system

Abstract

Provide a system. 【Solution means】Means for a user to input product information and post hashtags and photos, Means for storing the posted product information in a database, Means for analyzing product information from hashtags and photos using generative AI and searching for related past posting data, Means for notifying the user of matching information, Means for users with a successful match to send and receive messages in real time, Means for processing payments for shipping and service fees, Means for managing product shipping and tracking information, Means for performing product receipt confirmation and user evaluation, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional free market apps and auction sites, it is difficult for users seeking reusable items to easily find desired products, and there are few places for users with unwanted items to easily provide reusable items. Also, in the current system, it is difficult to achieve real-time matching of demand and supply, and it is difficult to satisfy the needs of both parties. Therefore, it is required to more efficiently search for and provide reusable items and realize real-time matching between users.

Means for Solving the Problems

[0005] This invention provides a means for users to input product information and post hashtags and photos, and means for storing the posted product information in a database. Furthermore, it includes means for analyzing product information from hashtags and photos using a generation AI and searching for relevant past posting data, and means for notifying users of matching information. By combining means for matching users to send and receive messages in real time, means for processing payment of shipping and service fees, means for managing product shipping and tracking information, and means for confirming product receipt and user evaluation, this system significantly improves the efficiency of searching for and providing reused goods.

[0006] A "user" refers to an ordinary consumer who uses this system to post product information or search for products.

[0007] "Product information" refers to data posted by users, including the product name, details, hashtags, and photos.

[0008] A "hashtag" is a format in which specific keywords are written with quotation marks (""), and it is a means of classifying product information into a specific category or tag.

[0009] "Photographs" refer to still images recorded by cameras or other photographic devices, and are posted as part of product information.

[0010] A "database" refers to electronic information storage used to record, save, and manage posted product information and past posting data.

[0011] "Generative AI" refers to a program that uses artificial intelligence technology to analyze product information from posted hashtags and photos, and to search for related past posting data.

[0012] "Analysis" refers to the process by which information posted by a generating AI is analyzed to derive meaning and relevance.

[0013] "Real-time" refers to a state where information and data are processed and updated instantly, and become available almost simultaneously.

[0014] "Matching" refers to the process by which product information analyzed by generating AI is linked to users in a way that satisfies supply and demand conditions.

[0015] A "message" refers to electronic text information used for communication between users who have successfully matched with each other.

[0016] "Shipping fee" refers to the transportation costs incurred when shipping goods.

[0017] "Service fee" refers to a fee charged for using the system.

[0018] "Product shipment" refers to the process by which the product owner sends the product to the buyer through a delivery company.

[0019] "Tracking information" refers to information used to track the movement route and status of shipped goods.

[0020] "Receipt confirmation" refers to the process by which the buyer receives the product and the system confirms its receipt.

[0021] "User ratings" refer to the evaluations that users give to their trading partners after a transaction is completed, and are used as a reference for future transactions. [Brief explanation of the drawing]

[0022] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0023] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0024] First, the language used in the following description will be explained.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0035] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0042] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0043] This invention is a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users. The program for this system is described below in natural language.

[0044] System Overview

[0045] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[0046] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[0047] 3. The generation AI analyzes product information based on hashtags and photos, and matches relevant users in real time by referring to past posting data in the database.

[0048] 4. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[0049] 5. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[0050] 6. After the product arrives, the customer confirms receipt and leaves a review, completing the transaction.

[0051] Program processing

[0052] 1. Enter and post product information.

[0053] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[0054] The device temporarily holds the posted data and sends it to the server's API endpoint.

[0055] 2. Data Storage

[0056] The server saves the received post data to the "Posts" table in the database. It also records information such as the post's timestamp.

[0057] 3. AI-based analysis and matching

[0058] The generation AI deployed on the server analyzes the posted data. It analyzes product information from hashtags and photos, and searches for matching candidates by referring to past posting data in the database.

[0059] For example, if user A posts "used books" and user B posts "I want used books," the AI ​​will match these posts.

[0060] 4. Notification of matching information

[0061] The server notifies the relevant users of the matching information. Notifications are sent to the devices of both User A and User B, informing them that a match has been made.

[0062] 5. Sending and receiving messages

[0063] After the user receives the notification, they use the in-app messaging function to confirm the transaction details. For example, user B sends user A the message, "Please send me the used book."

[0064] 6. Payment Processing

[0065] The terminal prompts user B to enter payment information and sends it to the server.

[0066] The server processes the payment and completes the payment using an external payment API.

[0067] 7. Shipping of the product

[0068] User A ships a product and enters tracking information into the app, sending it to the server. The server saves the tracking information in a database and notifies User B.

[0069] 8. Confirmation of receipt and evaluation

[0070] User B receives the product and confirms receipt through the app. At the same time, they rate the trading partner through the app and send the rating to the server.

[0071] The server stores the evaluation information in a database and uses it as reference data for future transactions.

[0072] Specific example

[0073] User A posts a listing for a used book, offering to provide it. Simultaneously, User B posts a listing for a used book, indicating they are looking for that book. A generation AI matches these posts, and the server sends notifications to both users. User B contacts User A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once User B receives the book, they confirm receipt and rate User A.

[0074] In this way, this system allows users to trade in reused goods efficiently and easily.

[0075] The following describes the processing flow.

[0076] Step 1:

[0077] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[0078] Step 2:

[0079] The terminal temporarily stores the entered product information in memory and sends a POST request containing the submitted data to the server's API endpoint.

[0080] Step 3:

[0081] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[0082] Step 4:

[0083] The generation AI acquires new post data and calls an analysis engine running in a cloud environment. It analyzes product information based on hashtags and photos and searches the database for similar past posts.

[0084] Step 5:

[0085] The generating AI uses the analysis results to refer to past posting data in the database and matches relevant users. For example, if user B posts "I want a used book," the generating AI will detect a match with user A's post.

[0086] Step 6:

[0087] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0088] Step 7:

[0089] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0090] Step 8:

[0091] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[0092] Step 9:

[0093] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0094] Step 10:

[0095] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0096] Step 11:

[0097] User B receives the product and presses the receipt confirmation button in the app to confirm receipt. At the same time, User B fills out and submits a form to rate User A.

[0098] Step 12:

[0099] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0100] Through the steps outlined above, the AI ​​reuse matching SNS efficiently manages and supports transactions between users, ensuring smooth progress.

[0101] (Example 1)

[0102] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] Traditional online trading systems lacked sufficient accuracy in matching products and trading efficiency, making it difficult to find suitable matches between users. Furthermore, tracking transactions and managing evaluation information was cumbersome, highlighting the need to improve user convenience.

