AI compares the input feature information with the feature information in the database and evaluates the degree of match. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. The following describes an embodiment of the present invention.

[0039] 1. Enter lost item information

[0040] User:

[0041] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once this is complete, the user presses the "Submit" button to send the information to the server.

[0042] 2. Transmission and storage of information to the server

[0043] Device:

[0044] The terminal converts the characteristic information entered by the user into a data packet according to a format, and then transmits the data packet to the server. The transmission process can be carried out via the Internet or a dedicated network.

[0045] server:

[0046] The server receives the data packets sent from the device and stores them in a database. When storing the data, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0047] 3. Search for similar lost items

[0048] server:

[0049] The server runs a generative AI based on the information stored in the database. The AI ​​compares the feature information sent by the user with existing information in the database and calculates a similarity score. The similarity score is determined based on multiple parameters such as color, location, date and time. Information that scores above a certain level is listed as a search result.

[0050] 4. Notification of search results

[0051] server:

[0052] The server notifies the user of the search result list generated by the AI. Notifications are made according to the priority and number of search results set by the administrator, and the results can be viewed within the application.

[0053] Device:

[0054] The device receives the notification from the server and displays the search results list on the application screen. The user can check the list and select the corresponding lost item.

[0055] 5. Procedures for retrieving lost items

[0056] User:

[0057] Through the application, users select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0058] server:

[0059] The server receives a request from the user to collect the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0060] 6. Reposting if the owner cannot be found

[0061] server:

[0062] For lost items that have been stored for a certain period of time but whose owner has not been found, the server will repost them. To repost, the server will use third-party platforms or social media to send information converted into a specific format. The server will automatically connect to these external platforms and make the information public.

[0063] Examples:

[0064] 1. A user drops a blue wallet at Shinjuku Station.

[0065] 2. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0066] 3. The device converts this information into data packets and sends them to the server.

[0067] 4. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0068] 5. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0069] 6. If the user does not appear after the storage period has expired, the server will re-post the information on third-party platforms or social media to make it easier to find the owner.

[0070] In this way, the system of the present invention allows users to quickly find lost items, reduces business operations costs, and eliminates unnecessary waste.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user starts the application and opens the lost item information input form. The user enters the details of the lost item (color, shape, size, location, time, and other characteristics) and presses the "Submit" button.

[0074] Step 2:

[0075] The terminal converts the lost item information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[0076] Step 3:

[0077] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, it assigns a unique ID to each piece of characteristic information.

[0078] Step 4:

[0079] The server runs a generative AI based on the new information stored in the database. The generative AI compares the feature information sent by the user with existing data in the database and calculates a similarity score. If a similarity score above a certain level is found, the information is added to the search result list.

[0080] Step 5:

[0081] The server compiles the search results list generated by the generative AI and notifies the user, who can view the results within the application.

[0082] Step 6:

[0083] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they believe corresponds to their lost item.

[0084] Step 7:

[0085] The user selects the lost item from the list and presses the "Request for Collection" button. The user then enters any additional information or documents required for the collection procedure through the application.

[0086] Step 8:

[0087] The server receives a collection request from the user, notifies the lost and found center or storage management department of the received collection request, and requests additional information or identification documents as necessary.

[0088] Step 9:

[0089] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[0090] Step 10:

[0091] As part of the reposting process, the server publishes the information to third-party platforms and social networks, converting it into the appropriate format to increase the chances of finding the owner.

[0092] These steps will reduce the burden on users and create a system that quickly returns lost items to their owners.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In modern society, quickly identifying the owner of lost items and returning them without unnecessary effort or cost is a major challenge. Conventional methods require a lot of time and effort to manage and search for lost items, and if the owner cannot be found, the items are wasted. To improve this situation, a more efficient and reliable system for returning lost items to their owners is needed.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes: means for a user to input characteristics of a lost item; means for a terminal to send the input characteristic information to the server; means for the server to store the characteristic information in a database; means for the server to search for highly similar lost items using a generative AI model based on the stored characteristic information; means for the server to calculate a similarity score and list information with a certain score or higher; means for the server to notify the user of the search results; means for the terminal to display the search results to the user; means for the user to select the corresponding lost item and complete the collection procedure; and means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found. This allows users to quickly find lost items, reducing business operation costs and unnecessary waste.

[0098] "User" refers to an entity that uses the system to input characteristic information about lost items.

[0099] A "terminal" is a device operated by a user, including a smartphone, PC, etc. It has the role of transmitting characteristic information entered by the user to a server.

[0100] "Server" refers to the primary computer system that receives, stores, searches using the generative AI model, calculates similarity scores, notifies, and re-displays search results.

[0101] "Characteristic information" refers to detailed information for identifying a lost item, such as its color, shape, size, location, and time.

[0102] The term "data packet" refers to a data unit that a terminal generates by converting characteristic information into a specific format and transmits it to a server.

[0103] "Database" refers to a structured data storage for the server to store feature information.

[0104] A "generative AI model" refers to an artificial intelligence model that compares feature information from users with existing data to calculate similarity scores and generate search results.

[0105] The "similarity score" is a numerical representation of the similarity between lost items, calculated based on multiple parameters of the feature information. Information that achieves a certain score or higher will be listed as search results.

[0106] "Search results" refers to information on similar lost items, listed based on the similarity score calculated by the generative AI model.

[0107] "Notification" refers to the sending of information by the server to inform the user of search results, including email and in-application notifications.

[0108] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[0109] MODE FOR CARRYING OUT THE INVENTION

[0110] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. Specific embodiments of the present invention are described below.

[0111] Entering lost property information

[0112] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once the entry is complete, the user presses the "Submit" button to send the information to the device.

[0113] Sending and storing information on the server

[0114] The terminal converts the characteristic information entered by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0115] The server receives the data packets sent from the device and stores them in a database. When storing them in the database, each piece of characteristic information is assigned a unique ID. This ID prevents data collisions.

[0116] Search for similar lost items

[0117] The server searches the information stored in the database using a generative AI model (e.g., OpenAI® GPT-3®). This AI model compares the feature information sent by the user with existing information in the database and calculates a similarity score based on multiple parameters. Information that meets a certain score or higher is listed as a search result.

[0118] Search result notifications

[0119] The server notifies the user of the search result list generated by the AI. The notification is based on the priority and number of search results set by the administrator, and the results are made viewable within the application.

[0120] The device receives the notification from the server and displays the search result list on the application screen. The user can check the list and select the corresponding lost item.

[0121] Procedures for collecting lost items

[0122] Through the application, users can select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0123] The server receives a request from the user to retrieve the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0124] Reposting if owner cannot be found

[0125] The server automatically connects to these external platforms, converts the information into a specific format, and transmits it to the public.

[0126] Specific examples

[0127] If a user loses their blue wallet at Shinjuku Station, they will use the system as follows:

[0128] 1. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0129] 2. The device converts this information into data packets and sends them to the server.

[0130] 3. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0131] 4. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0132] 5. If the user does not show up after the retention period has expired, the server will repost the information on social media to make it easier to find the owner.

[0133] Prompt Sentence Examples

[0134] I lost a blue wallet at Shinjuku Station. It is blue, rectangular, and approximately 20cm x 10cm in size. I lost it near the east exit of Shinjuku Station around 10:00 AM on October 1, 2023. Please search for lost items based on this characteristic information.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Enter lost item information

[0137] The user launches a dedicated application on their smartphone or computer and inputs the characteristics of the lost item, including color, shape, size, location, time, and other characteristics. After completing the input, the user presses the "Send" button.

[0138] Input: Feature information entered by the user (e.g., color "blue", shape "rectangle", size "20cm x 10cm", location "Shinjuku Station East Exit", time "October 1, 2023, around 10:00 AM")

[0139] Output: Send instructions are sent to the terminal.

[0140] Specific operation: The user enters various characteristics into a form within the app and presses the "Submit" button.

[0141] Step 2: Format and send the information

[0142] The terminal converts the characteristic information input by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0143] Input: User characteristics information.

[0144] Output: Data packets sent to the server.

[0145] Specific operation: The terminal packetizes the feature information and sends it to the server via the Internet.

[0146] Step 3: Receiving and storing information

[0147] The server receives data packets sent from the device and stores them in a database. Each piece of characteristic information is assigned a unique ID, which is managed to avoid data collisions.

[0148] Input: Data packets sent from the terminal.

[0149] Output: Feature information stored in a database with a unique ID.

[0150] Specific operation: The server receives the packet, stores the information in a database, and generates and assigns an ID.

[0151] Step 4: Search for similar lost items

[0152] The server uses a generative AI model (e.g., OpenAI GPT-3) to search for similar lost items based on the information stored in the database. The AI ​​compares feature information and calculates a similarity score based on multiple parameters (color, shape, size, location, time, etc.).

[0153] Input: feature information stored in the database, new feature information sent by the user.

[0154] Output: A list of data with high similarity scores.

[0155] Specific operation: The generative AI searches for information in the database, calculates similarity scores, and creates a list.

[0156] Step 5: Notification of search results

[0157] The server notifies the user of the search result list generated by the AI. Notifications are made based on the priority and number of search results set by the administrator. The device receives this notification and displays the search result list on the application screen.

[0158] Input: The generated search result list.

[0159] Output: Notification to user, list displayed on terminal.

[0160] Specific operation: The server notifies the user of the result list, and the terminal displays it.

[0161] Step 6: Procedures for claiming lost property

[0162] The user selects the lost item from the search results list and presses the "Request Collection" button. The user then enters any additional information or documents required for the collection procedure, including identity verification and confirmation of the lost item details. The server then notifies the lost and found center or storage management department of the received collection request, and requests additional information or identity verification documents as necessary.

[0163] Input: User selections and additional information.

[0164] Output: Notification of pickup request, request for additional information.

[0165] Specific operation: The user makes a collection request, enters the necessary documents, and sends them. The server receives them and notifies the lost and found center.

[0166] Step 7: Reposting if the owner cannot be found

[0167] The server will repost any lost items that have been stored for a certain period of time but whose owner has not been found. This reposting process is carried out using third-party platforms and social media. The server automatically connects to these external platforms, converts the information into a specific format, sends it, and makes it public.

[0168] Input: Information on lost items that have not been returned for a specific period of time.

[0169] Output: Information reposted to social media and third-party platforms.

[0170] Specific operation: The server connects to the platform, publishes the necessary information, and searches for the owner again.

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] Conventional lost property return systems have had the problem of making it difficult for users to quickly and efficiently locate lost items. Lost items are often found in crowded places, such as public areas or commercial facilities, where there is a lot of jumbled information, making it difficult to find the correct owner, and it can take a long time. Even when surveillance cameras and other equipment are available, their use is limited, and manual verification is often required, making the process cumbersome and time-consuming.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to send the input characteristic information to the server, means for the server to save the characteristic information in a database, means for the server to search for similar lost items based on the saved characteristic information using a generation AI, means for the server to notify the user of the search results, means for the user to select the corresponding lost item and carry out the procedure for collection, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found, and means for linking with surveillance cameras installed in public areas and commercial facilities to identify the characteristics of lost items in real time, thereby enabling users to find lost items quickly and efficiently.

[0176] A "user" is a person who uses this system to input information about lost items and check search results.

[0177] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0178] A "server" is a computer system that receives information sent by users, stores it in a database, and performs the necessary processing.

[0179] "Characteristic information" refers to information about the color, shape, size, location, time, and other characteristics of the lost item.

[0180] "Generative AI" is an artificial intelligence technology that compares the similarity of input feature information with information in a database.

[0181] The "database" is an information management system that stores characteristic information of lost items stored on a server.

[0182] "Search results" are information about lost items that the generating AI determines to be highly similar from within the database.

[0183] A "surveillance camera" is a video capture device installed in public areas or commercial facilities.

[0184] The "collection procedure" is the procedure that the user goes through to actually receive the lost item that he or she has selected.

[0185] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared on third-party platforms or social media.

[0186] "Public areas" refer to places that are freely accessible to the general public, such as parks, train stations, and shopping centers.

[0187] "Commercial facilities" refers to places where goods and services are sold, such as shopping malls and supermarkets.

[0188] "Real time" refers to the time characteristic of processing occurring immediately without delay.

[0189] This invention provides a system in which a user inputs the characteristics of a lost item, and the system uses generative AI to search for similar data and quickly return the item to the owner. This section explains how to specifically implement the invention.

[0190] System Configuration

[0191] The system constituting the invention comprises the following elements:

[0192] 1. The terminal where the user types

[0193] Users launch the application using a smartphone, tablet, or PC and input the characteristics of the lost item, including color, shape, size, location, and time. These devices have iOS or ANDROID (registered trademark)-based applications installed.

[0194] 2. Server

[0195] The server receives the feature information sent by the user and stores it in a database. The server uses Django or Flask to function as a web server and MySQL (registered trademark) or PostgreSQL for database management. It also uses a generative AI model (using Tensorflow (registered trademark) or PyTorch) to compare the information in the database with the input feature information and calculate a similarity score.

[0196] 3. Surveillance cameras

[0197] Surveillance cameras installed in public areas and commercial facilities transmit images to a server in real time and are used to automatically identify the characteristics of lost items.

[0198] Data processing and calculation

[0199] User side

[0200] When a user uses the application to input the characteristics of a lost item, the device converts the information into data packets and sends them over the network to a server.

[0201] Server side

[0202] The server stores the received data packets in a database and assigns a unique ID to each feature. The stored data is used as input for a generative AI model and compared with other information in the database. The generative AI model uses machine learning techniques to calculate a similarity score and create a list of similar lost items. This list is then sent to the user. The user receives the search results, selects the relevant lost item, and completes the process of claiming it.

[0203] In addition, the server also has the ability to automatically repost information about lost items that have been stored for a certain period of time but whose owners cannot be found on third-party platforms or social media.

[0204] Specific examples

[0205] For example, if a user loses a blue wallet at a train station, the following steps are performed: The user uses their smartphone to enter the wallet's color (blue), shape, size, location (station), time of loss, etc. into an application and presses the send button. This information is sent from the device to a server, which stores the data in a database and uses a generative AI model to search for existing information about the blue wallet. The server then notifies the user of a list of similar lost items, which the user can review and proceed with the collection process.

[0206] Prompt Sentence Examples

[0207] "Example of characteristics to enter if you lost your blue wallet at Shinjuku Station"

[0208] Color: Blue

[0209] Shape: Wallet

[0210] Size: Small

[0211] Dropped at: Shinjuku Station

[0212] Drop time: 10:00

[0213] As described above, the system of the present invention enables users to quickly find lost items and improves the efficiency of lost item management in public areas and commercial facilities.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] The user inputs the characteristics of the lost item using a smartphone or tablet application. The information input by the user includes the color, shape, size, location, and time of the lost item.

[0217] input:

[0218] Color: Blue

[0219] Shape: Wallet

[0220] Size: Small

[0221] Location: Station

[0222] Time: 10:00

[0223] output:

[0224] The input data is stored in the terminal.

[0225] Step 2:

[0226] The terminal converts the characteristic information input by the user into a data packet and transmits it to the server, where the data packet contains the input characteristic information in a formatted form.

[0227] input:

[0228] User-entered feature information

[0229] output:

[0230] Data packets sent to the server

[0231] Step 3:

[0232] The server stores the received feature information in a database. The server analyzes the data packet, assigns a unique ID to each feature, and stores the information in a database.

[0233] input:

[0234] Data packets sent from the device

[0235] output:

[0236] Feature information stored in the database

[0237] Step 4:

[0238] The server runs a generative AI model based on the feature information stored in the database. The generative AI model compares the information stored in the database with the newly input information and calculates a similarity score. Similarity is evaluated based on parameters such as color, shape, size, location, and time.

[0239] input:

[0240] New feature information

[0241] Existing information in the database

[0242] output:

[0243] Search result list with similarity scores

[0244] Step 5:

[0245] The server notifies the user of information that has a certain score or higher based on the search result list created by the generative AI model. The user can check the search result list within the application.

[0246] input:

[0247] Search result list

[0248] output:

[0249] User Notification

[0250] Step 6:

[0251] The user checks the list of search results and selects the item they are looking for. The selected information is sent to the server to process the item. The user then completes procedures such as verifying their identity and providing additional information through the application.

[0252] input:

[0253] Search result list

[0254] User Selection

[0255] output:

[0256] Pickup procedure information sent to the server

[0257] Step 7:

[0258] The server receives the claim request, notifies the lost property management department, and, if necessary, requests the user to submit additional information or identification documents.

[0259] input:

[0260] Pick-up procedure information

[0261] output:

[0262] Notifying the Lost and Found Center

[0263] Requesting additional information from the user

[0264] Step 8:

[0265] When a lost item is stored on the server for a certain period of time but the owner cannot be found, the information is reposted on a third-party platform or social networking site, including the information converted into a specific format.

[0266] input:

[0267] Characteristic information whose storage period has expired

[0268] output:

[0269] Reposting information on third-party platforms or social media

[0270] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0271] This invention is a system that allows users to input the characteristics of lost items, and then uses a generative AI and an emotion engine to search for similar data, quickly returning the item to its owner. In addition, by combining this with the emotion engine, it also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0272] 1. Enter lost item information

[0273] User:

[0274] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item, and the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. Once the information has been entered, the user presses the "Submit" button.

[0275] 2. Transmission and storage of information to the server

[0276] Device:

[0277] The terminal converts the characteristic information and emotional state information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[0278] server:

[0279] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0280] 3. Search for similar lost items

[0281] server:

[0282] The server operates the generation AI and emotion engine based on new information stored in the database. The generation AI compares the feature information sent by the user with existing information in the database and calculates a similarity score. The emotion engine corrects the input emotion information and provides assistance such as prompting the user to reconfirm the information if they are impatient. If a similarity above a certain score is found, the information is added to the search result list.

[0283] 4. Notification of search results

[0284] server:

[0285] The server compiles the search results list generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[0286] Device:

[0287] The device receives the notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they think corresponds to their lost item.