[0104] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0105] In this invention, the server includes means for users to input product information and post hashtags and images; means for storing the posted product information in a database; means for analyzing product information from hashtags and images using a generative AI model and searching for relevant past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking data; and means for confirming receipt of products and providing user ratings. This enables users to efficiently and reliably match products and conduct transactions.

[0106] A "user" is an individual or legal entity that uses the system to input product information and conduct transactions.

[0107] "Product information" refers to information including the name, details, hashtags, and images of the product that the user wishes to trade.

[0108] A "hashtag" is an identifier inserted into text data to make product information easier to identify.

[0109] "Images" are visual data used to visually represent products offered or requested by a user.

[0110] A "database" is an information storage system for effectively storing and managing product information.

[0111] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and match product information.

[0112] "Matching information" refers to information that identifies potential transaction partners between related users, based on analysis by a generative AI model.

[0113] "Messages" are text-based communications used by matching users to negotiate transaction details in real time.

[0114] The means of processing "payments" refers to electronic payment systems that allow users to pay fees or service charges for transactions.

[0115] "Shipping a product" refers to the act of a seller delivering the product to the buyer.

[0116] "Tracking data" refers to information that allows you to check the delivery status of a product in real time after it has been shipped.

[0117] "Receipt confirmation" is a procedure that allows the system to verify that the buyer has received the product.

[0118] "User ratings" are feedback information that users use to evaluate each other's transaction status after a transaction is completed.

[0119] This system is configured so that users input product information, post hashtags and images to social media, and a generating AI model analyzes this information to assist in matching users. The specific implementation method of the system is described below.

[0120] System Configuration

[0121] This invention is primarily composed of three components: a server, a terminal, and a user.

[0122] 1. Enter and post product information.

[0123] Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input the product name, details, hashtags, and images and post them. For example, they might post a photo of a book with the hashtags "used book" and "used book".

[0124] The terminal temporarily stores the entered product information and sends the data to the server's API endpoint.

[0125] 2. Saving data

[0126] The server stores the received post data in a database. For example, it records information such as product name, details, hashtags, images, and posting date and time in the "Posts" table of the database.

[0127] 3. AI-based analysis and matching

[0128] A generative AI model deployed on the server analyzes the posted data (hashtags and images). The generative AI model uses image analysis and natural language processing techniques to classify product information and extract features.

[0129] The server searches past posting data in the database based on the analysis results of the generated AI model and selects matching candidates between related users. For example, if user A posts "used books" and user B posts "I want used books," the system will match these posts.

[0130] 4. Notification of matching information

[0131] The server sends notifications to users who have been successfully matched. This allows users to confirm that a match has been made through the app's notification function.

[0132] 5. Sending and receiving messages

[0133] After receiving a notification, users can use the app's messaging function to negotiate the details of the transaction. For example, user B can send user A a message saying, "Please send me a used book."

[0134] 6. Payment Processing

[0135] The terminal prompts user B to enter payment information and sends it to the server. The server processes the payment using an external payment API. Once the payment is complete, user B is notified.

[0136] 7. Shipping the product

[0137] User A ships a product and obtains tracking information. User A sends the tracking information to the server via the app. The server stores this information in a database and notifies User B.

[0138] 8. Confirmation of receipt and evaluation

[0139] User B receives the product and confirms receipt through the app. At the same time, they rate the transaction and send it to the server. The server stores the rate information in a database and uses it as reference data for future transactions.

[0140] Specific example

[0141] For example, suppose user A posts a "used book" and wants to offer it. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generative AI model matches these posts, and the server sends notifications to both users. User B contacts user A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once user B receives the book, they confirm receipt and rate user A. In this way, this system allows users to trade used goods efficiently and easily.

[0142] Example of a prompt

[0143] Please generate a product list that corresponds to "used books".

[0144] Please describe the procedure for analyzing posted hashtags and images to match them with relevant users.

[0145] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0146] Step 1:

[0147] The user launches the app and enters product information.

[0148] Specific operation: The user enters the product name, details, hashtags, and image, and then clicks the post button.

[0149] Input: Product name, details, hashtags, image.

[0150] Output: Product information dataset. Temporarily stored on the device.

[0151] Step 2:

[0152] The device sends the entered data to the server's API endpoint.

[0153] Specific operation: The device creates an HTTP POST request and sends data to the server's API endpoint.

[0154] Input: A dataset of product information.

[0155] Output: Product information sent to the server.

[0156] Step 3:

[0157] The server receives the submitted data.

[0158] Specific operation: The server parses the HTTP request it receives and extracts the data payload.

[0159] Input: Product information sent from the device.

[0160] Output: Product information is loaded into the server's memory.

[0161] Step 4:

[0162] The server saves product information to the database.

[0163] Specific operation: The server inserts the extracted product information into the "Posts" table in the database.

[0164] Input: Data payload.

[0165] Output: A new record in the "Posts" table of the database.

[0166] Step 5:

[0167] The AI ​​model deployed on the server analyzes the submitted data.

[0168] Specific operation: The generative AI model extracts features of product information through image analysis and natural language processing, and determines its attributes.

[0169] Input: Database entry data.

[0170] Output: Analysis results of product information.

[0171] Step 6:

[0172] The server searches for relevant past posts based on the analysis results of the generated AI model.

[0173] Specific operation: The server queries the database for relevant posts that match the analysis results.

[0174] Input: Product information analysis results.

[0175] Output: Dataset of matching candidates.

[0176] Step 7:

[0177] The server selects matching candidates and sends a notification to the user.

[0178] Specific operation: The server sends a push notification to the device of the relevant matching candidate user.

[0179] Input: Dataset of matching candidates.

[0180] Output: Notification to the user.

[0181] Step 8:

[0182] The user receives a notification and begins exchanging messages.

[0183] Specific operation: The user clicks the notification and negotiates the terms of the transaction using the in-app messaging function.

[0184] Input: Notification content.

[0185] Output: The content of the message exchange.

[0186] Step 9:

[0187] The terminal prompts the user to enter payment information and sends it to the server.

[0188] Specific operation: The user enters payment information and clicks the submit button. The device sends that information to the server.

[0189] Input: Payment information.

[0190] Output: Payment information sent to the server.

[0191] Step 10:

[0192] The server processes the payment and completes the payment using an external payment API.

[0193] Specific operation: The server calls an external payment API to confirm the payment. If the transaction is successful, user B is notified.

[0194] Input: Payment information.

[0195] Output: Payment completion notification.

[0196] Step 11:

[0197] User A ships the product and enters the tracking information into the app, then sends it to the server.

[0198] Specific action: User A ships the product, obtains the tracking number, enters it into the app, and clicks the submit button.

[0199] Input: Tracking number.

[0200] Output: Tracking information sent to the server.

[0201] Step 12:

[0202] The server saves the tracking information and notifies user B.

[0203] Specific operation: The server saves the tracking information to the database and notifies user B that "the item has been shipped."

[0204] Input: Tracking information.

[0205] Output: Notification to User B.

[0206] Step 13:

[0207] User B receives the product and confirms receipt via the app.

[0208] Specific action: After the product arrives, user B opens the app and clicks the "Confirm Receipt" button.

[0209] Input: Delivered items.

[0210] Output: Receipt confirmation information.

[0211] Step 14:

[0212] User B evaluates User A and sends the evaluation to the server.

[0213] Specific action: User B enters a rating for the trading partner and clicks the submit button.

[0214] Input: Evaluation information.

[0215] Output: Evaluation information sent to the server.

[0216] Step 15:

[0217] The server saves the evaluation information to the database.

[0218] Specific operation: The server saves evaluation information to a database and uses it as reference data for future transactions.

[0219] Input: Evaluation information.

[0220] Output: A database containing evaluation information.

[0221] (Application Example 1)

[0222] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0223] Current e-commerce sites make it difficult for users to efficiently buy, sell, and exchange goods, especially matching used items. Furthermore, managing shipping, payment processing, and tracking information is cumbersome, and a system that centralizes these processes is needed. Additionally, mechanisms for user ratings and ensuring transaction security are lacking. Therefore, a platform is needed that enhances user convenience while allowing for secure transactions.

[0224] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0225] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; means for confirming receipt of products and providing user ratings; and means for buying, selling, and exchanging products using a smartphone application. This enables centralized management of efficient and safe transactions of reused goods, improving user convenience and transaction reliability.

[0226] A "user" is an individual or organization that uses the system to input product information or conduct transactions.

[0227] "Product information" refers to information such as product name, details, price, and images that users enter into the system.

[0228] A "hashtag" is an identifying keyword added to product information, making it easier to search and categorize posted data.

[0229] A "photo" is an image file posted to visually supplement product information.

[0230] "Generative AI" refers to artificial intelligence technology that analyzes input data and provides relevant information.

[0231] A "database" is a system for storing and managing posted product information and transaction data.

[0232] "Matching information" refers to information about suitable trading partners presented as a result of analysis by the generating AI.

[0233] "Notifications" refer to the means by which the system communicates matching information and other important information to the user.

[0234] "Real-time" means that data is sent and received between users instantly and without delay.

[0235] A "message" is text information that users send and receive to confirm transaction details.

[0236] "Payment processing" refers to the process by which a user pays for a transaction or service fee.

[0237] "Tracking information" refers to information used to track the delivery status of a product after it has been shipped.

[0238] "Rating" refers to feedback that a user provides to their trading partner after a transaction is completed.

[0239] A "smartphone application" is software that runs on a smartphone and provides the user with the functions of the present invention.

[0240] System Configuration

[0241] To realize this application, this system includes the following main components.

[0242] Smartphone application: Provides an interface for users to input product information and post hashtags and photos. Works on iOS and Android® devices.

[0243] Generative AI Model: Uses Transformers-based AI such as BERT and GPT to analyze and match product information from hashtags and photos.

[0244] Server and Database: Responsible for backend logic. For example, using Python frameworks such as Django or Flask, and storing data in a PostgreSQL database.

[0245] External APIs: Stripe API is used for payment processing, and the shipping carrier's API is used for tracking information.

[0246] Program processing

[0247] Step 1: Enter and post product information.

[0248] Users enter product information through a smartphone app and post photos along with hashtags. This information is temporarily stored on the user's device.

[0249] Step 2: Sending and saving data

[0250] The device sends the posted data to the server. The server saves the received data to the "Posts" table in the database. The post's timestamp is also recorded at this time.

[0251] Step 3: Analysis and matching using generative AI

[0252] A generation AI installed on the server analyzes past post data in the database. The generation AI analyzes product information based on hashtags and photos and searches for related post data.

[0253] Step 4: Notification of matching information

[0254] The server notifies the user of matching information. Once the user receives the notification, they can confirm in the app that a match has been made.

[0255] Step 5: Sending and receiving messages

[0256] After receiving a notification, users use the in-app messaging function to check the transaction details. For example, a buyer might ask a seller, "Could you tell me the price of the item?"

[0257] Step 6: Payment Processing

[0258] The device receives payment information from the user and sends it to the server. The server completes the payment using an external payment API (such as the Stripe API).

[0259] Step 7: Shipping and Tracking

[0260] The seller ships the item and enters the tracking information into the app. The server saves this information to a database and notifies the buyer.

[0261] Step 8: Confirm receipt and review

[0262] The buyer receives the product and confirms receipt through the app. The buyer also rates the other party within the app, and the server stores the rating information.

[0263] Specific example

[0264] User A posts product information along with "used book" using a smartphone app. Similarly, User B posts "I want a used book." Based on this information, a generative AI model matches the two. The server notifies both users of the matching information, and Users A and B confirm the transaction details via message. User B completes the payment, and User A ships the product. Tracking information can be viewed in the app, and finally, User B receives the product, confirms receipt, and leaves a review.

[0265] Example of a prompt

[0266] "User A, who owns a used book, opens a smartphone app and posts product information with the hashtag 'used book'. Similarly, User B posts 'I want a used book'. A generative AI model analyzes these posts, recognizes that the two users match, and sends a notification. Once both users negotiate and payment is completed, User A ships the item and registers the tracking information in the app. Finally, User B receives the item, and the transaction is complete."

[0267] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0268] Step 1:

[0269] The user opens the smartphone app, enters product information (product name, details), hashtags, and a photo, and posts it. This input data is temporarily stored on the device. Specifically, the user enters data into a form for product information, takes a photo, and uploads it. The input data is converted to JSON format and sent to the server.

[0270] Step 2:

[0271] The device sends the posted data to the server's API endpoint. The server stores this data in the "Posts" table in its database. The post's timestamp is also recorded. Specifically, the device sends a POST request to the server, and the server processes the received data and stores it in the database.

[0272] Step 3:

[0273] A generative AI installed on the server analyzes the posted data in the database. The generative AI analyzes product information from the input hashtags and photos and searches for related past posted data. Specifically, an AI model (e.g., BERT, GPT) analyzes the meaning of the hashtags and uses image recognition technology (e.g., CNN) to extract information from the photos. It lists highly relevant posts as search results and returns that information to the server.

[0274] Step 4:

[0275] Based on the matching candidate data returned by the AI, the server sends notifications to relevant users. Specifically, the server sends push notifications to relevant devices to inform users of the possibility of a transaction. Users receive the notification and confirm that a match has been made in the app.

[0276] Step 5:

[0277] After a user receives a notification, they can send and receive messages in real time using the in-app chat function. Specifically, messages sent by the user are sent to the server and immediately displayed on the recipient's device. This allows for confirmation of transaction details and negotiations to take place.