[0288] 5. Procedures for retrieving lost items

[0289] User:

[0290] The user selects the lost item from the search results list through the application. The emotion engine monitors the user's emotions, and if the user is under stress during the process, the system will provide appropriate guidance and support. The user then presses the "Request Collection" button and enters any additional information or documents required for the collection process.

[0291] server:

[0292] The server receives a request from the user to retrieve the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. The emotion engine responds in a way that reduces the user's stress.

[0293] 6. Reposting if the owner cannot be found

[0294] server:

[0295] For lost items that remain unclaimed after a certain period of time, the server processes them for re-posting. This process involves sending information converted into a specific format via third-party platforms or social media. The emotion engine then provides appropriate follow-up to the user, reducing their anxiety about the item being re-posted.

[0296] Examples:

[0297] 1. A user drops a blue wallet at Shinjuku Station.

[0298] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time lost, etc.), and the emotion engine recognizes the emotion. When the user presses the "Send" button, the emotion engine detects anxious facial expressions and tone of voice and prompts the user to double-check the accuracy of the information.

[0299] 3. The device converts this information and emotion information into a data packet and sends it to the server.

[0300] 4. The server stores the received information in a database and searches the existing database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[0301] 5. The user checks the corresponding blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[0302] 6. If the user does not appear after the storage period has expired, the server will use its emotion engine to take measures to reduce anxiety when reposting the information on third-party platforms or social media.

[0303] This system not only reduces the burden on users and ensures that lost items are returned to their owners quickly, but also improves the overall user experience by incorporating an emotion engine.

[0304] The processing flow will be explained below.

[0305] Step 1:

[0306] The user launches the application and opens the lost item information entry form. The user enters the details of the item they lost (color, shape, size, location, time, and other characteristics). At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[0307] Step 2:

[0308] The terminal converts the user's input data (feature information and emotional state information) into a data packet according to the format, and sends the converted data packet to the server.

[0309] Step 3:

[0310] The server receives the data packet, checks its contents, stores the received information in a database, and assigns a unique ID to each feature. Emotional state information is also stored.

[0311] Step 4:

[0312] The server runs the generation AI based on the new information stored in the database. The generation AI compares the feature information sent by the user with the existing information in the database and calculates a similarity score. If a similarity above a certain score is found, the information is added to the search result list.

[0313] Step 5:

[0314] The server uses an emotion engine to analyze the feature information and the user's emotional state. If the emotional state indicates impatience or tension, it sends a notification to the user urging them to "reconfirm the accuracy of the information." After the user reconfirms, the information is updated.

[0315] Step 6:

[0316] The server compiles a list of search results generated by the AI ​​and notifies the user. The emotion engine creates a notification with a message in easy-to-understand language based on the user's emotional state.

[0317] Step 7:

[0318] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user checks the list and selects the information that they think corresponds to their lost item.

[0319] Step 8:

[0320] The user selects the lost item from the list and presses the "Request Collection" button. The user then enters any additional information or documents required to complete the collection process. The emotion engine monitors the user's emotional state and offers appropriate guidance and support if the user is under stress during the process.

[0321] Step 9:

[0322] The server receives a request from the user to collect the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. Based on the emotion engine, the server provides additional support to reduce the user's stress.

[0323] Step 10:

[0324] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[0325] Step 11:

[0326] As part of the reposting process, the server publishes the information to third-party platforms and social media to increase the chances of the owner being found. The server also converts the information into an appropriate format. The emotion engine then follows up with the user appropriately to reduce their anxiety about the reposting.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional lost item return systems search for similarities based only on characteristic information entered by the user, and do not take into account the user's emotional state, making them inadequate for users who are stressed or anxious. Another issue is that re-posting of lost items when the owner cannot be found is not automated. This results in a poor user experience and often results in lost items not being returned efficiently.

[0330] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input characteristics of a lost item and for an emotion engine to recognize the emotional state; means for a terminal to transmit the input characteristic information and emotional state information to the server; means for the server to store the characteristic information and emotion information in a database; means for the server to search for similar lost items using a generation AI based on the stored characteristic information; means for the server to notify the user of the search results together with a message corresponding to the emotion generated by the emotion engine; means for the user to select the corresponding lost item and carry out procedures for retrieval; and means for the server to re-post information about lost items whose owners have not been found even after being stored for a certain period of time. This enables efficient and prompt return of lost items while taking into account the user's emotional state.

[0331] "User" refers to an individual or corporation that uses the System to enter information about lost items and receive search results.

[0332] A "terminal" is a device used by a user to input information, and includes smartphones, personal computers, etc.

[0333] "Characteristic information" refers to detailed attribute information about the lost item, including elements such as color, shape, size, location, and time.

[0334] "Emotion engine" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0335] "Emotional state information" refers to data indicating the emotional state of the user as recognized by the emotion engine.

[0336] "Generative AI" refers to artificial intelligence that uses machine learning techniques to generate and search for highly similar data.

[0337] "Database" refers to a collection of information including characteristic information and emotion information stored on a server.

[0338] "Similarity score" refers to a number that indicates the similarity of the data, calculated by the generating AI based on the characteristic information of the lost item.

[0339] "Notification" refers to communication in the form of search results or messages sent from a server to a user.

[0340] "Collection Procedure" refers to the series of steps a User goes through to identify a lost item and confirm its ownership.

[0341] "Reposting" refers to the act of reposting information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[0342] This invention is a system that promptly returns lost items to their owners by using a generative AI and an emotion engine based on the user's input of characteristic information about the lost item. By combining this with the emotion engine, it also has the ability to recognize the user's emotional state and suggest appropriate responses.

[0343] The system has the following configuration:

[0344] 1. User inputs lost item information

[0345] The user launches a dedicated application on a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters detailed information such as color, shape, size, location, and time. The emotion engine also analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[0346] 2. Transmission and storage of information to the server

[0347] The device converts the characteristic information and emotional state information entered by the user into a data packet and sends it to the server. The server then checks the received data packet and stores its contents in a database. The characteristic information and emotional information are managed using a unique ID.

[0348] 3. Search for similar lost items

[0349] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the feature information with existing information in the database and calculates a similarity score. The emotion engine provides assistance to the user, such as reconfirming information, based on the emotion information.

[0350] 4. Notification of search results

[0351] The server compiles a list of search results generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[0352] 5. Procedures for retrieving lost items

[0353] The user selects the lost item from the search results list through the application and initiates the process of claiming it. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress during the process.

[0354] 6. Reposting if the owner cannot be found

[0355] If the owner cannot be found after a certain period of storage, the server will repost the content. The reposting information will be sent via third-party platforms or social media. The emotion engine will follow up to alleviate concerns about the reposting.

[0356] Specific examples

[0357] 1. A user drops a blue wallet at a train station.

[0358] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time of loss, etc.). The emotion engine recognizes emotions and, if it detects a panicked expression or tone of voice, prompts the user to reconfirm the information.

[0359] 3. The device converts the input information and emotion information into data packets and sends them to the server.

[0360] 4. The server stores the received information in a database and searches the database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[0361] 5. The user checks the blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[0362] 6. If the owner cannot be found after the storage period has expired, the server will repost the information on a third-party platform or social media, and the emotion engine will take measures to alleviate anxiety.

[0363] This system reduces the burden on users, enables quick return of lost items, and improves the user experience through an emotion engine.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step-by-step process instructions

[0366] Step 1:

[0367] The user launches a dedicated application from their smartphone or PC. They then fill out a form within the application to input the characteristics of the lost item (color, shape, size, location, time, etc.). At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. The input is the specific attribute information of the lost item and the user's emotional state. The output is the information entered into the form and the recognized emotional state.

[0368] Step 2:

[0369] The device converts the feature information and emotional state information entered by the user into a data packet. The generated data packet includes the user's input data and the emotional state recognized by the emotion engine. Specifically, the application on the device converts the data into JSON format and sends it to the server as an HTTP POST request. The input is the feature information and emotional state information entered by the user. The output is the data packet to be sent to the server.

[0370] Step 3:

[0371] The server receives data packets sent from the device. The received data is analyzed and stored in a database. A unique ID is assigned to the feature information and emotional state information, and they are managed uniquely in the database. The data packets sent from the device are obtained as input. The feature information and emotional state information stored in the database are obtained as output.

[0372] Step 4:

[0373] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the input feature information with existing data in the database and calculates a similarity score. The emotion engine assists with response based on emotion information. The inputs are existing information in the database and newly saved feature information and emotion information. The output is a similarity score and an assistance message.

[0374] Step 5:

[0375] The server compiles the search result list generated by the generation AI. At the same time, the emotion engine generates an easy-to-understand message based on the user's emotional information. This is sent to the user within the application as a notification. The inputs are the search result list and message obtained from the generation AI and emotion engine. The output is the message to be notified to the user and the search results.

[0376] Step 6:

[0377] The device receives a notification from the server and displays a list of search results on the application screen. The user checks this list and selects the information that corresponds to their lost item. The input is the list of search results and a message notified by the server. The output is the search results displayed in the application and the user's selection.

[0378] Step 7:

[0379] The user identifies the lost item and presses the "Request Collection" button to proceed. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user becomes stressed during the process. The input is the user's selection and emotional state. The output is additional information and guidance regarding the collection request.

[0380] Step 8:

[0381] The server receives a collection request from the user and notifies the lost and found center or storage management department of the received information. If necessary, it requests additional information or identification documents. The emotion engine responds to reduce the user's stress. The input is the collection request information sent by the user. The output is a collection request notification to the lost and found center or the department in charge, along with additional request information.

[0382] Step 9:

[0383] If the owner cannot be found after a certain period of storage, the server will repost the item. To repost the item, a third-party platform or social networking site is used, and the information is converted into a specific format and sent. The emotion engine will provide appropriate follow-up to the user to alleviate anxiety about the item being reposted. The input is information about lost items that have passed the storage period. The output is information about the item being reposted to a third-party platform or social networking site, along with a follow-up message.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] In modern society, lost items frequently occur in public places and other crowded locations, and the process of returning them takes a lot of time and effort. In these circumstances, there is a need for a method to accurately and quickly collate a wide variety of lost item information and return them to their owners. It is also important to reduce the stress and impatience felt by users searching for lost items and provide a smooth return process. To solve this problem, a new system that combines an emotion engine and generative AI is required.

[0387] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to transmit the input characteristic information and the user's emotional state to the server, means for the server to store the characteristic information and the emotional state in a database, means for the server to search for similar lost items based on the stored characteristic information using a generation AI, means for an emotion engine to recognize the user's emotional state and suggest an appropriate response based on the search result, means for the server to notify the user of the search result in a message corresponding to the user's emotional state, means for the user to select the corresponding lost item and carry out the collection procedure, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owner has not been found, and means for capturing characteristic information via a specific device and comparing it with the database. This allows the user to quickly and reliably find lost items and return them to their owners in a stress-free process.

[0388] "User" means any person or entity that uses the System to enter the characteristics of lost items and receive search results.

[0389] A "terminal" is a device operated by a user, such as a smartphone, a personal computer, or smart glasses.

[0390] "Characteristic information" refers to specific descriptions of the lost item, such as its color, shape, size, location, and time.

[0391] "Emotional state" is information that indicates the user's emotions, and refers to the mental state extracted from facial expressions, tone of voice, etc.

[0392] "Server" refers to a computer system that processes and stores information sent from a terminal and, if necessary, connects to a database.

[0393] "Database" refers to an electronic collection of information for storing and managing characteristic information and emotional states.

[0394] "Generative AI" refers to artificial intelligence that uses machine learning technology to analyze characteristic information and search for lost items that are highly similar.

[0395] An "emotion engine" refers to software that analyzes a user's emotional state in real time and suggests appropriate responses accordingly.

[0396] A "message" is information sent from a server to a user, and may be expressed in the form of text, audio, or an image.

[0397] "Pick-up procedure" refers to a series of operations that a user performs to retrieve a lost item based on a search result.

[0398] "Re-posting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared.

[0399] "Specific device" refers to special hardware equipment, such as smart glasses or a camera, that has the ability to capture characteristic information.

[0400] This invention is a system in which a user inputs the characteristics of a lost item, and based on that information, a generative AI and an emotion engine are used to search for similar data and quickly return it to the owner.Furthermore, it has a function that uses the emotion engine to recognize the user's emotional state and suggest appropriate responses.

[0401] The server receives the characteristic information and emotional state information input by the user from the device and stores them in a database according to a format. For example, a user captures an image of a lost item (e.g., a blue wallet) through smart glasses and inputs its characteristic information (color, shape, size, location, time, etc.). The input information is sent to the server via the device, and the server stores it in a database.

[0402] The server then uses the generation AI to match the stored feature information with existing data in the database to search for similar lost items. The generation AI uses machine learning techniques to calculate a similarity score for the feature information and identify items with high scores. During this process, the emotion engine recognizes the user's emotional state and prompts the user to reconfirm the accuracy of the information if they are feeling anxious.

[0403] The server generates search results and sends a notification to the user. The notification includes a message in a user-friendly format based on the emotional information analyzed by the emotion engine. The user then selects the lost item and initiates the procedure for retrieval. During this process, the emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress.

[0404] Once the user has completed the collection process, the server notifies the lost and found center or storage management department and requests any additional information or identification documents required. If the owner cannot be found, the server stores the lost item for a certain period of time and re-posts it on third-party platforms or social media. At this time, the emotion engine performs appropriate follow-up to alleviate the user's anxiety.

[0405] As a concrete example, let's say a user loses a blue wallet at a train station. The user inputs the wallet's characteristics through the smart glasses. The server receives the information and uses the generative AI to compare it with the database. When the matching blue wallet is found, the user is notified with a message based on the user's emotion. The user confirms the information and proceeds with the procedure to retrieve it, and the emotion engine responds to reduce the user's stress.

[0406] Examples of input prompts for generative AI models include:

[0407] "A blue wallet has been found. Search the database for lost items that match the color, shape, and size of this wallet. At the same time, the staff is feeling anxious. Use the emotion engine to suggest the best response."

[0408] In this way, the present invention not only reduces the burden on the user and allows lost items to be returned to their owners more quickly, but also improves the overall user experience by including an emotion engine.

[0409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0410] Step 1: User enters the characteristics of the lost item

[0411] The user puts on the smart glasses and uses the capture function to take a picture of the lost item (e.g., a blue wallet). The user then inputs characteristic information such as color, shape, size, location, and time of dropping into the application. This input information is simultaneously analyzed by the emotion engine to determine the user's emotional state (impatience or anxiety).

[0412] Input: Feature information of the lost item (color, shape, size, location, time), user's emotional information

[0413] Output: Data packets of feature information and emotion information

[0414] Step 2: The device sends a data packet to the server

[0415] The device converts the input feature information and emotion information into a data packet according to a format, which is then encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[0416] Input: feature information and emotion information data

[0417] Output: Data packets sent to the server

[0418] Step 3: The server receives and stores the data packet

[0419] The server receives the data packets sent from the device and checks their contents. When storing the received information in a database, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0420] Input: Data packets of feature information and emotion information

[0421] Output: Feature information and emotion information stored in a database

[0422] Step 4: The server uses the generated AI to search for similar lost items

[0423] The server runs a generative AI based on the feature and emotion information stored in the database. The generative AI analyzes the feature information and compares it with existing data in the database to calculate a similarity score. The emotion engine takes into account the user's emotional state and prompts reconfirmation as necessary.

[0424] Input: feature information and emotion information in the database

[0425] Output: A list of lost items with high similarity scores

[0426] Step 5: The server notifies the user of the search results

[0427] The server compiles a list of similar lost items generated by the AI ​​and creates a message in a format that is easy for the user to understand based on the emotional information analyzed by the emotion engine. This notification is sent to the user in a format that can be viewed within the application.

[0428] Input: List of similar lost items, user's emotional information

[0429] Output: Search result notification sent to user

[0430] Step 6: User selects the lost item and begins collection procedures

[0431] The user receives a notification and checks the list of lost items within the app. If the item is found, the user can proceed to claim it. During this process, an emotion engine monitors the user's emotional state and provides guidance and support if the user is under stress.

[0432] Input: Search result notification, user's emotional state

[0433] Output: User's selection of lost item and notification of completion of claim procedure

[0434] Step 7: The server notifies the lost and found center

[0435] Once the user has completed the collection process, the server notifies the lost and found center or storage department and manages the process of requesting additional information or identification documents, if necessary.

[0436] Input: Notification of completion of collection procedure

[0437] Output: Notification to the Lost and Found Center and request for necessary documents

[0438] Step 8: Reposting if the server owner cannot be found

[0439] If the owner cannot be found after a certain period of time, the server will repost the information on a third-party platform or social networking site, and the emotion engine will follow up and take measures to alleviate concerns about the reposting.

[0440] Input: Lost and found information for reposting

[0441] Output: Reposting notifications to third-party platforms and social media

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0443] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0445] [Second embodiment]

[0446] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0447] 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.

[0448] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0449] 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.

[0450] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0452] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0453] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0454] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0455] 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.

[0456] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0457] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0458] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. The following describes an embodiment of the present invention.

[0459] 1. Enter lost item information

[0460] User:

[0461] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once this is complete, the user presses the "Submit" button to send the information to the server.

[0462] 2. Transmission and storage of information to the server

[0463] Device:

[0464] The terminal converts the characteristic information entered by the user into a data packet according to a format, and then transmits the data packet to the server. The transmission process can be carried out via the Internet or a dedicated network.

[0465] server:

[0466] The server receives the data packets sent from the device and stores them in a database. When storing the data, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0467] 3. Search for similar lost items

[0468] server:

[0469] The server runs a generative AI based on the information stored in the database. The AI ​​compares the feature information sent by the user with existing information in the database and calculates a similarity score. The similarity score is determined based on multiple parameters such as color, location, date and time. Information that scores above a certain level is listed as a search result.

[0470] 4. Notification of search results

[0471] server:

[0472] The server notifies the user of the search result list generated by the AI. Notifications are made according to the priority and number of search results set by the administrator, and the results can be viewed within the application.

[0473] Device:

[0474] The device receives the notification from the server and displays the search results list on the application screen. The user can check the list and select the corresponding lost item.

[0475] 5. Procedures for retrieving lost items

[0476] User:

[0477] Through the application, users select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0478] server:

[0479] The server receives a request from the user to collect the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0480] 6. Reposting if the owner cannot be found

[0481] server:

[0482] For lost items that have been stored for a certain period of time but whose owner has not been found, the server will repost them. To repost, the server will use third-party platforms or social media to send information converted into a specific format. The server will automatically connect to these external platforms and make the information public.

[0483] Examples:

[0484] 1. A user drops a blue wallet at Shinjuku Station.

[0485] 2. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0486] 3. The device converts this information into data packets and sends them to the server.

[0487] 4. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0488] 5. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0489] 6. If the user does not appear after the storage period has expired, the server will re-post the information on third-party platforms or social media to make it easier to find the owner.

[0490] In this way, the system of the present invention allows users to quickly find lost items, reduces business operations costs, and eliminates unnecessary waste.

[0491] The processing flow will be explained below.

[0492] Step 1:

[0493] The user starts the application and opens the lost item information input form. The user enters the details of the lost item (color, shape, size, location, time, and other characteristics) and presses the "Submit" button.

[0494] Step 2:

[0495] The terminal converts the lost item information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[0496] Step 3:

[0497] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, it assigns a unique ID to each piece of characteristic information.

[0498] Step 4:

[0499] The server runs a generative AI based on the new information stored in the database. The generative AI compares the feature information sent by the user with existing data in the database and calculates a similarity score. If a similarity score above a certain level is found, the information is added to the search result list.

[0500] Step 5:

[0501] The server compiles the search results list generated by the generative AI and notifies the user, who can view the results within the application.

[0502] Step 6:

[0503] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they believe corresponds to their lost item.

[0504] Step 7:

[0505] The user selects the lost item from the list and presses the "Request for Collection" button. The user then enters any additional information or documents required for the collection procedure through the application.

[0506] Step 8:

[0507] The server receives a collection request from the user, notifies the lost and found center or storage management department of the received collection request, and requests additional information or identification documents as necessary.

[0508] Step 9:

[0509] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[0510] Step 10:

[0511] As part of the reposting process, the server publishes the information to third-party platforms and social networks, converting it into the appropriate format to increase the chances of finding the owner.

[0512] These steps will reduce the burden on users and create a system that quickly returns lost items to their owners.

[0513] Example 1

[0514] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0515] In modern society, quickly identifying the owner of lost items and returning them without unnecessary effort or cost is a major challenge. Conventional methods require a lot of time and effort to manage and search for lost items, and if the owner cannot be found, the items are wasted. To improve this situation, a more efficient and reliable system for returning lost items to their owners is needed.

[0516] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0517] In this invention, the server includes: means for a user to input characteristics of a lost item; means for a terminal to send the input characteristic information to the server; means for the server to store the characteristic information in a database; means for the server to search for highly similar lost items using a generative AI model based on the stored characteristic information; means for the server to calculate a similarity score and list information with a certain score or higher; means for the server to notify the user of the search results; means for the terminal to display the search results to the user; means for the user to select the corresponding lost item and complete the collection procedure; and means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found. This allows users to quickly find lost items, reducing business operation costs and unnecessary waste.

[0518] "User" refers to an entity that uses the system to input characteristic information about lost items.

[0519] A "terminal" is a device operated by a user, including a smartphone, PC, etc. It has the role of transmitting characteristic information entered by the user to a server.

[0520] "Server" refers to the primary computer system that receives, stores, searches using the generative AI model, calculates similarity scores, notifies, and re-displays search results.

[0521] "Characteristic information" refers to detailed information for identifying a lost item, such as its color, shape, size, location, and time.

[0522] The term "data packet" refers to a data unit that a terminal generates by converting characteristic information into a specific format and transmits it to a server.

[0523] "Database" refers to a structured data storage for the server to store feature information.

[0524] A "generative AI model" refers to an artificial intelligence model that compares feature information from users with existing data to calculate similarity scores and generate search results.

[0525] The "similarity score" is a numerical representation of the similarity between lost items, calculated based on multiple parameters of the feature information. Information that achieves a certain score or higher will be listed as search results.

[0526] "Search results" refers to information on similar lost items, listed based on the similarity score calculated by the generative AI model.

[0527] "Notification" refers to the sending of information by the server to inform the user of search results, including email and in-application notifications.

[0528] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[0529] MODE FOR CARRYING OUT THE INVENTION

[0530] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. Specific embodiments of the present invention are described below.

[0531] Entering lost property information

[0532] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once the entry is complete, the user presses the "Submit" button to send the information to the device.

[0533] Sending and storing information on the server

[0534] The terminal converts the characteristic information entered by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0535] The server receives the data packets sent from the device and stores them in a database. When storing them in the database, each piece of characteristic information is assigned a unique ID. This ID prevents data collisions.

[0536] Search for similar lost items

[0537] The server searches the information stored in the database using a generative AI model (such as OpenAI GPT-3). This AI model compares the feature information sent by the user with existing information in the database and calculates a similarity score based on multiple parameters. Information that meets a certain score or higher is listed as a search result.

[0538] Search result notifications

[0539] The server notifies the user of the search result list generated by the AI. The notification is based on the priority and number of search results set by the administrator, and the results are made viewable within the application.

[0540] The device receives the notification from the server and displays the search result list on the application screen. The user can check the list and select the corresponding lost item.

[0541] Procedures for collecting lost items

[0542] Through the application, users can select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0543] The server receives a request from the user to retrieve the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0544] Reposting if owner cannot be found

[0545] The server automatically connects to these external platforms, converts the information into a specific format, and transmits it to the public.

[0546] Specific examples

[0547] If a user loses their blue wallet at Shinjuku Station, they will use the system as follows:

[0548] 1. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0549] 2. The device converts this information into data packets and sends them to the server.

[0550] 3. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0551] 4. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0552] 5. If the user does not show up after the retention period has expired, the server will repost the information on social media to make it easier to find the owner.

[0553] Prompt Sentence Examples

[0554] I lost a blue wallet at Shinjuku Station. It is blue, rectangular, and approximately 20cm x 10cm in size. I lost it near the east exit of Shinjuku Station around 10:00 AM on October 1, 2023. Please search for lost items based on this characteristic information.

[0555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0556] Step 1: Enter lost item information

[0557] The user launches a dedicated application on their smartphone or computer and inputs the characteristics of the lost item, including color, shape, size, location, time, and other characteristics. After completing the input, the user presses the "Send" button.

[0558] Input: Feature information entered by the user (e.g., color "blue", shape "rectangle", size "20cm x 10cm", location "Shinjuku Station East Exit", time "October 1, 2023, around 10:00 AM")

[0559] Output: Send instructions are sent to the terminal.

[0560] Specific operation: The user enters various characteristics into a form within the app and presses the "Submit" button.

[0561] Step 2: Format and send the information

[0562] The terminal converts the characteristic information input by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0563] Input: User characteristics information.

[0564] Output: Data packets sent to the server.

[0565] Specific operation: The terminal packetizes the feature information and sends it to the server via the Internet.

[0566] Step 3: Receiving and storing information

[0567] The server receives data packets sent from the device and stores them in a database. Each piece of characteristic information is assigned a unique ID, which is managed to avoid data collisions.

[0568] Input: Data packets sent from the terminal.

[0569] Output: Feature information stored in a database with a unique ID.

[0570] Specific operation: The server receives the packet, stores the information in a database, and generates and assigns an ID.

[0571] Step 4: Search for similar lost items

[0572] The server uses a generative AI model (e.g., OpenAI GPT-3) to search for similar lost items based on the information stored in the database. The AI ​​compares feature information and calculates a similarity score based on multiple parameters (color, shape, size, location, time, etc.).

[0573] Input: feature information stored in the database, new feature information sent by the user.

[0574] Output: A list of data with high similarity scores.

[0575] Specific operation: The generative AI searches for information in the database, calculates similarity scores, and creates a list.

[0576] Step 5: Notification of search results

[0577] The server notifies the user of the search result list generated by the AI. Notifications are made based on the priority and number of search results set by the administrator. The device receives this notification and displays the search result list on the application screen.

[0578] Input: The generated search result list.

[0579] Output: Notification to user, list displayed on terminal.

[0580] Specific operation: The server notifies the user of the result list, and the terminal displays it.

[0581] Step 6: Procedures for claiming lost property

[0582] The user selects the lost item from the search results list and presses the "Request Collection" button. The user then enters any additional information or documents required for the collection procedure, including identity verification and confirmation of the lost item details. The server then notifies the lost and found center or storage management department of the received collection request, and requests additional information or identity verification documents as necessary.

[0583] Input: User selections and additional information.

[0584] Output: Notification of pickup request, request for additional information.

[0585] Specific operation: The user makes a collection request, enters the necessary documents, and sends them. The server receives them and notifies the lost and found center.

[0586] Step 7: Reposting if the owner cannot be found

[0587] The server will repost any lost items that have been stored for a certain period of time but whose owner has not been found. This reposting process is carried out using third-party platforms and social media. The server automatically connects to these external platforms, converts the information into a specific format, sends it, and makes it public.

[0588] Input: Information on lost items that have not been returned for a specific period of time.

[0589] Output: Information reposted to social media and third-party platforms.

[0590] Specific operation: The server connects to the platform, publishes the necessary information, and searches for the owner again.

[0591] (Application example 1)

[0592] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] Conventional lost property return systems have had the problem of making it difficult for users to quickly and efficiently locate lost items. Lost items are often found in crowded places, such as public areas or commercial facilities, where there is a lot of jumbled information, making it difficult to find the correct owner, and it can take a long time. Even when surveillance cameras and other equipment are available, their use is limited, and manual verification is often required, making the process cumbersome and time-consuming.

[0594] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0595] In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to send the input characteristic information to the server, means for the server to save the characteristic information in a database, means for the server to search for similar lost items based on the saved characteristic information using a generation AI, means for the server to notify the user of the search results, means for the user to select the corresponding lost item and carry out the procedure for collection, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found, and means for linking with surveillance cameras installed in public areas and commercial facilities to identify the characteristics of lost items in real time, thereby enabling users to find lost items quickly and efficiently.

[0596] A "user" is a person who uses this system to input information about lost items and check search results.

[0597] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0598] A "server" is a computer system that receives information sent by users, stores it in a database, and performs the necessary processing.

[0599] "Characteristic information" refers to information about the color, shape, size, location, time, and other characteristics of the lost item.

[0600] "Generative AI" is an artificial intelligence technology that compares the similarity of input feature information with information in a database.

[0601] The "database" is an information management system that stores characteristic information of lost items stored on a server.

[0602] "Search results" are information about lost items that the generating AI determines to be highly similar from within the database.

[0603] A "surveillance camera" is a video capture device installed in public areas or commercial facilities.

[0604] The "collection procedure" is the procedure that the user goes through to actually receive the lost item that he or she has selected.

[0605] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared on third-party platforms or social media.

[0606] "Public areas" refer to places that are freely accessible to the general public, such as parks, train stations, and shopping centers.

[0607] "Commercial facilities" refers to places where goods and services are sold, such as shopping malls and supermarkets.

[0608] "Real time" refers to the time characteristic of processing occurring immediately without delay.

[0609] This invention provides a system in which a user inputs the characteristics of a lost item, and the system uses generative AI to search for similar data and quickly return the item to the owner. This section explains how to specifically implement the invention.

[0610] System Configuration

[0611] The system constituting the invention comprises the following elements:

[0612] 1. The terminal where the user types

[0613] Users launch the application using a smartphone, tablet, or PC and input the characteristics of the lost item, such as color, shape, size, location, and time. These devices have the iOS or Android-based application installed.

[0614] 2. Server

[0615] The server receives the feature information sent by the user and stores it in a database. The server uses Django or Flask as a web server and MySQL or PostgreSQL for database management. It also uses a generative AI model (using TensorFlow or PyTorch) to compare the information in the database with the input feature information and calculate a similarity score.

[0616] 3. Surveillance cameras

[0617] Surveillance cameras installed in public areas and commercial facilities transmit images to a server in real time and are used to automatically identify the characteristics of lost items.

[0618] Data processing and calculation

[0619] User side

[0620] When a user uses the application to input the characteristics of a lost item, the device converts the information into data packets and sends them over the network to a server.

[0621] Server side

[0622] The server stores the received data packets in a database and assigns a unique ID to each feature. The stored data is used as input for a generative AI model and compared with other information in the database. The generative AI model uses machine learning techniques to calculate a similarity score and create a list of similar lost items. This list is then sent to the user. The user receives the search results, selects the relevant lost item, and completes the process of claiming it.

[0623] In addition, the server also has the ability to automatically repost information about lost items that have been stored for a certain period of time but whose owners cannot be found on third-party platforms or social media.

[0624] Specific examples

[0625] For example, if a user loses a blue wallet at a train station, the following steps are performed: The user uses their smartphone to enter the wallet's color (blue), shape, size, location (station), time of loss, etc. into an application and presses the send button. This information is sent from the device to a server, which stores the data in a database and uses a generative AI model to search for existing information about the blue wallet. The server then notifies the user of a list of similar lost items, which the user can review and proceed with the collection process.

[0626] Prompt Sentence Examples

[0627] "Example of characteristics to enter if you lost your blue wallet at Shinjuku Station"

[0628] Color: Blue

[0629] Shape: Wallet

[0630] Size: Small

[0631] Dropped at: Shinjuku Station

[0632] Drop time: 10:00

[0633] As described above, the system of the present invention enables users to quickly find lost items and improves the efficiency of lost item management in public areas and commercial facilities.

[0634] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0635] Step 1:

[0636] The user inputs the characteristics of the lost item using a smartphone or tablet application. The information input by the user includes the color, shape, size, location, and time of the lost item.

[0637] input:

[0638] Color: Blue

[0639] Shape: Wallet

[0640] Size: Small

[0641] Location: Station

[0642] Time: 10:00

[0643] output:

[0644] The input data is stored in the terminal.

[0645] Step 2:

[0646] The terminal converts the characteristic information input by the user into a data packet and transmits it to the server, where the data packet contains the input characteristic information in a formatted form.

[0647] input:

[0648] User-entered feature information

[0649] output:

[0650] Data packets sent to the server

[0651] Step 3:

[0652] The server stores the received feature information in a database. The server analyzes the data packet, assigns a unique ID to each feature, and stores the information in a database.

[0653] input:

[0654] Data packets sent from the device

[0655] output:

[0656] Feature information stored in the database

[0657] Step 4:

[0658] The server runs a generative AI model based on the feature information stored in the database. The generative AI model compares the information stored in the database with the newly input information and calculates a similarity score. Similarity is evaluated based on parameters such as color, shape, size, location, and time.

[0659] input:

[0660] New feature information

[0661] Existing information in the database

[0662] output:

[0663] Search result list with similarity scores

[0664] Step 5:

[0665] The server notifies the user of information that has a certain score or higher based on the search result list created by the generative AI model. The user can check the search result list within the application.

[0666] input:

[0667] Search result list

[0668] output:

[0669] User Notification

[0670] Step 6:

[0671] The user checks the list of search results and selects the item they are looking for. The selected information is sent to the server to process the item. The user then completes procedures such as verifying their identity and providing additional information through the application.

[0672] input:

[0673] Search result list

[0674] User Selection

[0675] output:

[0676] Pickup procedure information sent to the server

[0677] Step 7:

[0678] The server receives the claim request, notifies the lost property management department, and, if necessary, requests the user to submit additional information or identification documents.

[0679] input:

[0680] Pick-up procedure information

[0681] output:

[0682] Notifying the Lost and Found Center

[0683] Requesting additional information from the user

[0684] Step 8:

[0685] When a lost item is stored on the server for a certain period of time but the owner cannot be found, the information is reposted on a third-party platform or social networking site, including the information converted into a specific format.

[0686] input:

[0687] Characteristic information whose storage period has expired

[0688] output:

[0689] Reposting information on third-party platforms or social media

[0690] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0691] This invention is a system that allows users to input the characteristics of lost items, and then uses a generative AI and an emotion engine to search for similar data, quickly returning the item to its owner. In addition, by combining this with the emotion engine, it also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0692] 1. Enter lost item information

[0693] User:

[0694] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item, and the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. Once the information has been entered, the user presses the "Submit" button.

[0695] 2. Transmission and storage of information to the server

[0696] Device:

[0697] The terminal converts the characteristic information and emotional state information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[0698] server:

[0699] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0700] 3. Search for similar lost items

[0701] server:

[0702] The server operates the generation AI and emotion engine based on new information stored in the database. The generation AI compares the feature information sent by the user with existing information in the database and calculates a similarity score. The emotion engine corrects the input emotion information and provides assistance such as prompting the user to reconfirm the information if they are impatient. If a similarity above a certain score is found, the information is added to the search result list.

[0703] 4. Notification of search results

[0704] server:

[0705] The server compiles the search results list generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[0706] Device:

[0707] The device receives the notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they think corresponds to their lost item.

[0708] 5. Procedures for retrieving lost items

[0709] User:

[0710] The user selects the lost item from the search results list through the application. The emotion engine monitors the user's emotions, and if the user is under stress during the process, the system will provide appropriate guidance and support. The user then presses the "Request Collection" button and enters any additional information or documents required for the collection process.

[0711] server:

[0712] The server receives a request from the user to retrieve the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. The emotion engine responds in a way that reduces the user's stress.

[0713] 6. Reposting if the owner cannot be found

[0714] server:

[0715] For lost items that remain unclaimed after a certain period of time, the server processes them for re-posting. This process involves sending information converted into a specific format via third-party platforms or social media. The emotion engine then provides appropriate follow-up to the user, reducing their anxiety about the item being re-posted.

[0716] Examples:

[0717] 1. A user drops a blue wallet at Shinjuku Station.