[0278] Step 6:

[0279] The terminal prompts the buyer for payment information and sends this data to the server. The server uses an external payment API (e.g., Stripe API) to process the payment. Specifically, the server makes an API call and receives confirmation that the payment was successful. This information is then notified to both the buyer and the seller.

[0280] Step 7:

[0281] The seller ships the product and enters the tracking information into the app. The server saves this tracking information in the database and notifies the purchaser. Specifically, the tracking number entered by the seller is sent to the server and stored in the database. When the transmission is complete, a notification is sent to the purchaser, and the delivery status can be checked within the app.

[0282] Step 8:

[0283] When the purchaser receives the product, they confirm the receipt through the app. At the same time, they evaluate the trading partner. The server receives this information and saves it in the database. As a specific operation, the purchaser clicks the receipt button and enters it into the evaluation form. This data is sent to the server and recorded in the database. When the evaluation result is saved, it is used as reference data for future transactions.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0285] This invention combines an emotion engine that recognizes the user's emotion with a system in which the user inputs product information, posts hashtags and photos on SNS, and AI analyzes this information to assist in matching between users. The program of this system will be described in natural language below.

[0286] Overview of the System

[0287] 1. The user uses a terminal such as a smartphone or a personal computer and enters information about the product they want or can offer through the app. Specifically, they enter the product name and detailed information and post the corresponding hashtags and product photos.

[0288] 2. The terminal sends the input product information to the server. The server saves the posted data in the database and analyzes the information using the generative AI.

[0289] 3. The emotion engine analyzes the user's emotions from the posted text and photo data. The analyzed emotion data is an important factor in matching.

[0290] 4. The generation AI analyzes product information based on hashtags and photos, and matches related users by referring to past posting data in the database. In this process, sentiment data provided by the sentiment engine is also taken into consideration.

[0291] 5. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[0292] 6. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[0293] 7. After the product arrives, the user confirms receipt and leaves a review, and the emotion engine is activated again during this review process.

[0294] Program processing

[0295] 1. Enter and post product information.

[0296] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[0297] The device temporarily holds the posted data and sends it to the server's API endpoint.

[0298] 2. Data Storage

[0299] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[0300] 3. Emotional Analysis

[0301] The emotion engine placed on the server analyzes the posted data (articles and photos). Through this analysis, the current emotional state of the user (e.g., joy, sadness, excitement, etc.) is classified, and numericalized emotion data is generated.

[0302] 4. Analysis and Matching by AI

[0303] The generation AI obtains new posted data and analyzes product information from hashtags and photos. At the same time, relevant users are searched from the database considering the emotion data provided by the emotion engine.

[0304] As a specific example, assume user A posts "used books" and wants to offer used books. At the same time, user B posts "want used books" and is looking for that book. At this time, the generation AI matches the posts of user A and user B, and the emotion engine evaluates the emotional state of the users to perform an optimal match.

[0305] 5. Notification of Matching Information

[0306] The server notifies the relevant users of the matching information. Specifically, in - app notifications and push notifications are sent to the terminals of user A and user B to inform them that the matching has been established.

[0307] 6. Sending and Receiving Messages

[0308] The user receives the notification and uses the message function in the app to check the details of the transaction. For example, user B sends a message to user A saying "Please send me the used book". The message is sent to the other party's terminal via the server.

[0309] 7. Payment Processing

[0310] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[0311] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0312] 8. Shipping of the product

[0313] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0314] 9. Confirmation of receipt and evaluation

[0315] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[0316] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0317] Through these detailed steps, the AI ​​reuse matching SNS efficiently manages transactions between users, and by utilizing an emotion engine, it achieves optimal matching and service provision that also takes users' emotions into consideration.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[0321] Step 2:

[0322] The terminal temporarily stores the entered product information in memory and sends a POST request to the server's API endpoint. The posted data includes the product name, hashtags, photos, and user ID.

[0323] Step 3:

[0324] The server analyzes the received post data and saves it to the "Posts" table in the database. During saving, the post's timestamp and user ID are also recorded.

[0325] Step 4:

[0326] The server's emotion engine analyzes the posted text and photo data to evaluate the user's emotional state. As a result of the analysis, it generates numerical data representing emotional states such as joy, sadness, and excitement.

[0327] Step 5:

[0328] The server's AI retrieves newly saved post data and analyzes product information based on hashtags and photos. Simultaneously, it also considers sentiment data provided by the sentiment engine and searches the database for relevant past posts.

[0329] Step 6:

[0330] The generation AI matches relevant users based on the analysis results. For example, if user A's "used books" and user B's "I want used books" match, the emotion engine also evaluates the emotional state of both users and performs the optimal match.

[0331] Step 7:

[0332] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0333] Step 8:

[0334] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0335] Step 9:

[0336] The terminal prompts user B to enter payment information and sends it to the server. This payment information includes credit card information and electronic payment information.

[0337] Step 10:

[0338] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0339] Step 11:

[0340] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0341] Step 12:

[0342] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[0343] Step 13:

[0344] The server saves the receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0347] The problem this invention aims to solve is to facilitate smooth matching and transaction completion in conventional user-to-user product transactions. In particular, matching that does not take emotional factors into consideration can negatively impact transaction satisfaction and success rates. Furthermore, a system is needed to efficiently manage a series of processes such as real-time message sending and receiving, payment processing, product shipping, and receipt confirmation.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes means for users to input information about products and post hashtags and images; means for storing the posted product information in a data management device; means for analyzing product information from hashtags and images using generated artificial intelligence and searching for relevant past posts; means for notifying users of matching information; means for matching users to send and receive information in real time; means for processing payment of delivery and service fees; means for managing product delivery and tracking information; means for confirming receipt of products and providing user ratings; means for analyzing the user's emotions from posted content and images; and means for considering the analyzed emotional data in matching. This enables optimal matching that takes user emotions into account and efficient management of the entire transaction process.

[0350] "Product information" refers to data including the name, details, and features of the product that the user is providing or wants.

[0351] A hashtag is a keyword used to categorize a post and make it easier for other users to find it.

[0352] "Images" are data that includes visual information about products that a user provides or wants.

[0353] A "data management device" refers to any system used to store and manage submitted data.

[0354] "Artificial intelligence" is a technology that analyzes information about products from hashtags, images, etc., to perform optimal matching.

[0355] "Posted information" refers to data including all product information, hashtags, and images entered by the user.

[0356] "Matching information" refers to information used by AI to notify users of potential trading partners who are likely to be the best match among related users.

[0357] "Sending and receiving information" refers to a means of communication that allows users to check transaction details in real time and conduct negotiations.

[0358] "Shipping and service fees" include all costs, including the costs of shipping the goods and server usage fees.

[0359] "Tracking information" refers to data used to check the current delivery status of a shipped item.

[0360] "Receipt confirmation" is a method by which the user confirms whether the product has arrived safely and notifies the seller accordingly.

[0361] "User ratings" refer to feedback information used to evaluate the trading partner after a transaction is completed.

[0362] "Emotion" refers to the user's emotional state as interpreted from the posted text and images.

[0363] "Analyzed emotional data" refers to data that shows the user's emotional state, which has been analyzed and quantified by the emotional engine.

[0364] This invention is a system for facilitating product transactions between users, and its specific embodiment is shown in the following steps. Users access the application using a smartphone or personal computer, input product information, and post hashtags and images. The terminal temporarily stores the posted data and then sends it to the server. The server stores the received data in a data management device and further analyzes it using an emotion engine and generative AI.

[0365] The generation AI model deployed on the server analyzes the posted hashtags and images to extract information about the product. Furthermore, it searches for relevant users by referencing past posting data and performs optimal matching. In this process, the emotion engine analyzes the user's emotions from the posted content and images, and the analyzed emotional data is also taken into consideration in the matching process.

[0366] For example, if user A posts a picture with the captions "Used books" and "I'm offering used books," and user B posts a picture with the captions "I want a used book" and "I'm looking for that book," this system uses generative AI to analyze the content of both posts. At the same time, the emotion engine detects the emotional state of the users, and if, for example, user A is posting with a joyful emotion, the system prioritizes matching that user.

[0367] The server generates matching results based on this information and sends notifications to the relevant users. Users negotiate transaction details in real time using the in-app messaging function. Payment processing is handled by the server in cooperation with an external payment provider using payment data sent from the device. If successful, the information is recorded in the database and the user is notified.

[0368] Subsequently, User A ships the product and enters the tracking number into the app. This information is stored on the server and notified to User B. Once User B receives the product, they confirm receipt within the app and rate User A. The emotion engine also operates during this rating process, analyzing the user's emotional state. The receipt confirmation and rating data are stored on the server's data management system and used as reference for future transactions.

[0369] Example of a prompt:

[0370] "User A posts 'Used books' and 'I'm offering used books,' while User B posts 'I want a used book' and 'I'm looking for that book.' Explain how the AI ​​engine matches these posts."

[0371] Through the above embodiments, transactions between users can be facilitated, and by considering emotional information, it is possible to increase transaction satisfaction and success rates.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user launches the app and enters information about the product.

[0375] Input: Product name, details, hashtags, image.

[0376] Specific operation: The user uploads a "used book," its details, the hashtag "used book," and an image of the product to the app's input form.

[0377] Output: Product information data is generated and temporarily stored on the device.

[0378] Step 2:

[0379] The terminal sends the product information entered by the user to the server.

[0380] Input: Product information data entered by the user.

[0381] Specific operation: The terminal sends a POST request to the server's API endpoint with the temporarily stored product information data.

[0382] Output: Product information data is sent to the server.

[0383] Step 3:

[0384] The server stores the received product information in the data management device.

[0385] Input: Product information data.

[0386] Specific operation: The server analyzes the received product information data and saves it to the "Posts" table in the database. Metadata such as timestamps and user IDs are also recorded at the same time.

[0387] Output: Product information data is saved to the database.

[0388] Step 4:

[0389] The server sends product information to the emotion engine for analysis.

[0390] Input: Product information data.

[0391] Specific operation: The server sends product information data (text and images) to the emotion engine, which then analyzes the user's emotions.

[0392] Output: Numerical emotional data is generated and returned to the server.

[0393] Step 5:

[0394] The generation AI analyzes product information, searches for related past posts, and performs matching.

[0395] Input: Product information data, past post data, emotional data.

[0396] Specific operation: The generating AI analyzes hashtags and images from product information data and searches past posting data. Furthermore, it considers emotional data to select the most suitable matching candidates.

[0397] Output: Information on the best matching candidates.

[0398] Step 6:

[0399] The server notifies relevant users of the matching information.

[0400] Input: Matching candidate information.

[0401] Specific operation: Based on the matching information, the server sends in-app notifications and push notifications to related users (for example, user A and user B).

[0402] Output: Matching information is sent to both User A and User B.

[0403] Step 7:

[0404] The user checks the notification and uses the in-app messaging function to view the transaction details.

[0405] Input: Matching notification.

[0406] Specific action: User A and User B discuss the transaction details using the app's messaging function. For example, User B sends User A the message, "Please send me a used book."

[0407] Output: Transaction details message.

[0408] Step 8:

[0409] The terminal prompts the user to enter payment information and sends it to the server.

[0410] Input: Payment information (credit card information, electronic payment information).

[0411] Specific operation: User B enters payment information into the app, and the device sends that data to the server.

[0412] Output: Payment data is sent to the server.

[0413] Step 9:

[0414] The server calls the payment provider's API to process the payment.

[0415] Input: Payment data.

[0416] Specific operation: The server calls an external payment provider API to process the payment, records the information in the database if successful, and notifies the user.

[0417] Output: Payment confirmation notice; payment information is recorded in the database.

[0418] Step 10:

[0419] User A ships the product and enters the tracking number into the app.

[0420] Input: Tracking number.

[0421] Specific operation: User A hands over the product to the delivery company and enters the received tracking number into the app. The server saves this information to the database and notifies User B.

[0422] Output: Tracking information is saved to the database and user B is notified.

[0423] Step 11:

[0424] User B receives the product and confirms receipt.

[0425] Input: Press the "Confirm Receipt" button.

[0426] Specific action: User B receives the product and presses the "Confirm Receipt" button within the app. At the same time, a form for User A to enter their review appears.

[0427] Output: Receipt confirmation data and evaluation data are sent to the server.

[0428] Step 12:

[0429] The server saves receipt confirmation and evaluation data to the database, and the transaction is completed.

[0430] Input: Receipt confirmation data, evaluation data.

[0431] Specific operation: The server saves the receipt confirmation data and evaluation data to the data management device, indicating that the transaction has been completed.

[0432] Output: Transaction completion data is saved to the database.

[0433] Through these steps, the system can efficiently manage the entire transaction process, including optimal matching that takes user emotions into consideration.

[0434] (Application Example 2)

[0435] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0436] Traditional e-commerce sites often resulted in unsatisfactory transactions because users could not fully understand the emotions and needs of the other party when purchasing or listing products. Furthermore, matching based solely on simple product information was problematic because it failed to provide optimal suggestions tailored to the user's emotions and circumstances.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0438] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for generating emotion data using an emotion engine that analyzes user emotions; means for the generation AI to suggest the most suitable products and sellers considering the user's emotion data; means for notifying users of matching information; means for matching users to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; and means for confirming receipt of products and performing user evaluations. This makes it possible to perform optimal matching that takes user emotions into consideration and realize highly satisfying transactions.