[0718] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time lost, etc.), and the emotion engine recognizes the emotion. When the user presses the "Send" button, the emotion engine detects anxious facial expressions and tone of voice and prompts the user to double-check the accuracy of the information.

[0719] 3. The device converts this information and emotion information into a data packet and sends it to the server.

[0720] 4. The server stores the received information in a database and searches the existing database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[0721] 5. The user checks the corresponding blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[0722] 6. If the user does not appear after the storage period has expired, the server will use its emotion engine to take measures to reduce anxiety when reposting the information on third-party platforms or social media.

[0723] This system not only reduces the burden on users and ensures that lost items are returned to their owners quickly, but also improves the overall user experience by incorporating an emotion engine.

[0724] The processing flow will be explained below.

[0725] Step 1:

[0726] The user launches the application and opens the lost item information entry form. The user enters the details of the item they lost (color, shape, size, location, time, and other characteristics). At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[0727] Step 2:

[0728] The terminal converts the user's input data (feature information and emotional state information) into a data packet according to the format, and sends the converted data packet to the server.

[0729] Step 3:

[0730] The server receives the data packet, checks its contents, stores the received information in a database, and assigns a unique ID to each feature. Emotional state information is also stored.

[0731] Step 4:

[0732] The server runs the generation AI based on the new information stored in the database. The generation AI compares the feature information sent by the user with the existing information in the database and calculates a similarity score. If a similarity above a certain score is found, the information is added to the search result list.

[0733] Step 5:

[0734] The server uses an emotion engine to analyze the feature information and the user's emotional state. If the emotional state indicates impatience or tension, it sends a notification to the user urging them to "reconfirm the accuracy of the information." After the user reconfirms, the information is updated.

[0735] Step 6:

[0736] The server compiles a list of search results generated by the AI ​​and notifies the user. The emotion engine creates a notification with a message in easy-to-understand language based on the user's emotional state.

[0737] Step 7:

[0738] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user checks the list and selects the information that they think corresponds to their lost item.

[0739] Step 8:

[0740] The user selects the lost item from the list and presses the "Request Collection" button. The user then enters any additional information or documents required to complete the collection process. The emotion engine monitors the user's emotional state and offers appropriate guidance and support if the user is under stress during the process.

[0741] Step 9:

[0742] The server receives a request from the user to collect the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. Based on the emotion engine, the server provides additional support to reduce the user's stress.

[0743] Step 10:

[0744] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[0745] Step 11:

[0746] As part of the reposting process, the server publishes the information to third-party platforms and social media to increase the chances of the owner being found. The server also converts the information into an appropriate format. The emotion engine then follows up with the user appropriately to reduce their anxiety about the reposting.

[0747] Example 2

[0748] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0749] Conventional lost item return systems search for similarities based only on characteristic information entered by the user, and do not take into account the user's emotional state, making them inadequate for users who are stressed or anxious. Another issue is that re-posting of lost items when the owner cannot be found is not automated. This results in a poor user experience and often results in lost items not being returned efficiently.

[0750] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input characteristics of a lost item and for an emotion engine to recognize the emotional state; means for a terminal to transmit the input characteristic information and emotional state information to the server; means for the server to store the characteristic information and emotion information in a database; means for the server to search for similar lost items using a generation AI based on the stored characteristic information; means for the server to notify the user of the search results together with a message corresponding to the emotion generated by the emotion engine; means for the user to select the corresponding lost item and carry out procedures for retrieval; and means for the server to re-post information about lost items whose owners have not been found even after being stored for a certain period of time. This enables efficient and prompt return of lost items while taking into account the user's emotional state.

[0751] "User" refers to an individual or corporation that uses the System to enter information about lost items and receive search results.

[0752] A "terminal" is a device used by a user to input information, and includes smartphones, personal computers, etc.

[0753] "Characteristic information" refers to detailed attribute information about the lost item, including elements such as color, shape, size, location, and time.

[0754] "Emotion engine" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0755] "Emotional state information" refers to data indicating the emotional state of the user as recognized by the emotion engine.

[0756] "Generative AI" refers to artificial intelligence that uses machine learning techniques to generate and search for highly similar data.

[0757] "Database" refers to a collection of information including characteristic information and emotion information stored on a server.

[0758] "Similarity score" refers to a number that indicates the similarity of the data, calculated by the generating AI based on the characteristic information of the lost item.

[0759] "Notification" refers to communication in the form of search results or messages sent from a server to a user.

[0760] "Collection Procedure" refers to the series of steps a User goes through to identify a lost item and confirm its ownership.

[0761] "Reposting" refers to the act of reposting information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[0762] This invention is a system that promptly returns lost items to their owners by using a generative AI and an emotion engine based on the user's input of characteristic information about the lost item. By combining this with the emotion engine, it also has the ability to recognize the user's emotional state and suggest appropriate responses.

[0763] The system has the following configuration:

[0764] 1. User inputs lost item information

[0765] The user launches a dedicated application on a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters detailed information such as color, shape, size, location, and time. The emotion engine also analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[0766] 2. Transmission and storage of information to the server

[0767] The device converts the characteristic information and emotional state information entered by the user into a data packet and sends it to the server. The server then checks the received data packet and stores its contents in a database. The characteristic information and emotional information are managed using a unique ID.

[0768] 3. Search for similar lost items

[0769] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the feature information with existing information in the database and calculates a similarity score. The emotion engine provides assistance to the user, such as reconfirming information, based on the emotion information.

[0770] 4. Notification of search results

[0771] The server compiles a list of search results generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[0772] 5. Procedures for retrieving lost items

[0773] The user selects the lost item from the search results list through the application and initiates the process of claiming it. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress during the process.

[0774] 6. Reposting if the owner cannot be found

[0775] If the owner cannot be found after a certain period of storage, the server will repost the content. The reposting information will be sent via third-party platforms or social media. The emotion engine will follow up to alleviate concerns about the reposting.

[0776] Specific examples

[0777] 1. A user drops a blue wallet at a train station.

[0778] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time of loss, etc.). The emotion engine recognizes emotions and, if it detects a panicked expression or tone of voice, prompts the user to reconfirm the information.

[0779] 3. The device converts the input information and emotion information into data packets and sends them to the server.

[0780] 4. The server stores the received information in a database and searches the database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[0781] 5. The user checks the blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[0782] 6. If the owner cannot be found after the storage period has expired, the server will repost the information on a third-party platform or social media, and the emotion engine will take measures to alleviate anxiety.

[0783] This system reduces the burden on users, enables quick return of lost items, and improves the user experience through an emotion engine.

[0784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0785] Step-by-step process instructions

[0786] Step 1:

[0787] The user launches a dedicated application from their smartphone or PC. They then fill out a form within the application to input the characteristics of the lost item (color, shape, size, location, time, etc.). At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. The input is the specific attribute information of the lost item and the user's emotional state. The output is the information entered into the form and the recognized emotional state.

[0788] Step 2:

[0789] The device converts the feature information and emotional state information entered by the user into a data packet. The generated data packet includes the user's input data and the emotional state recognized by the emotion engine. Specifically, the application on the device converts the data into JSON format and sends it to the server as an HTTP POST request. The input is the feature information and emotional state information entered by the user. The output is the data packet to be sent to the server.

[0790] Step 3:

[0791] The server receives data packets sent from the device. The received data is analyzed and stored in a database. A unique ID is assigned to the feature information and emotional state information, and they are managed uniquely in the database. The data packets sent from the device are obtained as input. The feature information and emotional state information stored in the database are obtained as output.

[0792] Step 4:

[0793] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the input feature information with existing data in the database and calculates a similarity score. The emotion engine assists with response based on emotion information. The inputs are existing information in the database and newly saved feature information and emotion information. The output is a similarity score and an assistance message.

[0794] Step 5:

[0795] The server compiles the search result list generated by the generation AI. At the same time, the emotion engine generates an easy-to-understand message based on the user's emotional information. This is sent to the user within the application as a notification. The inputs are the search result list and message obtained from the generation AI and emotion engine. The output is the message to be notified to the user and the search results.

[0796] Step 6:

[0797] The device receives a notification from the server and displays a list of search results on the application screen. The user checks this list and selects the information that corresponds to their lost item. The input is the list of search results and a message notified by the server. The output is the search results displayed in the application and the user's selection.

[0798] Step 7:

[0799] The user identifies the lost item and presses the "Request Collection" button to proceed. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user becomes stressed during the process. The input is the user's selection and emotional state. The output is additional information and guidance regarding the collection request.

[0800] Step 8:

[0801] The server receives a collection request from the user and notifies the lost and found center or storage management department of the received information. If necessary, it requests additional information or identification documents. The emotion engine responds to reduce the user's stress. The input is the collection request information sent by the user. The output is a collection request notification to the lost and found center or the department in charge, along with additional request information.

[0802] Step 9:

[0803] If the owner cannot be found after a certain period of storage, the server will repost the item. To repost the item, a third-party platform or social networking site is used, and the information is converted into a specific format and sent. The emotion engine will provide appropriate follow-up to the user to alleviate anxiety about the item being reposted. The input is information about lost items that have passed the storage period. The output is information about the item being reposted to a third-party platform or social networking site, along with a follow-up message.

[0804] (Application example 2)

[0805] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0806] In modern society, lost items frequently occur in public places and other crowded locations, and the process of returning them takes a lot of time and effort. In these circumstances, there is a need for a method to accurately and quickly collate a wide variety of lost item information and return them to their owners. It is also important to reduce the stress and impatience felt by users searching for lost items and provide a smooth return process. To solve this problem, a new system that combines an emotion engine and generative AI is required.

[0807] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to transmit the input characteristic information and the user's emotional state to the server, means for the server to store the characteristic information and the emotional state in a database, means for the server to search for similar lost items based on the stored characteristic information using a generation AI, means for an emotion engine to recognize the user's emotional state and suggest an appropriate response based on the search result, means for the server to notify the user of the search result in a message corresponding to the user's emotional state, means for the user to select the corresponding lost item and carry out the collection procedure, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owner has not been found, and means for capturing characteristic information via a specific device and comparing it with the database. This allows the user to quickly and reliably find lost items and return them to their owners in a stress-free process.

[0808] "User" means any person or entity that uses the System to enter the characteristics of lost items and receive search results.

[0809] A "terminal" is a device operated by a user, such as a smartphone, a personal computer, or smart glasses.

[0810] "Characteristic information" refers to specific descriptions of the lost item, such as its color, shape, size, location, and time.

[0811] "Emotional state" is information that indicates the user's emotions, and refers to the mental state extracted from facial expressions, tone of voice, etc.

[0812] "Server" refers to a computer system that processes and stores information sent from a terminal and, if necessary, connects to a database.

[0813] "Database" refers to an electronic collection of information for storing and managing characteristic information and emotional states.

[0814] "Generative AI" refers to artificial intelligence that uses machine learning technology to analyze characteristic information and search for lost items that are highly similar.

[0815] An "emotion engine" refers to software that analyzes a user's emotional state in real time and suggests appropriate responses accordingly.

[0816] A "message" is information sent from a server to a user, and may be expressed in the form of text, audio, or an image.

[0817] "Pick-up procedure" refers to a series of operations that a user performs to retrieve a lost item based on a search result.

[0818] "Re-posting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared.

[0819] "Specific device" refers to special hardware equipment, such as smart glasses or a camera, that has the ability to capture characteristic information.

[0820] This invention is a system in which a user inputs the characteristics of a lost item, and based on that information, a generative AI and an emotion engine are used to search for similar data and quickly return it to the owner.Furthermore, it has a function that uses the emotion engine to recognize the user's emotional state and suggest appropriate responses.

[0821] The server receives the characteristic information and emotional state information input by the user from the device and stores them in a database according to a format. For example, a user captures an image of a lost item (e.g., a blue wallet) through smart glasses and inputs its characteristic information (color, shape, size, location, time, etc.). The input information is sent to the server via the device, and the server stores it in a database.

[0822] The server then uses the generation AI to match the stored feature information with existing data in the database to search for similar lost items. The generation AI uses machine learning techniques to calculate a similarity score for the feature information and identify items with high scores. During this process, the emotion engine recognizes the user's emotional state and prompts the user to reconfirm the accuracy of the information if they are feeling anxious.

[0823] The server generates search results and sends a notification to the user. The notification includes a message in a user-friendly format based on the emotional information analyzed by the emotion engine. The user then selects the lost item and initiates the procedure for retrieval. During this process, the emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress.

[0824] Once the user has completed the collection process, the server notifies the lost and found center or storage management department and requests any additional information or identification documents required. If the owner cannot be found, the server stores the lost item for a certain period of time and re-posts it on third-party platforms or social media. At this time, the emotion engine performs appropriate follow-up to alleviate the user's anxiety.

[0825] As a concrete example, let's say a user loses a blue wallet at a train station. The user inputs the wallet's characteristics through the smart glasses. The server receives the information and uses the generative AI to compare it with the database. When the matching blue wallet is found, the user is notified with a message based on the user's emotion. The user confirms the information and proceeds with the procedure to retrieve it, and the emotion engine responds to reduce the user's stress.

[0826] Examples of input prompts for generative AI models include:

[0827] "A blue wallet has been found. Search the database for lost items that match the color, shape, and size of this wallet. At the same time, the staff is feeling anxious. Use the emotion engine to suggest the best response."

[0828] In this way, the present invention not only reduces the burden on the user and allows lost items to be returned to their owners more quickly, but also improves the overall user experience by including an emotion engine.

[0829] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0830] Step 1: User enters the characteristics of the lost item

[0831] The user puts on the smart glasses and uses the capture function to take a picture of the lost item (e.g., a blue wallet). The user then inputs characteristic information such as color, shape, size, location, and time of dropping into the application. This input information is simultaneously analyzed by the emotion engine to determine the user's emotional state (impatience or anxiety).

[0832] Input: Feature information of the lost item (color, shape, size, location, time), user's emotional information

[0833] Output: Data packets of feature information and emotion information

[0834] Step 2: The device sends a data packet to the server

[0835] The device converts the input feature information and emotion information into a data packet according to a format, which is then encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[0836] Input: feature information and emotion information data

[0837] Output: Data packets sent to the server

[0838] Step 3: The server receives and stores the data packet

[0839] The server receives the data packets sent from the device and checks their contents. When storing the received information in a database, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0840] Input: Data packets of feature information and emotion information

[0841] Output: Feature information and emotion information stored in a database

[0842] Step 4: The server uses the generated AI to search for similar lost items

[0843] The server runs a generative AI based on the feature and emotion information stored in the database. The generative AI analyzes the feature information and compares it with existing data in the database to calculate a similarity score. The emotion engine takes into account the user's emotional state and prompts reconfirmation as necessary.

[0844] Input: feature information and emotion information in the database

[0845] Output: A list of lost items with high similarity scores

[0846] Step 5: The server notifies the user of the search results

[0847] The server compiles a list of similar lost items generated by the AI ​​and creates a message in a format that is easy for the user to understand based on the emotional information analyzed by the emotion engine. This notification is sent to the user in a format that can be viewed within the application.

[0848] Input: List of similar lost items, user's emotional information

[0849] Output: Search result notification sent to user

[0850] Step 6: User selects the lost item and begins collection procedures

[0851] The user receives a notification and checks the list of lost items within the app. If the item is found, the user can proceed to claim it. During this process, an emotion engine monitors the user's emotional state and provides guidance and support if the user is under stress.

[0852] Input: Search result notification, user's emotional state

[0853] Output: User's selection of lost item and notification of completion of claim procedure

[0854] Step 7: The server notifies the lost and found center

[0855] Once the user has completed the collection process, the server notifies the lost and found center or storage department and manages the process of requesting additional information or identification documents, if necessary.

[0856] Input: Notification of completion of collection procedure

[0857] Output: Notification to the Lost and Found Center and request for necessary documents

[0858] Step 8: Reposting if the server owner cannot be found

[0859] If the owner cannot be found after a certain period of time, the server will repost the information on a third-party platform or social networking site, and the emotion engine will follow up and take measures to alleviate concerns about the reposting.

[0860] Input: Lost and found information for reposting

[0861] Output: Reposting notifications to third-party platforms and social media

[0862] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0863] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0864] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0865] [Third embodiment]

[0866] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0867] 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.

[0868] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0869] 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.

[0870] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0871] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0872] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0873] 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.

[0874] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0875] 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.

[0876] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0877] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0878] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. The following describes an embodiment of the present invention.

[0879] 1. Enter lost item information

[0880] User:

[0881] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once this is complete, the user presses the "Submit" button to send the information to the server.

[0882] 2. Transmission and storage of information to the server

[0883] Device:

[0884] The terminal converts the characteristic information entered by the user into a data packet according to a format, and then transmits the data packet to the server. The transmission process can be carried out via the Internet or a dedicated network.

[0885] server:

[0886] The server receives the data packets sent from the device and stores them in a database. When storing the data, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[0887] 3. Search for similar lost items

[0888] server:

[0889] The server runs a generative AI based on the information stored in the database. The AI ​​compares the feature information sent by the user with existing information in the database and calculates a similarity score. The similarity score is determined based on multiple parameters such as color, location, date and time. Information that scores above a certain level is listed as a search result.

[0890] 4. Notification of search results

[0891] server:

[0892] The server notifies the user of the search result list generated by the AI. Notifications are made according to the priority and number of search results set by the administrator, and the results can be viewed within the application.

[0893] Device:

[0894] The device receives the notification from the server and displays the search results list on the application screen. The user can check the list and select the corresponding lost item.

[0895] 5. Procedures for retrieving lost items

[0896] User:

[0897] Through the application, users select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0898] server:

[0899] The server receives a request from the user to collect the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0900] 6. Reposting if the owner cannot be found

[0901] server:

[0902] For lost items that have been stored for a certain period of time but whose owner has not been found, the server will repost them. To repost, the server will use third-party platforms or social media to send information converted into a specific format. The server will automatically connect to these external platforms and make the information public.

[0903] Examples:

[0904] 1. A user drops a blue wallet at Shinjuku Station.

[0905] 2. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0906] 3. The device converts this information into data packets and sends them to the server.