[0439] A "user" is an individual or legal entity that uses the system to purchase or list goods for sale.

[0440] "Product information" refers to information including the name, details, and related data such as hashtags and photos of the product that the user wishes to list or purchase.

[0441] A "hashtag" is a keyword used to identify the characteristics or category of a product, making it easier to search for information on social media and search engines.

[0442] "Generative AI" refers to an algorithm that uses artificial intelligence technology to analyze product information from posted hashtags and photos, searches for relevant past posting data, and performs optimal matching.

[0443] An "emotion engine" refers to a module or technology that analyzes emotional data from user-submitted text and photos and quantifies emotional states.

[0444] A "database" is a digital storage system used to centrally manage and store posted product information, sentiment data, past transaction data, and other similar information.

[0445] "Matching information" refers to information about potential trading partners between users, derived by a generative AI and an emotion engine.

[0446] "Real-time" means that messages are sent and received and information is exchanged between users instantly.

[0447] "Shipping and service fees" refer to the costs of delivering goods through logistics and various expenses related to the use of the system.

[0448] "Tracking information" refers to information that shows where a product is passing through during delivery and what stage it is currently in.

[0449] "Receipt confirmation" refers to the process of confirming that the product has been delivered to the buyer and finalizing that status within the system.

[0450] "User ratings" refer to feedback information used by other users to evaluate the quality and satisfaction level of a transaction based on its outcome.

[0451] "Notifications" refer to messages or alerts sent to inform users about successful matches or the progress of transactions.

[0452] The system that implements this application example includes means for users to input product information and post hashtags and photos, means for saving the posted product information to a database, means for analyzing product information from hashtags and photos using a generative AI and searching for related past posting data, means for generating sentiment data using an sentiment engine that analyzes the user's emotions, means for the generative AI to suggest the most suitable products and sellers considering the user's sentiment data, and means for notifying the user of matching information.

[0453] The server receives product information entered by users using devices such as smartphones and personal computers, and stores this information in a database. The database includes the product name, details, related hashtags, and photos. Next, the server uses generative AI to analyze the product information from hashtags and photos and searches for related past posts. A generative AI model is used for this process.

[0454] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions from the posted text and photos. The emotion engine quantifies the user's emotional state and stores the generated emotion data in a database. The generating AI also refers to this emotion data to suggest the most suitable products and sellers to the user.

[0455] This system also includes a means for notifying users of matching information and for matching users to send and receive messages in real time. Once a transaction is completed, the server processes payment of shipping and service fees and manages product shipping and tracking information. Finally, after the product arrives, it confirms receipt and provides user feedback, storing this information in a database.

[0456] As a concrete example, consider a case where user A posts a photo from their smartphone with the hashtags "old manga" and "old manga." Suppose user B posts "I want old manga," sending information that they are looking for old manga. The emotion engine analyzes that user A has the emotion of "nostalgia," while user B has the emotion of "excitement." The generative AI model matches the two in the most optimal way and notifies the user of the result.

[0457] Example of a prompt:

[0458] "Enter product information and post a photo with the hashtag #oldcomicbooks. Our emotional AI will analyze your emotional state and suggest the best buyer / seller."

[0459] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0460] Step 1:

[0461] Users input product information via their smartphones or computers. Specifically, they input and post the product name, product details text, and related hashtags and photos. The input data, including the product name "Old Comic Books," the hashtag "Old Comic Books," and the photo file, is sent to the server. The device temporarily stores this data and sends a POST request to the server's API endpoint.

[0462] Step 2:

[0463] The server parses the received post data and stores it in the database. The stored data includes the product name, details, hashtags, the path to the photo file, and the poster's user ID and timestamp. At this stage, the input data is converted to the appropriate format and registered in the "Posts" table of the database.

[0464] Step 3:

[0465] An emotion engine deployed on the server analyzes the submitted data (text and photos). The input data consists of the product description and photos. The emotion engine uses natural language processing (NLP) and image recognition technology to quantify the user's emotional state (e.g., "nostalgic" or "excited"). The analysis results in quantified emotion data.

[0466] Step 4:

[0467] The generative AI model acquires new post data and analyzes product information based on hashtags and photos. Furthermore, it considers sentiment data provided by the sentiment engine to perform optimal user matching. Input data includes user sentiment data, product information, and historical related data. Based on this data, the generative AI identifies the most suitable sellers and buyers and generates matching results.

[0468] Step 5:

[0469] The server notifies the relevant users of the generated matching results. The device is notified of the successful match via in-app notifications or push notifications. The notification includes a brief profile of the other user and the reason for the match.

[0470] Step 6:

[0471] Users receive notifications and review and negotiate transaction details through the in-app messaging function. Specific input data includes message text and conversation history. The server manages this message data, enabling real-time sending and receiving.

[0472] Step 7:

[0473] Once a transaction is completed, shipping and service fees are paid through the buyer's device. The input data includes payment information (credit card or electronic payment information), and the server calls the API of an external payment provider to process the payment. A confirmation message is generated as output if the payment is successful.

[0474] Step 8:

[0475] The seller ships the item via their device, and the tracking number provided by the shipping carrier is entered into the app. The server stores this tracking information in a database and notifies the buyer. The input data includes the tracking number and the shipping timestamp.

[0476] Step 9:

[0477] Once the buyer receives the item and presses the "Received" button, the receipt of the item is confirmed. Furthermore, the buyer provides feedback to the seller. The emotion engine also operates during the feedback process, analyzing the user's emotional state. This process saves the receipt confirmation and feedback information to a database.

[0478] By following these steps, a system is created that optimizes matching while considering user emotions, thereby improving transaction satisfaction.

[0479] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0480] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0481] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0482] [Second Embodiment]

[0483] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0484] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0485] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0486] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0487] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0488] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0489] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0490] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0491] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0492] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0493] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0494] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0495] This invention is a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users. The program for this system is described below in natural language.

[0496] System Overview

[0497] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[0498] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[0499] 3. The generation AI analyzes product information based on hashtags and photos, and matches relevant users in real time by referring to past posting data in the database.

[0500] 4. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[0501] 5. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[0502] 6. After the product arrives, the customer confirms receipt and leaves a review, completing the transaction.

[0503] Program processing

[0504] 1. Enter and post product information.

[0505] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[0506] The device temporarily holds the posted data and sends it to the server's API endpoint.

[0507] 2. Data Storage

[0508] The server saves the received post data to the "Posts" table in the database. It also records information such as the post's timestamp.

[0509] 3. AI-based analysis and matching

[0510] The generation AI deployed on the server analyzes the posted data. It analyzes product information from hashtags and photos, and searches for matching candidates by referring to past posting data in the database.

[0511] For example, if user A posts "used books" and user B posts "I want used books," the AI ​​will match these posts.

[0512] 4. Notification of matching information

[0513] The server notifies the relevant users of the matching information. Notifications are sent to the devices of both User A and User B, informing them that a match has been made.

[0514] 5. Sending and receiving messages

[0515] After the user receives the notification, they use the in-app messaging function to confirm the transaction details. For example, user B sends user A the message, "Please send me the used book."

[0516] 6. Payment Processing

[0517] The terminal prompts user B to enter payment information and sends it to the server.

[0518] The server processes the payment and completes the payment using an external payment API.

[0519] 7. Shipping of the product

[0520] User A ships a product and enters tracking information into the app, sending it to the server. The server saves the tracking information in a database and notifies User B.

[0521] 8. Confirmation of receipt and evaluation

[0522] User B receives the product and confirms receipt through the app. At the same time, they rate the trading partner through the app and send the rating to the server.

[0523] The server stores the evaluation information in a database and uses it as reference data for future transactions.

[0524] Specific example

[0525] User A posts a listing for a used book, offering to provide it. Simultaneously, User B posts a listing for a used book, indicating they are looking for that book. A generation AI matches these posts, and the server sends notifications to both users. User B contacts User A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once User B receives the book, they confirm receipt and rate User A.

[0526] In this way, this system allows users to trade in reused goods efficiently and easily.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[0530] Step 2:

[0531] The terminal temporarily stores the entered product information in memory and sends a POST request containing the submitted data to the server's API endpoint.

[0532] Step 3:

[0533] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[0534] Step 4:

[0535] The generation AI acquires new post data and calls an analysis engine running in a cloud environment. It analyzes product information based on hashtags and photos and searches the database for similar past posts.

[0536] Step 5:

[0537] The generating AI uses the analysis results to refer to past posting data in the database and matches relevant users. For example, if user B posts "I want a used book," the generating AI will detect a match with user A's post.

[0538] Step 6:

[0539] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0540] Step 7:

[0541] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0542] Step 8:

[0543] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[0544] Step 9:

[0545] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0546] Step 10:

[0547] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0548] Step 11:

[0549] User B receives the product and presses the receipt confirmation button in the app to confirm receipt. At the same time, User B fills out and submits a form to rate User A.

[0550] Step 12:

[0551] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0552] Through the steps outlined above, the AI ​​reuse matching SNS efficiently manages and supports transactions between users, ensuring smooth progress.

[0553] (Example 1)

[0554] Next, we will describe Example 1. 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."

[0555] Traditional online trading systems lacked sufficient accuracy in matching products and trading efficiency, making it difficult to find suitable matches between users. Furthermore, tracking transactions and managing evaluation information was cumbersome, highlighting the need to improve user convenience.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0557] In this invention, the server includes means for users to input product information and post hashtags and images; means for storing the posted product information in a database; means for analyzing product information from hashtags and images using a generative AI model and searching for relevant past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking data; and means for confirming receipt of products and providing user ratings. This enables users to efficiently and reliably match products and conduct transactions.

[0558] A "user" is an individual or legal entity that uses the system to input product information and conduct transactions.

[0559] "Product information" refers to information including the name, details, hashtags, and images of the product that the user wishes to trade.

[0560] A "hashtag" is an identifier inserted into text data to make product information easier to identify.

[0561] "Images" are visual data used to visually represent products offered or requested by a user.

[0562] A "database" is an information storage system for effectively storing and managing product information.

[0563] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and match product information.

[0564] "Matching information" refers to information that identifies potential transaction partners between related users, based on analysis by a generative AI model.

[0565] "Messages" are text-based communications used by matching users to negotiate transaction details in real time.

[0566] The means of processing "payments" refers to electronic payment systems that allow users to pay fees or service charges for transactions.

[0567] "Shipping a product" refers to the act of a seller delivering the product to the buyer.

[0568] "Tracking data" refers to information that allows you to check the delivery status of a product in real time after it has been shipped.

[0569] "Receipt confirmation" is a procedure that allows the system to verify that the buyer has received the product.

[0570] "User ratings" are feedback information that users use to evaluate each other's transaction status after a transaction is completed.

[0571] This system is configured so that users input product information, post hashtags and images to social media, and a generating AI model analyzes this information to assist in matching users. The specific implementation method of the system is described below.

[0572] System Configuration

[0573] This invention is primarily composed of three components: a server, a terminal, and a user.

[0574] 1. Enter and post product information.

[0575] Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input the product name, details, hashtags, and images and post them. For example, they might post a photo of a book with the hashtags "used book" and "used book".

[0576] The terminal temporarily stores the entered product information and sends the data to the server's API endpoint.

[0577] 2. Saving data

[0578] The server stores the received post data in a database. For example, it records information such as product name, details, hashtags, images, and posting date and time in the "Posts" table of the database.

[0579] 3. AI-based analysis and matching

[0580] A generative AI model deployed on the server analyzes the posted data (hashtags and images). The generative AI model uses image analysis and natural language processing techniques to classify product information and extract features.

[0581] The server searches past posting data in the database based on the analysis results of the generated AI model and selects matching candidates between related users. For example, if user A posts "used books" and user B posts "I want used books," the system will match these posts.

[0582] 4. Notification of matching information

[0583] The server sends notifications to users who have been successfully matched. This allows users to confirm that a match has been made through the app's notification function.

[0584] 5. Sending and receiving messages

[0585] After receiving a notification, users can use the app's messaging function to negotiate the details of the transaction. For example, user B can send user A a message saying, "Please send me a used book."

[0586] 6. Payment Processing

[0587] The terminal prompts user B to enter payment information and sends it to the server. The server processes the payment using an external payment API. Once the payment is complete, user B is notified.

[0588] 7. Shipping the product

[0589] User A ships a product and obtains tracking information. User A sends the tracking information to the server via the app. The server stores this information in a database and notifies User B.

[0590] 8. Confirmation of receipt and evaluation

[0591] User B receives the product and confirms receipt through the app. At the same time, they rate the transaction and send it to the server. The server stores the rate information in a database and uses it as reference data for future transactions.

[0592] Specific example

[0593] For example, suppose user A posts a "used book" and wants to offer it. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generative AI model matches these posts, and the server sends notifications to both users. User B contacts user A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once user B receives the book, they confirm receipt and rate user A. In this way, this system allows users to trade used goods efficiently and easily.

[0594] Example of a prompt

[0595] Please generate a product list that corresponds to "used books".

[0596] Please describe the procedure for analyzing posted hashtags and images to match them with relevant users.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] The user launches the app and enters product information.

[0600] Specific operation: The user enters the product name, details, hashtags, and image, and then clicks the post button.

[0601] Input: Product name, details, hashtags, image.

[0602] Output: Product information dataset. Temporarily stored on the device.