[0907] 4. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0908] 5. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0909] 6. If the user does not appear after the storage period has expired, the server will re-post the information on third-party platforms or social media to make it easier to find the owner.

[0910] In this way, the system of the present invention allows users to quickly find lost items, reduces business operations costs, and eliminates unnecessary waste.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] The user starts the application and opens the lost item information input form. The user enters the details of the lost item (color, shape, size, location, time, and other characteristics) and presses the "Submit" button.

[0914] Step 2:

[0915] The terminal converts the lost item information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[0916] Step 3:

[0917] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, it assigns a unique ID to each piece of characteristic information.

[0918] Step 4:

[0919] The server runs a generative AI based on the new information stored in the database. The generative AI compares the feature information sent by the user with existing data in the database and calculates a similarity score. If a similarity score above a certain level is found, the information is added to the search result list.

[0920] Step 5:

[0921] The server compiles the search results list generated by the generative AI and notifies the user, who can view the results within the application.

[0922] Step 6:

[0923] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they believe corresponds to their lost item.

[0924] Step 7:

[0925] The user selects the lost item from the list and presses the "Request for Collection" button. The user then enters any additional information or documents required for the collection procedure through the application.

[0926] Step 8:

[0927] The server receives a collection request from the user, notifies the lost and found center or storage management department of the received collection request, and requests additional information or identification documents as necessary.

[0928] Step 9:

[0929] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[0930] Step 10:

[0931] As part of the reposting process, the server publishes the information to third-party platforms and social networks, converting it into the appropriate format to increase the chances of finding the owner.

[0932] These steps will reduce the burden on users and create a system that quickly returns lost items to their owners.

[0933] Example 1

[0934] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0935] In modern society, quickly identifying the owner of lost items and returning them without unnecessary effort or cost is a major challenge. Conventional methods require a lot of time and effort to manage and search for lost items, and if the owner cannot be found, the items are wasted. To improve this situation, a more efficient and reliable system for returning lost items to their owners is needed.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0937] In this invention, the server includes: means for a user to input characteristics of a lost item; means for a terminal to send the input characteristic information to the server; means for the server to store the characteristic information in a database; means for the server to search for highly similar lost items using a generative AI model based on the stored characteristic information; means for the server to calculate a similarity score and list information with a certain score or higher; means for the server to notify the user of the search results; means for the terminal to display the search results to the user; means for the user to select the corresponding lost item and complete the collection procedure; and means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found. This allows users to quickly find lost items, reducing business operation costs and unnecessary waste.

[0938] "User" refers to an entity that uses the system to input characteristic information about lost items.

[0939] A "terminal" is a device operated by a user, including a smartphone, PC, etc. It has the role of transmitting characteristic information entered by the user to a server.

[0940] "Server" refers to the primary computer system that receives, stores, searches using the generative AI model, calculates similarity scores, notifies, and re-displays search results.

[0941] "Characteristic information" refers to detailed information for identifying a lost item, such as its color, shape, size, location, and time.

[0942] The term "data packet" refers to a data unit that a terminal generates by converting characteristic information into a specific format and transmits it to a server.

[0943] "Database" refers to a structured data storage for the server to store feature information.

[0944] A "generative AI model" refers to an artificial intelligence model that compares feature information from users with existing data to calculate similarity scores and generate search results.

[0945] The "similarity score" is a numerical representation of the similarity between lost items, calculated based on multiple parameters of the feature information. Information that achieves a certain score or higher will be listed as search results.

[0946] "Search results" refers to information on similar lost items, listed based on the similarity score calculated by the generative AI model.

[0947] "Notification" refers to the sending of information by the server to inform the user of search results, including email and in-application notifications.

[0948] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[0949] MODE FOR CARRYING OUT THE INVENTION

[0950] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. Specific embodiments of the present invention are described below.

[0951] Entering lost property information

[0952] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once the entry is complete, the user presses the "Submit" button to send the information to the device.

[0953] Sending and storing information on the server

[0954] The terminal converts the characteristic information entered by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0955] The server receives the data packets sent from the device and stores them in a database. When storing them in the database, each piece of characteristic information is assigned a unique ID. This ID prevents data collisions.

[0956] Search for similar lost items

[0957] The server searches the information stored in the database using a generative AI model (such as OpenAI GPT-3). This AI model compares the feature information sent by the user with existing information in the database and calculates a similarity score based on multiple parameters. Information that meets a certain score or higher is listed as a search result.

[0958] Search result notifications

[0959] The server notifies the user of the search result list generated by the AI. The notification is based on the priority and number of search results set by the administrator, and the results are made viewable within the application.

[0960] The device receives the notification from the server and displays the search result list on the application screen. The user can check the list and select the corresponding lost item.

[0961] Procedures for collecting lost items

[0962] Through the application, users can select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[0963] The server receives a request from the user to retrieve the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[0964] Reposting if owner cannot be found

[0965] The server automatically connects to these external platforms, converts the information into a specific format, and transmits it to the public.

[0966] Specific examples

[0967] If a user loses their blue wallet at Shinjuku Station, they will use the system as follows:

[0968] 1. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[0969] 2. The device converts this information into data packets and sends them to the server.

[0970] 3. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[0971] 4. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[0972] 5. If the user does not show up after the retention period has expired, the server will repost the information on social media to make it easier to find the owner.

[0973] Prompt Sentence Examples

[0974] I lost a blue wallet at Shinjuku Station. It is blue, rectangular, and approximately 20cm x 10cm in size. I lost it near the east exit of Shinjuku Station around 10:00 AM on October 1, 2023. Please search for lost items based on this characteristic information.

[0975] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0976] Step 1: Enter lost item information

[0977] The user launches a dedicated application on their smartphone or computer and inputs the characteristics of the lost item, including color, shape, size, location, time, and other characteristics. After completing the input, the user presses the "Send" button.

[0978] Input: Feature information entered by the user (e.g., color "blue", shape "rectangle", size "20cm x 10cm", location "Shinjuku Station East Exit", time "October 1, 2023, around 10:00 AM")

[0979] Output: Send instructions are sent to the terminal.

[0980] Specific operation: The user enters various characteristics into a form within the app and presses the "Submit" button.

[0981] Step 2: Format and send the information

[0982] The terminal converts the characteristic information input by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[0983] Input: User characteristics information.

[0984] Output: Data packets sent to the server.

[0985] Specific operation: The terminal packetizes the feature information and sends it to the server via the Internet.

[0986] Step 3: Receiving and storing information

[0987] The server receives data packets sent from the device and stores them in a database. Each piece of characteristic information is assigned a unique ID, which is managed to avoid data collisions.

[0988] Input: Data packets sent from the terminal.

[0989] Output: Feature information stored in a database with a unique ID.

[0990] Specific operation: The server receives the packet, stores the information in a database, and generates and assigns an ID.

[0991] Step 4: Search for similar lost items

[0992] The server uses a generative AI model (e.g., OpenAI GPT-3) to search for similar lost items based on the information stored in the database. The AI ​​compares feature information and calculates a similarity score based on multiple parameters (color, shape, size, location, time, etc.).

[0993] Input: feature information stored in the database, new feature information sent by the user.

[0994] Output: A list of data with high similarity scores.

[0995] Specific operation: The generative AI searches for information in the database, calculates similarity scores, and creates a list.

[0996] Step 5: Notification of search results

[0997] The server notifies the user of the search result list generated by the AI. Notifications are made based on the priority and number of search results set by the administrator. The device receives this notification and displays the search result list on the application screen.

[0998] Input: The generated search result list.

[0999] Output: Notification to user, list displayed on terminal.

[1000] Specific operation: The server notifies the user of the result list, and the terminal displays it.

[1001] Step 6: Procedures for claiming lost property

[1002] The user selects the lost item from the search results list and presses the "Request Collection" button. The user then enters any additional information or documents required for the collection procedure, including identity verification and confirmation of the lost item details. The server then notifies the lost and found center or storage management department of the received collection request, and requests additional information or identity verification documents as necessary.

[1003] Input: User selections and additional information.

[1004] Output: Notification of pickup request, request for additional information.

[1005] Specific operation: The user makes a collection request, enters the necessary documents, and sends them. The server receives them and notifies the lost and found center.

[1006] Step 7: Reposting if the owner cannot be found

[1007] The server will repost any lost items that have been stored for a certain period of time but whose owner has not been found. This reposting process is carried out using third-party platforms and social media. The server automatically connects to these external platforms, converts the information into a specific format, sends it, and makes it public.

[1008] Input: Information on lost items that have not been returned for a specific period of time.

[1009] Output: Information reposted to social media and third-party platforms.

[1010] Specific operation: The server connects to the platform, publishes the necessary information, and searches for the owner again.

[1011] (Application example 1)

[1012] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1013] Conventional lost property return systems have had the problem of making it difficult for users to quickly and efficiently locate lost items. Lost items are often found in crowded places, such as public areas or commercial facilities, where there is a lot of jumbled information, making it difficult to find the correct owner, and it can take a long time. Even when surveillance cameras and other equipment are available, their use is limited, and manual verification is often required, making the process cumbersome and time-consuming.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1015] In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to send the input characteristic information to the server, means for the server to save the characteristic information in a database, means for the server to search for similar lost items based on the saved characteristic information using a generation AI, means for the server to notify the user of the search results, means for the user to select the corresponding lost item and carry out the procedure for collection, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found, and means for linking with surveillance cameras installed in public areas and commercial facilities to identify the characteristics of lost items in real time, thereby enabling users to find lost items quickly and efficiently.

[1016] A "user" is a person who uses this system to input information about lost items and check search results.

[1017] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[1018] A "server" is a computer system that receives information sent by users, stores it in a database, and performs the necessary processing.

[1019] "Characteristic information" refers to information about the color, shape, size, location, time, and other characteristics of the lost item.

[1020] "Generative AI" is an artificial intelligence technology that compares the similarity of input feature information with information in a database.

[1021] The "database" is an information management system that stores characteristic information of lost items stored on a server.

[1022] "Search results" are information about lost items that the generating AI determines to be highly similar from within the database.

[1023] A "surveillance camera" is a video capture device installed in public areas or commercial facilities.

[1024] The "collection procedure" is the procedure that the user goes through to actually receive the lost item that he or she has selected.

[1025] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared on third-party platforms or social media.

[1026] "Public areas" refer to places that are freely accessible to the general public, such as parks, train stations, and shopping centers.

[1027] "Commercial facilities" refers to places where goods and services are sold, such as shopping malls and supermarkets.

[1028] "Real time" refers to the time characteristic of processing occurring immediately without delay.

[1029] This invention provides a system in which a user inputs the characteristics of a lost item, and the system uses generative AI to search for similar data and quickly return the item to the owner. This section explains how to specifically implement the invention.

[1030] System Configuration

[1031] The system constituting the invention comprises the following elements:

[1032] 1. The terminal where the user types

[1033] Users launch the application using a smartphone, tablet, or PC and input the characteristics of the lost item, such as color, shape, size, location, and time. These devices have the iOS or Android-based application installed.

[1034] 2. Server

[1035] The server receives the feature information sent by the user and stores it in a database. The server uses Django or Flask as a web server and MySQL or PostgreSQL for database management. It also uses a generative AI model (using TensorFlow or PyTorch) to compare the information in the database with the input feature information and calculate a similarity score.

[1036] 3. Surveillance cameras

[1037] Surveillance cameras installed in public areas and commercial facilities transmit images to a server in real time and are used to automatically identify the characteristics of lost items.

[1038] Data processing and calculation

[1039] User side

[1040] When a user uses the application to input the characteristics of a lost item, the device converts the information into data packets and sends them over the network to a server.

[1041] Server side

[1042] The server stores the received data packets in a database and assigns a unique ID to each feature. The stored data is used as input for a generative AI model and compared with other information in the database. The generative AI model uses machine learning techniques to calculate a similarity score and create a list of similar lost items. This list is then sent to the user. The user receives the search results, selects the relevant lost item, and completes the process of claiming it.

[1043] In addition, the server also has the ability to automatically repost information about lost items that have been stored for a certain period of time but whose owners cannot be found on third-party platforms or social media.

[1044] Specific examples

[1045] For example, if a user loses a blue wallet at a train station, the following steps are performed: The user uses their smartphone to enter the wallet's color (blue), shape, size, location (station), time of loss, etc. into an application and presses the send button. This information is sent from the device to a server, which stores the data in a database and uses a generative AI model to search for existing information about the blue wallet. The server then notifies the user of a list of similar lost items, which the user can review and proceed with the collection process.

[1046] Prompt Sentence Examples

[1047] "Example of characteristics to enter if you lost your blue wallet at Shinjuku Station"

[1048] Color: Blue

[1049] Shape: Wallet

[1050] Size: Small

[1051] Dropped at: Shinjuku Station

[1052] Drop time: 10:00

[1053] As described above, the system of the present invention enables users to quickly find lost items and improves the efficiency of lost item management in public areas and commercial facilities.

[1054] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1055] Step 1:

[1056] The user inputs the characteristics of the lost item using a smartphone or tablet application. The information input by the user includes the color, shape, size, location, and time of the lost item.

[1057] input:

[1058] Color: Blue

[1059] Shape: Wallet

[1060] Size: Small

[1061] Location: Station

[1062] Time: 10:00

[1063] output:

[1064] The input data is stored in the terminal.

[1065] Step 2:

[1066] The terminal converts the characteristic information input by the user into a data packet and transmits it to the server, where the data packet contains the input characteristic information in a formatted form.

[1067] input:

[1068] User-entered feature information

[1069] output:

[1070] Data packets sent to the server

[1071] Step 3:

[1072] The server stores the received feature information in a database. The server analyzes the data packet, assigns a unique ID to each feature, and stores the information in a database.

[1073] input:

[1074] Data packets sent from the device

[1075] output:

[1076] Feature information stored in the database

[1077] Step 4:

[1078] The server runs a generative AI model based on the feature information stored in the database. The generative AI model compares the information stored in the database with the newly input information and calculates a similarity score. Similarity is evaluated based on parameters such as color, shape, size, location, and time.

[1079] input:

[1080] New feature information

[1081] Existing information in the database

[1082] output:

[1083] Search result list with similarity scores

[1084] Step 5:

[1085] The server notifies the user of information that has a certain score or higher based on the search result list created by the generative AI model. The user can check the search result list within the application.

[1086] input:

[1087] Search result list

[1088] output:

[1089] User Notification

[1090] Step 6:

[1091] The user checks the list of search results and selects the item they are looking for. The selected information is sent to the server to process the item. The user then completes procedures such as verifying their identity and providing additional information through the application.

[1092] input:

[1093] Search result list

[1094] User Selection

[1095] output:

[1096] Pickup procedure information sent to the server

[1097] Step 7:

[1098] The server receives the claim request, notifies the lost property management department, and, if necessary, requests the user to submit additional information or identification documents.

[1099] input:

[1100] Pick-up procedure information

[1101] output:

[1102] Notifying the Lost and Found Center

[1103] Requesting additional information from the user

[1104] Step 8:

[1105] When a lost item is stored on the server for a certain period of time but the owner cannot be found, the information is reposted on a third-party platform or social networking site, including the information converted into a specific format.

[1106] input:

[1107] Characteristic information whose storage period has expired

[1108] output:

[1109] Reposting information on third-party platforms or social media

[1110] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1111] This invention is a system that allows users to input the characteristics of lost items, and then uses a generative AI and an emotion engine to search for similar data, quickly returning the item to its owner. In addition, by combining this with the emotion engine, it also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1112] 1. Enter lost item information

[1113] User:

[1114] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item, and the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. Once the information has been entered, the user presses the "Submit" button.

[1115] 2. Transmission and storage of information to the server

[1116] Device:

[1117] The terminal converts the characteristic information and emotional state information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[1118] server:

[1119] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[1120] 3. Search for similar lost items

[1121] server:

[1122] The server operates the generation AI and emotion engine based on new information stored in the database. The generation AI compares the feature information sent by the user with existing information in the database and calculates a similarity score. The emotion engine corrects the input emotion information and provides assistance such as prompting the user to reconfirm the information if they are impatient. If a similarity above a certain score is found, the information is added to the search result list.

[1123] 4. Notification of search results

[1124] server:

[1125] The server compiles the search results list generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[1126] Device:

[1127] The device receives the notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they think corresponds to their lost item.

[1128] 5. Procedures for retrieving lost items

[1129] User:

[1130] The user selects the lost item from the search results list through the application. The emotion engine monitors the user's emotions, and if the user is under stress during the process, the system will provide appropriate guidance and support. The user then presses the "Request Collection" button and enters any additional information or documents required for the collection process.

[1131] server:

[1132] The server receives a request from the user to retrieve the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. The emotion engine responds in a way that reduces the user's stress.

[1133] 6. Reposting if the owner cannot be found

[1134] server:

[1135] For lost items that remain unclaimed after a certain period of time, the server processes them for re-posting. This process involves sending information converted into a specific format via third-party platforms or social media. The emotion engine then provides appropriate follow-up to the user, reducing their anxiety about the item being re-posted.

[1136] Examples:

[1137] 1. A user drops a blue wallet at Shinjuku Station.

[1138] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time lost, etc.), and the emotion engine recognizes the emotion. When the user presses the "Send" button, the emotion engine detects anxious facial expressions and tone of voice and prompts the user to double-check the accuracy of the information.

[1139] 3. The device converts this information and emotion information into a data packet and sends it to the server.

[1140] 4. The server stores the received information in a database and searches the existing database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[1141] 5. The user checks the corresponding blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[1142] 6. If the user does not appear after the storage period has expired, the server will use its emotion engine to take measures to reduce anxiety when reposting the information on third-party platforms or social media.

[1143] This system not only reduces the burden on users and ensures that lost items are returned to their owners quickly, but also improves the overall user experience by incorporating an emotion engine.

[1144] The processing flow will be explained below.

[1145] Step 1:

[1146] The user launches the application and opens the lost item information entry form. The user enters the details of the item they lost (color, shape, size, location, time, and other characteristics). At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[1147] Step 2:

[1148] The terminal converts the user's input data (feature information and emotional state information) into a data packet according to the format, and sends the converted data packet to the server.

[1149] Step 3:

[1150] The server receives the data packet, checks its contents, stores the received information in a database, and assigns a unique ID to each feature. Emotional state information is also stored.

[1151] Step 4:

[1152] The server runs the generation AI based on the new information stored in the database. The generation AI compares the feature information sent by the user with the existing information in the database and calculates a similarity score. If a similarity above a certain score is found, the information is added to the search result list.

[1153] Step 5:

[1154] The server uses an emotion engine to analyze the feature information and the user's emotional state. If the emotional state indicates impatience or tension, it sends a notification to the user urging them to "reconfirm the accuracy of the information." After the user reconfirms, the information is updated.

[1155] Step 6:

[1156] The server compiles a list of search results generated by the AI ​​and notifies the user. The emotion engine creates a notification with a message in easy-to-understand language based on the user's emotional state.

[1157] Step 7:

[1158] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user checks the list and selects the information that they think corresponds to their lost item.

[1159] Step 8:

[1160] The user selects the lost item from the list and presses the "Request Collection" button. The user then enters any additional information or documents required to complete the collection process. The emotion engine monitors the user's emotional state and offers appropriate guidance and support if the user is under stress during the process.

[1161] Step 9:

[1162] The server receives a request from the user to collect the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. Based on the emotion engine, the server provides additional support to reduce the user's stress.

[1163] Step 10:

[1164] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[1165] Step 11:

[1166] As part of the reposting process, the server publishes the information to third-party platforms and social media to increase the chances of the owner being found. The server also converts the information into an appropriate format. The emotion engine then follows up with the user appropriately to reduce their anxiety about the reposting.

[1167] Example 2

[1168] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1169] Conventional lost item return systems search for similarities based only on characteristic information entered by the user, and do not take into account the user's emotional state, making them inadequate for users who are stressed or anxious. Another issue is that re-posting of lost items when the owner cannot be found is not automated. This results in a poor user experience and often results in lost items not being returned efficiently.

[1170] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input characteristics of a lost item and for an emotion engine to recognize the emotional state; means for a terminal to transmit the input characteristic information and emotional state information to the server; means for the server to store the characteristic information and emotion information in a database; means for the server to search for similar lost items using a generation AI based on the stored characteristic information; means for the server to notify the user of the search results together with a message corresponding to the emotion generated by the emotion engine; means for the user to select the corresponding lost item and carry out procedures for retrieval; and means for the server to re-post information about lost items whose owners have not been found even after being stored for a certain period of time. This enables efficient and prompt return of lost items while taking into account the user's emotional state.

[1171] "User" refers to an individual or corporation that uses the System to enter information about lost items and receive search results.

[1172] A "terminal" is a device used by a user to input information, and includes smartphones, personal computers, etc.

[1173] "Characteristic information" refers to detailed attribute information about the lost item, including elements such as color, shape, size, location, and time.

[1174] "Emotion engine" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[1175] "Emotional state information" refers to data indicating the emotional state of the user as recognized by the emotion engine.

[1176] "Generative AI" refers to artificial intelligence that uses machine learning techniques to generate and search for highly similar data.

[1177] "Database" refers to a collection of information including characteristic information and emotion information stored on a server.

[1178] "Similarity score" refers to a number that indicates the similarity of the data, calculated by the generating AI based on the characteristic information of the lost item.

[1179] "Notification" refers to communication in the form of search results or messages sent from a server to a user.

[1180] "Collection Procedure" refers to the series of steps a User goes through to identify a lost item and confirm its ownership.

[1181] "Reposting" refers to the act of reposting information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[1182] This invention is a system that promptly returns lost items to their owners by using a generative AI and an emotion engine based on the user's input of characteristic information about the lost item. By combining this with the emotion engine, it also has the ability to recognize the user's emotional state and suggest appropriate responses.

[1183] The system has the following configuration:

[1184] 1. User inputs lost item information

[1185] The user launches a dedicated application on a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters detailed information such as color, shape, size, location, and time. The emotion engine also analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[1186] 2. Transmission and storage of information to the server

[1187] The device converts the characteristic information and emotional state information entered by the user into a data packet and sends it to the server. The server then checks the received data packet and stores its contents in a database. The characteristic information and emotional information are managed using a unique ID.

[1188] 3. Search for similar lost items

[1189] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the feature information with existing information in the database and calculates a similarity score. The emotion engine provides assistance to the user, such as reconfirming information, based on the emotion information.

[1190] 4. Notification of search results

[1191] The server compiles a list of search results generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[1192] 5. Procedures for retrieving lost items

[1193] The user selects the lost item from the search results list through the application and initiates the process of claiming it. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress during the process.

[1194] 6. Reposting if the owner cannot be found

[1195] If the owner cannot be found after a certain period of storage, the server will repost the content. The reposting information will be sent via third-party platforms or social media. The emotion engine will follow up to alleviate concerns about the reposting.

[1196] Specific examples

[1197] 1. A user drops a blue wallet at a train station.

[1198] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time of loss, etc.). The emotion engine recognizes emotions and, if it detects a panicked expression or tone of voice, prompts the user to reconfirm the information.

[1199] 3. The device converts the input information and emotion information into data packets and sends them to the server.

[1200] 4. The server stores the received information in a database and searches the database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[1201] 5. The user checks the blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[1202] 6. If the owner cannot be found after the storage period has expired, the server will repost the information on a third-party platform or social media, and the emotion engine will take measures to alleviate anxiety.

[1203] This system reduces the burden on users, enables quick return of lost items, and improves the user experience through an emotion engine.

[1204] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1205] Step-by-step process instructions

[1206] Step 1:

[1207] The user launches a dedicated application from their smartphone or PC. They then fill out a form within the application to input the characteristics of the lost item (color, shape, size, location, time, etc.). At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. The input is the specific attribute information of the lost item and the user's emotional state. The output is the information entered into the form and the recognized emotional state.

[1208] Step 2:

[1209] The device converts the feature information and emotional state information entered by the user into a data packet. The generated data packet includes the user's input data and the emotional state recognized by the emotion engine. Specifically, the application on the device converts the data into JSON format and sends it to the server as an HTTP POST request. The input is the feature information and emotional state information entered by the user. The output is the data packet to be sent to the server.

[1210] Step 3:

[1211] The server receives data packets sent from the device. The received data is analyzed and stored in a database. A unique ID is assigned to the feature information and emotional state information, and they are managed uniquely in the database. The data packets sent from the device are obtained as input. The feature information and emotional state information stored in the database are obtained as output.

[1212] Step 4:

[1213] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the input feature information with existing data in the database and calculates a similarity score. The emotion engine assists with response based on emotion information. The inputs are existing information in the database and newly saved feature information and emotion information. The output is a similarity score and an assistance message.

[1214] Step 5:

[1215] The server compiles the search result list generated by the generation AI. At the same time, the emotion engine generates an easy-to-understand message based on the user's emotional information. This is sent to the user within the application as a notification. The inputs are the search result list and message obtained from the generation AI and emotion engine. The output is the message to be notified to the user and the search results.

[1216] Step 6:

[1217] The device receives a notification from the server and displays a list of search results on the application screen. The user checks this list and selects the information that corresponds to their lost item. The input is the list of search results and a message notified by the server. The output is the search results displayed in the application and the user's selection.

[1218] Step 7:

[1219] The user identifies the lost item and presses the "Request Collection" button to proceed. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user becomes stressed during the process. The input is the user's selection and emotional state. The output is additional information and guidance regarding the collection request.

[1220] Step 8:

[1221] The server receives a collection request from the user and notifies the lost and found center or storage management department of the received information. If necessary, it requests additional information or identification documents. The emotion engine responds to reduce the user's stress. The input is the collection request information sent by the user. The output is a collection request notification to the lost and found center or the department in charge, along with additional request information.

[1222] Step 9:

[1223] If the owner cannot be found after a certain period of storage, the server will repost the item. To repost the item, a third-party platform or social networking site is used, and the information is converted into a specific format and sent. The emotion engine will provide appropriate follow-up to the user to alleviate anxiety about the item being reposted. The input is information about lost items that have passed the storage period. The output is information about the item being reposted to a third-party platform or social networking site, along with a follow-up message.

[1224] (Application example 2)

[1225] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1226] In modern society, lost items frequently occur in public places and other crowded locations, and the process of returning them takes a lot of time and effort. In these circumstances, there is a need for a method to accurately and quickly collate a wide variety of lost item information and return them to their owners. It is also important to reduce the stress and impatience felt by users searching for lost items and provide a smooth return process. To solve this problem, a new system that combines an emotion engine and generative AI is required.

[1227] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to transmit the input characteristic information and the user's emotional state to the server, means for the server to store the characteristic information and the emotional state in a database, means for the server to search for similar lost items based on the stored characteristic information using a generation AI, means for an emotion engine to recognize the user's emotional state and suggest an appropriate response based on the search result, means for the server to notify the user of the search result in a message corresponding to the user's emotional state, means for the user to select the corresponding lost item and carry out the collection procedure, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owner has not been found, and means for capturing characteristic information via a specific device and comparing it with the database. This allows the user to quickly and reliably find lost items and return them to their owners in a stress-free process.

[1228] "User" means any person or entity that uses the System to enter the characteristics of lost items and receive search results.

[1229] A "terminal" is a device operated by a user, such as a smartphone, a personal computer, or smart glasses.

[1230] "Characteristic information" refers to specific descriptions of the lost item, such as its color, shape, size, location, and time.

[1231] "Emotional state" is information that indicates the user's emotions, and refers to the mental state extracted from facial expressions, tone of voice, etc.

[1232] "Server" refers to a computer system that processes and stores information sent from a terminal and, if necessary, connects to a database.

[1233] "Database" refers to an electronic collection of information for storing and managing characteristic information and emotional states.

[1234] "Generative AI" refers to artificial intelligence that uses machine learning technology to analyze characteristic information and search for lost items that are highly similar.

[1235] An "emotion engine" refers to software that analyzes a user's emotional state in real time and suggests appropriate responses accordingly.

[1236] A "message" is information sent from a server to a user, and may be expressed in the form of text, audio, or an image.

[1237] "Pick-up procedure" refers to a series of operations that a user performs to retrieve a lost item based on a search result.

[1238] "Re-posting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared.

[1239] "Specific device" refers to special hardware equipment, such as smart glasses or a camera, that has the ability to capture characteristic information.

[1240] This invention is a system in which a user inputs the characteristics of a lost item, and based on that information, a generative AI and an emotion engine are used to search for similar data and quickly return it to the owner.Furthermore, it has a function that uses the emotion engine to recognize the user's emotional state and suggest appropriate responses.

[1241] The server receives the characteristic information and emotional state information input by the user from the device and stores them in a database according to a format. For example, a user captures an image of a lost item (e.g., a blue wallet) through smart glasses and inputs its characteristic information (color, shape, size, location, time, etc.). The input information is sent to the server via the device, and the server stores it in a database.

[1242] The server then uses the generation AI to match the stored feature information with existing data in the database to search for similar lost items. The generation AI uses machine learning techniques to calculate a similarity score for the feature information and identify items with high scores. During this process, the emotion engine recognizes the user's emotional state and prompts the user to reconfirm the accuracy of the information if they are feeling anxious.

[1243] The server generates search results and sends a notification to the user. The notification includes a message in a user-friendly format based on the emotional information analyzed by the emotion engine. The user then selects the lost item and initiates the procedure for retrieval. During this process, the emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress.

[1244] Once the user has completed the collection process, the server notifies the lost and found center or storage management department and requests any additional information or identification documents required. If the owner cannot be found, the server stores the lost item for a certain period of time and re-posts it on third-party platforms or social media. At this time, the emotion engine performs appropriate follow-up to alleviate the user's anxiety.

[1245] As a concrete example, let's say a user loses a blue wallet at a train station. The user inputs the wallet's characteristics through the smart glasses. The server receives the information and uses the generative AI to compare it with the database. When the matching blue wallet is found, the user is notified with a message based on the user's emotion. The user confirms the information and proceeds with the procedure to retrieve it, and the emotion engine responds to reduce the user's stress.

[1246] Examples of input prompts for generative AI models include:

[1247] "A blue wallet has been found. Search the database for lost items that match the color, shape, and size of this wallet. At the same time, the staff is feeling anxious. Use the emotion engine to suggest the best response."

[1248] In this way, the present invention not only reduces the burden on the user and allows lost items to be returned to their owners more quickly, but also improves the overall user experience by including an emotion engine.

[1249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1250] Step 1: User enters the characteristics of the lost item

[1251] The user puts on the smart glasses and uses the capture function to take a picture of the lost item (e.g., a blue wallet). The user then inputs characteristic information such as color, shape, size, location, and time of dropping into the application. This input information is simultaneously analyzed by the emotion engine to determine the user's emotional state (impatience or anxiety).

[1252] Input: Feature information of the lost item (color, shape, size, location, time), user's emotional information

[1253] Output: Data packets of feature information and emotion information

[1254] Step 2: The device sends a data packet to the server

[1255] The device converts the input feature information and emotion information into a data packet according to a format, which is then encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[1256] Input: feature information and emotion information data

[1257] Output: Data packets sent to the server

[1258] Step 3: The server receives and stores the data packet

[1259] The server receives the data packets sent from the device and checks their contents. When storing the received information in a database, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[1260] Input: Data packets of feature information and emotion information

[1261] Output: Feature information and emotion information stored in a database

[1262] Step 4: The server uses the generated AI to search for similar lost items

[1263] The server runs a generative AI based on the feature and emotion information stored in the database. The generative AI analyzes the feature information and compares it with existing data in the database to calculate a similarity score. The emotion engine takes into account the user's emotional state and prompts reconfirmation as necessary.

[1264] Input: feature information and emotion information in the database

[1265] Output: A list of lost items with high similarity scores

[1266] Step 5: The server notifies the user of the search results

[1267] The server compiles a list of similar lost items generated by the AI ​​and creates a message in a format that is easy for the user to understand based on the emotional information analyzed by the emotion engine. This notification is sent to the user in a format that can be viewed within the application.

[1268] Input: List of similar lost items, user's emotional information

[1269] Output: Search result notification sent to user

[1270] Step 6: User selects the lost item and begins collection procedures

[1271] The user receives a notification and checks the list of lost items within the app. If the item is found, the user can proceed to claim it. During this process, an emotion engine monitors the user's emotional state and provides guidance and support if the user is under stress.

[1272] Input: Search result notification, user's emotional state

[1273] Output: User's selection of lost item and notification of completion of claim procedure

[1274] Step 7: The server notifies the lost and found center

[1275] Once the user has completed the collection process, the server notifies the lost and found center or storage department and manages the process of requesting additional information or identification documents, if necessary.

[1276] Input: Notification of completion of collection procedure

[1277] Output: Notification to the Lost and Found Center and request for necessary documents

[1278] Step 8: Reposting if the server owner cannot be found

[1279] If the owner cannot be found after a certain period of time, the server will repost the information on a third-party platform or social networking site, and the emotion engine will follow up and take measures to alleviate concerns about the reposting.

[1280] Input: Lost and found information for reposting

[1281] Output: Reposting notifications to third-party platforms and social media

[1282] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1283] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1284] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1285] [Fourth embodiment]

[1286] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1287] 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.

[1288] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[1289] 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.

[1290] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1291] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1292] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1293] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1294] 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.

[1295] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1296] 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.

[1297] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1298] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1299] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. The following describes an embodiment of the present invention.

[1300] 1. Enter lost item information

[1301] User:

[1302] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once this is complete, the user presses the "Submit" button to send the information to the server.

[1303] 2. Transmission and storage of information to the server

[1304] Device:

[1305] The terminal converts the characteristic information entered by the user into a data packet according to a format, and then transmits the data packet to the server. The transmission process can be carried out via the Internet or a dedicated network.

[1306] server:

[1307] The server receives the data packets sent from the device and stores them in a database. When storing the data, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[1308] 3. Search for similar lost items

[1309] server:

[1310] The server runs a generative AI based on the information stored in the database. The AI ​​compares the feature information sent by the user with existing information in the database and calculates a similarity score. The similarity score is determined based on multiple parameters such as color, location, date and time. Information that scores above a certain level is listed as a search result.

[1311] 4. Notification of search results

[1312] server:

[1313] The server notifies the user of the search result list generated by the AI. Notifications are made according to the priority and number of search results set by the administrator, and the results can be viewed within the application.

[1314] Device:

[1315] The device receives the notification from the server and displays the search results list on the application screen. The user can check the list and select the corresponding lost item.

[1316] 5. Procedures for retrieving lost items

[1317] User:

[1318] Through the application, users select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[1319] server:

[1320] The server receives a request from the user to collect the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[1321] 6. Reposting if the owner cannot be found

[1322] server:

[1323] For lost items that have been stored for a certain period of time but whose owner has not been found, the server will repost them. To repost, the server will use third-party platforms or social media to send information converted into a specific format. The server will automatically connect to these external platforms and make the information public.

[1324] Examples:

[1325] 1. A user drops a blue wallet at Shinjuku Station.

[1326] 2. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[1327] 3. The device converts this information into data packets and sends them to the server.

[1328] 4. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[1329] 5. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[1330] 6. If the user does not appear after the storage period has expired, the server will re-post the information on third-party platforms or social media to make it easier to find the owner.

[1331] In this way, the system of the present invention allows users to quickly find lost items, reduces business operations costs, and eliminates unnecessary waste.

[1332] The processing flow will be explained below.

[1333] Step 1:

[1334] The user starts the application and opens the lost item information input form. The user enters the details of the lost item (color, shape, size, location, time, and other characteristics) and presses the "Submit" button.

[1335] Step 2:

[1336] The terminal converts the lost item information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[1337] Step 3:

[1338] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, it assigns a unique ID to each piece of characteristic information.

[1339] Step 4:

[1340] The server runs a generative AI based on the new information stored in the database. The generative AI compares the feature information sent by the user with existing data in the database and calculates a similarity score. If a similarity score above a certain level is found, the information is added to the search result list.

[1341] Step 5:

[1342] The server compiles the search results list generated by the generative AI and notifies the user, who can view the results within the application.

[1343] Step 6:

[1344] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they believe corresponds to their lost item.

[1345] Step 7:

[1346] The user selects the lost item from the list and presses the "Request for Collection" button. The user then enters any additional information or documents required for the collection procedure through the application.

[1347] Step 8:

[1348] The server receives a collection request from the user, notifies the lost and found center or storage management department of the received collection request, and requests additional information or identification documents as necessary.

[1349] Step 9:

[1350] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[1351] Step 10:

[1352] As part of the reposting process, the server publishes the information to third-party platforms and social networks, converting it into the appropriate format to increase the chances of finding the owner.

[1353] These steps will reduce the burden on users and create a system that quickly returns lost items to their owners.

[1354] Example 1

[1355] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] In modern society, quickly identifying the owner of lost items and returning them without unnecessary effort or cost is a major challenge. Conventional methods require a lot of time and effort to manage and search for lost items, and if the owner cannot be found, the items are wasted. To improve this situation, a more efficient and reliable system for returning lost items to their owners is needed.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1358] In this invention, the server includes: means for a user to input characteristics of a lost item; means for a terminal to send the input characteristic information to the server; means for the server to store the characteristic information in a database; means for the server to search for highly similar lost items using a generative AI model based on the stored characteristic information; means for the server to calculate a similarity score and list information with a certain score or higher; means for the server to notify the user of the search results; means for the terminal to display the search results to the user; means for the user to select the corresponding lost item and complete the collection procedure; and means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found. This allows users to quickly find lost items, reducing business operation costs and unnecessary waste.

[1359] "User" refers to an entity that uses the system to input characteristic information about lost items.

[1360] A "terminal" is a device operated by a user, including a smartphone, PC, etc. It has the role of transmitting characteristic information entered by the user to a server.

[1361] "Server" refers to the primary computer system that receives, stores, searches using the generative AI model, calculates similarity scores, notifies, and re-displays search results.

[1362] "Characteristic information" refers to detailed information for identifying a lost item, such as its color, shape, size, location, and time.

[1363] The term "data packet" refers to a data unit that a terminal generates by converting characteristic information into a specific format and transmits it to a server.

[1364] "Database" refers to a structured data storage for the server to store feature information.

[1365] A "generative AI model" refers to an artificial intelligence model that compares feature information from users with existing data to calculate similarity scores and generate search results.

[1366] The "similarity score" is a numerical representation of the similarity between lost items, calculated based on multiple parameters of the feature information. Information that achieves a certain score or higher will be listed as search results.

[1367] "Search results" refers to information on similar lost items, listed based on the similarity score calculated by the generative AI model.

[1368] "Notification" refers to the sending of information by the server to inform the user of search results, including email and in-application notifications.

[1369] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[1370] MODE FOR CARRYING OUT THE INVENTION

[1371] The present invention is a system that allows a user to input the characteristics of a lost item, and then uses a generation AI to search for similar data based on that information, thereby quickly returning the item to its owner. Specific embodiments of the present invention are described below.

[1372] Entering lost property information

[1373] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item. Once the entry is complete, the user presses the "Submit" button to send the information to the device.

[1374] Sending and storing information on the server

[1375] The terminal converts the characteristic information entered by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[1376] The server receives the data packets sent from the device and stores them in a database. When storing them in the database, each piece of characteristic information is assigned a unique ID. This ID prevents data collisions.

[1377] Search for similar lost items

[1378] The server searches the information stored in the database using a generative AI model (such as OpenAI GPT-3). This AI model compares the feature information sent by the user with existing information in the database and calculates a similarity score based on multiple parameters. Information that meets a certain score or higher is listed as a search result.

[1379] Search result notifications

[1380] The server notifies the user of the search result list generated by the AI. The notification is based on the priority and number of search results set by the administrator, and the results are made viewable within the application.

[1381] The device receives the notification from the server and displays the search result list on the application screen. The user can check the list and select the corresponding lost item.

[1382] Procedures for collecting lost items

[1383] Through the application, users can select the relevant lost item from the search results list, then press the "Request Collection" button and enter any additional information or documentation required to process the collection, including verifying identity and details of the lost item.

[1384] The server receives a request from the user to retrieve the item, notifies the lost and found center or the storage management department, and, if necessary, requests the user to submit additional information or identification documents.

[1385] Reposting if owner cannot be found

[1386] The server automatically connects to these external platforms, converts the information into a specific format, and transmits it to the public.

[1387] Specific examples

[1388] If a user loses their blue wallet at Shinjuku Station, they will use the system as follows:

[1389] 1. The user opens the app, enters information about the blue wallet (color, shape, size, location and time lost, etc.), and presses the "Send" button.

[1390] 2. The device converts this information into data packets and sends them to the server.

[1391] 3. The server stores the received information in a database and uses the generative AI to search the existing database. If a matching blue wallet is found, the server notifies the user of the information.

[1392] 4. The user checks the blue wallet and makes a "request for collection." The server notifies the lost and found center and takes the necessary steps.

[1393] 5. If the user does not show up after the retention period has expired, the server will repost the information on social media to make it easier to find the owner.

[1394] Prompt Sentence Examples

[1395] I lost a blue wallet at Shinjuku Station. It is blue, rectangular, and approximately 20cm x 10cm in size. I lost it near the east exit of Shinjuku Station around 10:00 AM on October 1, 2023. Please search for lost items based on this characteristic information.

[1396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1397] Step 1: Enter lost item information

[1398] The user launches a dedicated application on their smartphone or computer and inputs the characteristics of the lost item, including color, shape, size, location, time, and other characteristics. After completing the input, the user presses the "Send" button.

[1399] Input: Feature information entered by the user (e.g., color "blue", shape "rectangle", size "20cm x 10cm", location "Shinjuku Station East Exit", time "October 1, 2023, around 10:00 AM")

[1400] Output: Send instructions are sent to the terminal.

[1401] Specific operation: The user enters various characteristics into a form within the app and presses the "Submit" button.

[1402] Step 2: Format and send the information

[1403] The terminal converts the characteristic information input by the user into a specific format and generates a data packet, which is then sent to a server via the Internet or a dedicated network.

[1404] Input: User characteristics information.

[1405] Output: Data packets sent to the server.

[1406] Specific operation: The terminal packetizes the feature information and sends it to the server via the Internet.

[1407] Step 3: Receiving and storing information

[1408] The server receives data packets sent from the device and stores them in a database. Each piece of characteristic information is assigned a unique ID, which is managed to avoid data collisions.

[1409] Input: Data packets sent from the terminal.

[1410] Output: Feature information stored in a database with a unique ID.

[1411] Specific operation: The server receives the packet, stores the information in a database, and generates and assigns an ID.

[1412] Step 4: Search for similar lost items

[1413] The server uses a generative AI model (e.g., OpenAI GPT-3) to search for similar lost items based on the information stored in the database. The AI ​​compares feature information and calculates a similarity score based on multiple parameters (color, shape, size, location, time, etc.).

[1414] Input: feature information stored in the database, new feature information sent by the user.

[1415] Output: A list of data with high similarity scores.

[1416] Specific operation: The generative AI searches for information in the database, calculates similarity scores, and creates a list.

[1417] Step 5: Notification of search results

[1418] The server notifies the user of the search result list generated by the AI. Notifications are made based on the priority and number of search results set by the administrator. The device receives this notification and displays the search result list on the application screen.

[1419] Input: The generated search result list.

[1420] Output: Notification to user, list displayed on terminal.

[1421] Specific operation: The server notifies the user of the result list, and the terminal displays it.

[1422] Step 6: Procedures for claiming lost property

[1423] The user selects the lost item from the search results list and presses the "Request Collection" button. The user then enters any additional information or documents required for the collection procedure, including identity verification and confirmation of the lost item details. The server then notifies the lost and found center or storage management department of the received collection request, and requests additional information or identity verification documents as necessary.

[1424] Input: User selections and additional information.

[1425] Output: Notification of pickup request, request for additional information.

[1426] Specific operation: The user makes a collection request, enters the necessary documents, and sends them. The server receives them and notifies the lost and found center.

[1427] Step 7: Reposting if the owner cannot be found

[1428] The server will repost any lost items that have been stored for a certain period of time but whose owner has not been found. This reposting process is carried out using third-party platforms and social media. The server automatically connects to these external platforms, converts the information into a specific format, sends it, and makes it public.

[1429] Input: Information on lost items that have not been returned for a specific period of time.

[1430] Output: Information reposted to social media and third-party platforms.

[1431] Specific operation: The server connects to the platform, publishes the necessary information, and searches for the owner again.

[1432] (Application example 1)

[1433] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1434] Conventional lost property return systems have had the problem of making it difficult for users to quickly and efficiently locate lost items. Lost items are often found in crowded places, such as public areas or commercial facilities, where there is a lot of jumbled information, making it difficult to find the correct owner, and it can take a long time. Even when surveillance cameras and other equipment are available, their use is limited, and manual verification is often required, making the process cumbersome and time-consuming.

[1435] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1436] In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to send the input characteristic information to the server, means for the server to save the characteristic information in a database, means for the server to search for similar lost items based on the saved characteristic information using a generation AI, means for the server to notify the user of the search results, means for the user to select the corresponding lost item and carry out the procedure for collection, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owners have not been found, and means for linking with surveillance cameras installed in public areas and commercial facilities to identify the characteristics of lost items in real time, thereby enabling users to find lost items quickly and efficiently.

[1437] A "user" is a person who uses this system to input information about lost items and check search results.

[1438] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[1439] A "server" is a computer system that receives information sent by users, stores it in a database, and performs the necessary processing.

[1440] "Characteristic information" refers to information about the color, shape, size, location, time, and other characteristics of the lost item.

[1441] "Generative AI" is an artificial intelligence technology that compares the similarity of input feature information with information in a database.

[1442] The "database" is an information management system that stores characteristic information of lost items stored on a server.

[1443] "Search results" are information about lost items that the generating AI determines to be highly similar from within the database.

[1444] A "surveillance camera" is a video capture device installed in public areas or commercial facilities.

[1445] The "collection procedure" is the procedure that the user goes through to actually receive the lost item that he or she has selected.

[1446] "Reposting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared on third-party platforms or social media.

[1447] "Public areas" refer to places that are freely accessible to the general public, such as parks, train stations, and shopping centers.

[1448] "Commercial facilities" refers to places where goods and services are sold, such as shopping malls and supermarkets.

[1449] "Real time" refers to the time characteristic of processing occurring immediately without delay.

[1450] This invention provides a system in which a user inputs the characteristics of a lost item, and the system uses generative AI to search for similar data and quickly return the item to the owner. This section explains how to specifically implement the invention.

[1451] System Configuration

[1452] The system constituting the invention comprises the following elements:

[1453] 1. The terminal where the user types

[1454] Users launch the application using a smartphone, tablet, or PC and input the characteristics of the lost item, such as color, shape, size, location, and time. These devices have the iOS or Android-based application installed.

[1455] 2. Server

[1456] The server receives the feature information sent by the user and stores it in a database. The server uses Django or Flask as a web server and MySQL or PostgreSQL for database management. It also uses a generative AI model (using TensorFlow or PyTorch) to compare the information in the database with the input feature information and calculate a similarity score.

[1457] 3. Surveillance cameras

[1458] Surveillance cameras installed in public areas and commercial facilities transmit images to a server in real time and are used to automatically identify the characteristics of lost items.

[1459] Data processing and calculation

[1460] User side

[1461] When a user uses the application to input the characteristics of a lost item, the device converts the information into data packets and sends them over the network to a server.

[1462] Server side

[1463] The server stores the received data packets in a database and assigns a unique ID to each feature. The stored data is used as input for a generative AI model and compared with other information in the database. The generative AI model uses machine learning techniques to calculate a similarity score and create a list of similar lost items. This list is then sent to the user. The user receives the search results, selects the relevant lost item, and completes the process of claiming it.

[1464] In addition, the server also has the ability to automatically repost information about lost items that have been stored for a certain period of time but whose owners cannot be found on third-party platforms or social media.

[1465] Specific examples

[1466] For example, if a user loses a blue wallet at a train station, the following steps are performed: The user uses their smartphone to enter the wallet's color (blue), shape, size, location (station), time of loss, etc. into an application and presses the send button. This information is sent from the device to a server, which stores the data in a database and uses a generative AI model to search for existing information about the blue wallet. The server then notifies the user of a list of similar lost items, which the user can review and proceed with the collection process.

[1467] Prompt Sentence Examples

[1468] "Example of characteristics to enter if you lost your blue wallet at Shinjuku Station"

[1469] Color: Blue

[1470] Shape: Wallet

[1471] Size: Small

[1472] Dropped at: Shinjuku Station

[1473] Drop time: 10:00

[1474] As described above, the system of the present invention enables users to quickly find lost items and improves the efficiency of lost item management in public areas and commercial facilities.

[1475] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1476] Step 1:

[1477] The user inputs the characteristics of the lost item using a smartphone or tablet application. The information input by the user includes the color, shape, size, location, and time of the lost item.

[1478] input:

[1479] Color: Blue

[1480] Shape: Wallet

[1481] Size: Small

[1482] Location: Station

[1483] Time: 10:00

[1484] output:

[1485] The input data is stored in the terminal.

[1486] Step 2:

[1487] The terminal converts the characteristic information input by the user into a data packet and transmits it to the server, where the data packet contains the input characteristic information in a formatted form.

[1488] input:

[1489] User-entered feature information

[1490] output:

[1491] Data packets sent to the server

[1492] Step 3:

[1493] The server stores the received feature information in a database. The server analyzes the data packet, assigns a unique ID to each feature, and stores the information in a database.

[1494] input:

[1495] Data packets sent from the device

[1496] output:

[1497] Feature information stored in the database

[1498] Step 4:

[1499] The server runs a generative AI model based on the feature information stored in the database. The generative AI model compares the information stored in the database with the newly input information and calculates a similarity score. Similarity is evaluated based on parameters such as color, shape, size, location, and time.

[1500] input:

[1501] New feature information

[1502] Existing information in the database

[1503] output:

[1504] Search result list with similarity scores

[1505] Step 5:

[1506] The server notifies the user of information that has a certain score or higher based on the search result list created by the generative AI model. The user can check the search result list within the application.

[1507] input:

[1508] Search result list

[1509] output:

[1510] User Notification

[1511] Step 6:

[1512] The user checks the list of search results and selects the item they are looking for. The selected information is sent to the server to process the item. The user then completes procedures such as verifying their identity and providing additional information through the application.

[1513] input:

[1514] Search result list

[1515] User Selection

[1516] output:

[1517] Pickup procedure information sent to the server

[1518] Step 7:

[1519] The server receives the claim request, notifies the lost property management department, and, if necessary, requests the user to submit additional information or identification documents.

[1520] input:

[1521] Pick-up procedure information

[1522] output:

[1523] Notifying the Lost and Found Center

[1524] Requesting additional information from the user

[1525] Step 8:

[1526] When a lost item is stored on the server for a certain period of time but the owner cannot be found, the information is reposted on a third-party platform or social networking site, including the information converted into a specific format.

[1527] input:

[1528] Characteristic information whose storage period has expired

[1529] output:

[1530] Reposting information on third-party platforms or social media

[1531] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1532] This invention is a system that allows users to input the characteristics of lost items, and then uses a generative AI and an emotion engine to search for similar data, quickly returning the item to its owner. In addition, by combining this with the emotion engine, it also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1533] 1. Enter lost item information

[1534] User:

[1535] The user launches the application using a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters details about the color, shape, size, location, time, and other characteristics of the lost item, and the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. Once the information has been entered, the user presses the "Submit" button.

[1536] 2. Transmission and storage of information to the server

[1537] Device:

[1538] The terminal converts the characteristic information and emotional state information input by the user into a data packet according to a format, and transmits the converted data packet to the server.

[1539] server:

[1540] The server receives the data packet sent from the device, checks its contents, and stores the received information in a database. When storing the information, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[1541] 3. Search for similar lost items

[1542] server:

[1543] The server operates the generation AI and emotion engine based on new information stored in the database. The generation AI compares the feature information sent by the user with existing information in the database and calculates a similarity score. The emotion engine corrects the input emotion information and provides assistance such as prompting the user to reconfirm the information if they are impatient. If a similarity above a certain score is found, the information is added to the search result list.

[1544] 4. Notification of search results

[1545] server:

[1546] The server compiles the search results list generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[1547] Device:

[1548] The device receives the notification from the server. Based on the notification, a search result list is displayed on the application screen. The user can review this list and select the information that they think corresponds to their lost item.

[1549] 5. Procedures for retrieving lost items

[1550] User:

[1551] The user selects the lost item from the search results list through the application. The emotion engine monitors the user's emotions, and if the user is under stress during the process, the system will provide appropriate guidance and support. The user then presses the "Request Collection" button and enters any additional information or documents required for the collection process.

[1552] server:

[1553] The server receives a request from the user to retrieve the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. The emotion engine responds in a way that reduces the user's stress.

[1554] 6. Reposting if the owner cannot be found

[1555] server:

[1556] For lost items that remain unclaimed after a certain period of time, the server processes them for re-posting. This process involves sending information converted into a specific format via third-party platforms or social media. The emotion engine then provides appropriate follow-up to the user, reducing their anxiety about the item being re-posted.

[1557] Examples:

[1558] 1. A user drops a blue wallet at Shinjuku Station.

[1559] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time lost, etc.), and the emotion engine recognizes the emotion. When the user presses the "Send" button, the emotion engine detects anxious facial expressions and tone of voice and prompts the user to double-check the accuracy of the information.

[1560] 3. The device converts this information and emotion information into a data packet and sends it to the server.

[1561] 4. The server stores the received information in a database and searches the existing database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[1562] 5. The user checks the corresponding blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[1563] 6. If the user does not appear after the storage period has expired, the server will use its emotion engine to take measures to reduce anxiety when reposting the information on third-party platforms or social media.

[1564] This system not only reduces the burden on users and ensures that lost items are returned to their owners quickly, but also improves the overall user experience by incorporating an emotion engine.

[1565] The processing flow will be explained below.

[1566] Step 1:

[1567] The user launches the application and opens the lost item information entry form. The user enters the details of the item they lost (color, shape, size, location, time, and other characteristics). At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[1568] Step 2:

[1569] The terminal converts the user's input data (feature information and emotional state information) into a data packet according to the format, and sends the converted data packet to the server.

[1570] Step 3:

[1571] The server receives the data packet, checks its contents, stores the received information in a database, and assigns a unique ID to each feature. Emotional state information is also stored.

[1572] Step 4:

[1573] The server runs the generation AI based on the new information stored in the database. The generation AI compares the feature information sent by the user with the existing information in the database and calculates a similarity score. If a similarity above a certain score is found, the information is added to the search result list.

[1574] Step 5:

[1575] The server uses an emotion engine to analyze the feature information and the user's emotional state. If the emotional state indicates impatience or tension, it sends a notification to the user urging them to "reconfirm the accuracy of the information." After the user reconfirms, the information is updated.

[1576] Step 6:

[1577] The server compiles a list of search results generated by the AI ​​and notifies the user. The emotion engine creates a notification with a message in easy-to-understand language based on the user's emotional state.

[1578] Step 7:

[1579] The device receives a notification from the server. Based on the notification, a search result list is displayed on the application screen. The user checks the list and selects the information that they think corresponds to their lost item.

[1580] Step 8:

[1581] The user selects the lost item from the list and presses the "Request Collection" button. The user then enters any additional information or documents required to complete the collection process. The emotion engine monitors the user's emotional state and offers appropriate guidance and support if the user is under stress during the process.

[1582] Step 9:

[1583] The server receives a request from the user to collect the item. It notifies the lost and found center or storage management department of the request, and requests additional information or identification documents as necessary. Based on the emotion engine, the server provides additional support to reduce the user's stress.

[1584] Step 10:

[1585] The server checks for information on lost items that have been stored for a certain period of time but whose owners have not been found, and starts the re-posting process based on the information found.

[1586] Step 11:

[1587] As part of the reposting process, the server publishes the information to third-party platforms and social media to increase the chances of the owner being found. The server also converts the information into an appropriate format. The emotion engine then follows up with the user appropriately to reduce their anxiety about the reposting.

[1588] Example 2

[1589] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1590] Conventional lost item return systems search for similarities based only on characteristic information entered by the user, and do not take into account the user's emotional state, making them inadequate for users who are stressed or anxious. Another issue is that re-posting of lost items when the owner cannot be found is not automated. This results in a poor user experience and often results in lost items not being returned efficiently.

[1591] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input characteristics of a lost item and for an emotion engine to recognize the emotional state; means for a terminal to transmit the input characteristic information and emotional state information to the server; means for the server to store the characteristic information and emotion information in a database; means for the server to search for similar lost items using a generation AI based on the stored characteristic information; means for the server to notify the user of the search results together with a message corresponding to the emotion generated by the emotion engine; means for the user to select the corresponding lost item and carry out procedures for retrieval; and means for the server to re-post information about lost items whose owners have not been found even after being stored for a certain period of time. This enables efficient and prompt return of lost items while taking into account the user's emotional state.

[1592] "User" refers to an individual or corporation that uses the System to enter information about lost items and receive search results.

[1593] A "terminal" is a device used by a user to input information, and includes smartphones, personal computers, etc.

[1594] "Characteristic information" refers to detailed attribute information about the lost item, including elements such as color, shape, size, location, and time.

[1595] "Emotion engine" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[1596] "Emotional state information" refers to data indicating the emotional state of the user as recognized by the emotion engine.

[1597] "Generative AI" refers to artificial intelligence that uses machine learning techniques to generate and search for highly similar data.

[1598] "Database" refers to a collection of information including characteristic information and emotion information stored on a server.

[1599] "Similarity score" refers to a number that indicates the similarity of the data, calculated by the generating AI based on the characteristic information of the lost item.

[1600] "Notification" refers to communication in the form of search results or messages sent from a server to a user.

[1601] "Collection Procedure" refers to the series of steps a User goes through to identify a lost item and confirm its ownership.

[1602] "Reposting" refers to the act of reposting information about lost items that have been stored for a certain period of time but whose owner has not been found on a third-party platform or social media.

[1603] This invention is a system that promptly returns lost items to their owners by using a generative AI and an emotion engine based on the user's input of characteristic information about the lost item. By combining this with the emotion engine, it also has the ability to recognize the user's emotional state and suggest appropriate responses.

[1604] The system has the following configuration:

[1605] 1. User inputs lost item information

[1606] The user launches a dedicated application on a device such as a smartphone or PC. The application displays a form for entering information about the lost item. The user enters detailed information such as color, shape, size, location, and time. The emotion engine also analyzes the user's facial expressions and tone of voice to recognize their emotional state.

[1607] 2. Transmission and storage of information to the server

[1608] The device converts the characteristic information and emotional state information entered by the user into a data packet and sends it to the server. The server then checks the received data packet and stores its contents in a database. The characteristic information and emotional information are managed using a unique ID.

[1609] 3. Search for similar lost items

[1610] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the feature information with existing information in the database and calculates a similarity score. The emotion engine provides assistance to the user, such as reconfirming information, based on the emotion information.

[1611] 4. Notification of search results

[1612] The server compiles a list of search results generated by the AI ​​and notifies the user. The notification includes a message in easy-to-understand language based on the emotional information analyzed by the emotion engine. The notification is sent in a format that can be viewed within the application.

[1613] 5. Procedures for retrieving lost items

[1614] The user selects the lost item from the search results list through the application and initiates the process of claiming it. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress during the process.

[1615] 6. Reposting if the owner cannot be found

[1616] If the owner cannot be found after a certain period of storage, the server will repost the content. The reposting information will be sent via third-party platforms or social media. The emotion engine will follow up to alleviate concerns about the reposting.

[1617] Specific examples

[1618] 1. A user drops a blue wallet at a train station.

[1619] 2. The user opens the app and enters information about the blue wallet (color, shape, size, location, time of loss, etc.). The emotion engine recognizes emotions and, if it detects a panicked expression or tone of voice, prompts the user to reconfirm the information.

[1620] 3. The device converts the input information and emotion information into data packets and sends them to the server.

[1621] 4. The server stores the received information in a database and searches the database using the generative AI and emotion engine. When a matching blue wallet is found, it notifies the user with a message corresponding to the emotion.

[1622] 5. The user checks the blue wallet and makes a "pickup request." If the emotion engine detects stress during the process, the server will provide support.

[1623] 6. If the owner cannot be found after the storage period has expired, the server will repost the information on a third-party platform or social media, and the emotion engine will take measures to alleviate anxiety.

[1624] This system reduces the burden on users, enables quick return of lost items, and improves the user experience through an emotion engine.

[1625] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1626] Step-by-step process instructions

[1627] Step 1:

[1628] The user launches a dedicated application from their smartphone or PC. They then fill out a form within the application to input the characteristics of the lost item (color, shape, size, location, time, etc.). At this time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. The input is the specific attribute information of the lost item and the user's emotional state. The output is the information entered into the form and the recognized emotional state.

[1629] Step 2:

[1630] The device converts the feature information and emotional state information entered by the user into a data packet. The generated data packet includes the user's input data and the emotional state recognized by the emotion engine. Specifically, the application on the device converts the data into JSON format and sends it to the server as an HTTP POST request. The input is the feature information and emotional state information entered by the user. The output is the data packet to be sent to the server.

[1631] Step 3:

[1632] The server receives data packets sent from the device. The received data is analyzed and stored in a database. A unique ID is assigned to the feature information and emotional state information, and they are managed uniquely in the database. The data packets sent from the device are obtained as input. The feature information and emotional state information stored in the database are obtained as output.

[1633] Step 4:

[1634] The server runs a generation AI and emotion engine based on the information stored in the database. The generation AI compares the input feature information with existing data in the database and calculates a similarity score. The emotion engine assists with response based on emotion information. The inputs are existing information in the database and newly saved feature information and emotion information. The output is a similarity score and an assistance message.

[1635] Step 5:

[1636] The server compiles the search result list generated by the generation AI. At the same time, the emotion engine generates an easy-to-understand message based on the user's emotional information. This is sent to the user within the application as a notification. The inputs are the search result list and message obtained from the generation AI and emotion engine. The output is the message to be notified to the user and the search results.

[1637] Step 6:

[1638] The device receives a notification from the server and displays a list of search results on the application screen. The user checks this list and selects the information that corresponds to their lost item. The input is the list of search results and a message notified by the server. The output is the search results displayed in the application and the user's selection.

[1639] Step 7:

[1640] The user identifies the lost item and presses the "Request Collection" button to proceed. The emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user becomes stressed during the process. The input is the user's selection and emotional state. The output is additional information and guidance regarding the collection request.

[1641] Step 8:

[1642] The server receives a collection request from the user and notifies the lost and found center or storage management department of the received information. If necessary, it requests additional information or identification documents. The emotion engine responds to reduce the user's stress. The input is the collection request information sent by the user. The output is a collection request notification to the lost and found center or the department in charge, along with additional request information.

[1643] Step 9:

[1644] If the owner cannot be found after a certain period of storage, the server will repost the item. To repost the item, a third-party platform or social networking site is used, and the information is converted into a specific format and sent. The emotion engine will provide appropriate follow-up to the user to alleviate anxiety about the item being reposted. The input is information about lost items that have passed the storage period. The output is information about the item being reposted to a third-party platform or social networking site, along with a follow-up message.

[1645] (Application example 2)

[1646] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1647] In modern society, lost items frequently occur in public places and other crowded locations, and the process of returning them takes a lot of time and effort. In these circumstances, there is a need for a method to accurately and quickly collate a wide variety of lost item information and return them to their owners. It is also important to reduce the stress and impatience felt by users searching for lost items and provide a smooth return process. To solve this problem, a new system that combines an emotion engine and generative AI is required.

[1648] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input characteristics of a lost item, means for a terminal to transmit the input characteristic information and the user's emotional state to the server, means for the server to store the characteristic information and the emotional state in a database, means for the server to search for similar lost items based on the stored characteristic information using a generation AI, means for an emotion engine to recognize the user's emotional state and suggest an appropriate response based on the search result, means for the server to notify the user of the search result in a message corresponding to the user's emotional state, means for the user to select the corresponding lost item and carry out the collection procedure, means for the server to re-post information about lost items that have been stored for a certain period of time but whose owner has not been found, and means for capturing characteristic information via a specific device and comparing it with the database. This allows the user to quickly and reliably find lost items and return them to their owners in a stress-free process.

[1649] "User" means any person or entity that uses the System to enter the characteristics of lost items and receive search results.

[1650] A "terminal" is a device operated by a user, such as a smartphone, a personal computer, or smart glasses.

[1651] "Characteristic information" refers to specific descriptions of the lost item, such as its color, shape, size, location, and time.

[1652] "Emotional state" is information that indicates the user's emotions, and refers to the mental state extracted from facial expressions, tone of voice, etc.

[1653] "Server" refers to a computer system that processes and stores information sent from a terminal and, if necessary, connects to a database.

[1654] "Database" refers to an electronic collection of information for storing and managing characteristic information and emotional states.

[1655] "Generative AI" refers to artificial intelligence that uses machine learning technology to analyze characteristic information and search for lost items that are highly similar.

[1656] An "emotion engine" refers to software that analyzes a user's emotional state in real time and suggests appropriate responses accordingly.

[1657] A "message" is information sent from a server to a user, and may be expressed in the form of text, audio, or an image.

[1658] "Pick-up procedure" refers to a series of operations that a user performs to retrieve a lost item based on a search result.

[1659] "Re-posting" refers to the re-publication of information about lost items that have been stored for a certain period of time but whose owner has not yet reappeared.

[1660] "Specific device" refers to special hardware equipment, such as smart glasses or a camera, that has the ability to capture characteristic information.

[1661] This invention is a system in which a user inputs the characteristics of a lost item, and based on that information, a generative AI and an emotion engine are used to search for similar data and quickly return it to the owner.Furthermore, it has a function that uses the emotion engine to recognize the user's emotional state and suggest appropriate responses.

[1662] The server receives the characteristic information and emotional state information input by the user from the device and stores them in a database according to a format. For example, a user captures an image of a lost item (e.g., a blue wallet) through smart glasses and inputs its characteristic information (color, shape, size, location, time, etc.). The input information is sent to the server via the device, and the server stores it in a database.

[1663] The server then uses the generation AI to match the stored feature information with existing data in the database to search for similar lost items. The generation AI uses machine learning techniques to calculate a similarity score for the feature information and identify items with high scores. During this process, the emotion engine recognizes the user's emotional state and prompts the user to reconfirm the accuracy of the information if they are feeling anxious.

[1664] The server generates search results and sends a notification to the user. The notification includes a message in a user-friendly format based on the emotional information analyzed by the emotion engine. The user then selects the lost item and initiates the procedure for retrieval. During this process, the emotion engine monitors the user's emotional state and provides appropriate guidance and support if the user is under stress.

[1665] Once the user has completed the collection process, the server notifies the lost and found center or storage management department and requests any additional information or identification documents required. If the owner cannot be found, the server stores the lost item for a certain period of time and re-posts it on third-party platforms or social media. At this time, the emotion engine performs appropriate follow-up to alleviate the user's anxiety.

[1666] As a concrete example, let's say a user loses a blue wallet at a train station. The user inputs the wallet's characteristics through the smart glasses. The server receives the information and uses the generative AI to compare it with the database. When the matching blue wallet is found, the user is notified with a message based on the user's emotion. The user confirms the information and proceeds with the procedure to retrieve it, and the emotion engine responds to reduce the user's stress.

[1667] Examples of input prompts for generative AI models include:

[1668] "A blue wallet has been found. Search the database for lost items that match the color, shape, and size of this wallet. At the same time, the staff is feeling anxious. Use the emotion engine to suggest the best response."

[1669] In this way, the present invention not only reduces the burden on the user and allows lost items to be returned to their owners more quickly, but also improves the overall user experience by including an emotion engine.

[1670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1671] Step 1: User enters the characteristics of the lost item

[1672] The user puts on the smart glasses and uses the capture function to take a picture of the lost item (e.g., a blue wallet). The user then inputs characteristic information such as color, shape, size, location, and time of dropping into the application. This input information is simultaneously analyzed by the emotion engine to determine the user's emotional state (impatience or anxiety).

[1673] Input: Feature information of the lost item (color, shape, size, location, time), user's emotional information

[1674] Output: Data packets of feature information and emotion information

[1675] Step 2: The device sends a data packet to the server

[1676] The device converts the input feature information and emotion information into a data packet according to a format, which is then encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[1677] Input: feature information and emotion information data

[1678] Output: Data packets sent to the server

[1679] Step 3: The server receives and stores the data packet

[1680] The server receives the data packets sent from the device and checks their contents. When storing the received information in a database, a unique ID is assigned to each piece of characteristic information, and the information is managed to prevent collisions with other data.

[1681] Input: Data packets of feature information and emotion information

[1682] Output: Feature information and emotion information stored in a database

[1683] Step 4: The server uses the generated AI to search for similar lost items

[1684] The server runs a generative AI based on the feature and emotion information stored in the database. The generative AI analyzes the feature information and compares it with existing data in the database to calculate a similarity score. The emotion engine takes into account the user's emotional state and prompts reconfirmation as necessary.

[1685] Input: feature information and emotion information in the database

[1686] Output: A list of lost items with high similarity scores

[1687] Step 5: The server notifies the user of the search results

[1688] The server compiles a list of similar lost items generated by the AI ​​and creates a message in a format that is easy for the user to understand based on the emotional information analyzed by the emotion engine. This notification is sent to the user in a format that can be viewed within the application.

[1689] Input: List of similar lost items, user's emotional information

[1690] Output: Search result notification sent to user

[1691] Step 6: User selects the lost item and begins collection procedures

[1692] The user receives a notification and checks the list of lost items within the app. If the item is found, the user can proceed to claim it. During this process, an emotion engine monitors the user's emotional state and provides guidance and support if the user is under stress.

[1693] Input: Search result notification, user's emotional state

[1694] Output: User's selection of lost item and notification of completion of claim procedure

[1695] Step 7: The server notifies the lost and found center

[1696] Once the user has completed the collection process, the server notifies the lost and found center or storage department and manages the process of requesting additional information or identification documents, if necessary.

[1697] Input: Notification of completion of collection procedure

[1698] Output: Notification to the Lost and Found Center and request for necessary documents

[1699] Step 8: Reposting if the server owner cannot be found

[1700] If the owner cannot be found after a certain period of time, the server will repost the information on a third-party platform or social networking site, and the emotion engine will follow up and take measures to alleviate concerns about the reposting.

[1701] Input: Lost and found information for reposting

[1702] Output: Reposting notifications to third-party platforms and social media

[1703] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1704] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1705] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1706] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1707] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1708] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1709] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1710] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1711] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1712] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1713] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1714] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1715] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1716] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1717] 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.

[1718] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1719] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1720] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1721] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1722] The above-...

Claims

1. a means for a user to input characteristics of a lost item; A means for transmitting the input characteristic information to a server by the terminal; a means for the server to store the characteristic information in a database; A method for searching for similar lost items using AI based on the feature information stored on the server; A means for the server to notify the user of the search results; A means for a user to select the relevant lost item and carry out procedures for collection; A method for re-posting information on lost items that have been stored on the server for a certain period of time but whose owner has not been found; A system including:

2. The system of claim 1, further comprising means for the generating AI to use machine learning techniques to determine similarity.

3. The system of claim 1, further comprising means for transmitting information to a third party platform or social networking site upon reposting.

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