[0603] Step 2:

[0604] The device sends the entered data to the server's API endpoint.

[0605] Specific operation: The device creates an HTTP POST request and sends data to the server's API endpoint.

[0606] Input: A dataset of product information.

[0607] Output: Product information sent to the server.

[0608] Step 3:

[0609] The server receives the submitted data.

[0610] Specific operation: The server parses the HTTP request it receives and extracts the data payload.

[0611] Input: Product information sent from the device.

[0612] Output: Product information is loaded into the server's memory.

[0613] Step 4:

[0614] The server saves product information to the database.

[0615] Specific operation: The server inserts the extracted product information into the "Posts" table in the database.

[0616] Input: Data payload.

[0617] Output: A new record in the "Posts" table of the database.

[0618] Step 5:

[0619] The AI ​​model deployed on the server analyzes the submitted data.

[0620] Specific operation: The generative AI model extracts features of product information through image analysis and natural language processing, and determines its attributes.

[0621] Input: Database entry data.

[0622] Output: Analysis results of product information.

[0623] Step 6:

[0624] The server searches for relevant past posts based on the analysis results of the generated AI model.

[0625] Specific operation: The server queries the database for relevant posts that match the analysis results.

[0626] Input: Product information analysis results.

[0627] Output: Dataset of matching candidates.

[0628] Step 7:

[0629] The server selects matching candidates and sends a notification to the user.

[0630] Specific operation: The server sends a push notification to the device of the relevant matching candidate user.

[0631] Input: Dataset of matching candidates.

[0632] Output: Notification to the user.

[0633] Step 8:

[0634] The user receives a notification and begins exchanging messages.

[0635] Specific operation: The user clicks the notification and negotiates the terms of the transaction using the in-app messaging function.

[0636] Input: Notification content.

[0637] Output: The content of the message exchange.

[0638] Step 9:

[0639] The terminal prompts the user to enter payment information and sends it to the server.

[0640] Specific operation: The user enters payment information and clicks the submit button. The device sends that information to the server.

[0641] Input: Payment information.

[0642] Output: Payment information sent to the server.

[0643] Step 10:

[0644] The server processes the payment and completes the payment using an external payment API.

[0645] Specific operation: The server calls an external payment API to confirm the payment. If the transaction is successful, user B is notified.

[0646] Input: Payment information.

[0647] Output: Payment completion notification.

[0648] Step 11:

[0649] User A ships the product and enters the tracking information into the app, then sends it to the server.

[0650] Specific action: User A ships the product, obtains the tracking number, enters it into the app, and clicks the submit button.

[0651] Input: Tracking number.

[0652] Output: Tracking information sent to the server.

[0653] Step 12:

[0654] The server saves the tracking information and notifies user B.

[0655] Specific operation: The server saves the tracking information to the database and notifies user B that "the item has been shipped."

[0656] Input: Tracking information.

[0657] Output: Notification to User B.

[0658] Step 13:

[0659] User B receives the product and confirms receipt via the app.

[0660] Specific action: After the product arrives, user B opens the app and clicks the "Confirm Receipt" button.

[0661] Input: Delivered items.

[0662] Output: Receipt confirmation information.

[0663] Step 14:

[0664] User B evaluates User A and sends the evaluation to the server.

[0665] Specific action: User B enters a rating for the trading partner and clicks the submit button.

[0666] Input: Evaluation information.

[0667] Output: Evaluation information sent to the server.

[0668] Step 15:

[0669] The server saves the evaluation information to the database.

[0670] Specific operation: The server saves evaluation information to a database and uses it as reference data for future transactions.

[0671] Input: Evaluation information.

[0672] Output: A database containing evaluation information.

[0673] (Application Example 1)

[0674] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0675] Current e-commerce sites make it difficult for users to efficiently buy, sell, and exchange goods, especially matching used items. Furthermore, managing shipping, payment processing, and tracking information is cumbersome, and a system that centralizes these processes is needed. Additionally, mechanisms for user ratings and ensuring transaction security are lacking. Therefore, a platform is needed that enhances user convenience while allowing for secure transactions.

[0676] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0677] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; means for confirming receipt of products and providing user ratings; and means for buying, selling, and exchanging products using a smartphone application. This enables centralized management of efficient and safe transactions of reused goods, improving user convenience and transaction reliability.

[0678] A "user" is an individual or organization that uses the system to input product information or conduct transactions.

[0679] "Product information" refers to information such as product name, details, price, and images that users enter into the system.

[0680] A "hashtag" is an identifying keyword added to product information, making it easier to search and categorize posted data.

[0681] A "photo" is an image file posted to visually supplement product information.

[0682] "Generative AI" refers to artificial intelligence technology that analyzes input data and provides relevant information.

[0683] A "database" is a system for storing and managing posted product information and transaction data.

[0684] "Matching information" refers to information about suitable trading partners presented as a result of analysis by the generating AI.

[0685] "Notifications" refer to the means by which the system communicates matching information and other important information to the user.

[0686] "Real-time" means that data is sent and received between users instantly and without delay.

[0687] A "message" is text information that users send and receive to confirm transaction details.

[0688] "Payment processing" refers to the process by which a user pays for a transaction or service fee.

[0689] "Tracking information" refers to information used to track the delivery status of a product after it has been shipped.

[0690] "Rating" refers to feedback that a user provides to their trading partner after a transaction is completed.

[0691] A "smartphone application" is software that runs on a smartphone and provides the user with the functions of the present invention.

[0692] System Configuration

[0693] To realize this application, this system includes the following main components.

[0694] Smartphone application: Provides an interface for users to enter product information and post hashtags and photos. Works on iOS and Android devices.

[0695] Generative AI Model: Uses Transformers-based AI such as BERT and GPT to analyze and match product information from hashtags and photos.

[0696] Server and Database: Responsible for backend logic. For example, using Python frameworks such as Django or Flask, and storing data in a PostgreSQL database.

[0697] External APIs: Stripe API is used for payment processing, and the shipping carrier's API is used for tracking information.

[0698] Program processing

[0699] Step 1: Enter and post product information.

[0700] Users enter product information through a smartphone app and post photos along with hashtags. This information is temporarily stored on the user's device.

[0701] Step 2: Sending and saving data

[0702] The device sends the posted data to the server. The server saves the received data to the "Posts" table in the database. The post's timestamp is also recorded at this time.

[0703] Step 3: Analysis and matching using generative AI

[0704] A generation AI installed on the server analyzes past post data in the database. The generation AI analyzes product information based on hashtags and photos and searches for related post data.

[0705] Step 4: Notification of matching information

[0706] The server notifies the user of matching information. Once the user receives the notification, they can confirm in the app that a match has been made.

[0707] Step 5: Sending and receiving messages

[0708] After receiving a notification, users use the in-app messaging function to check the transaction details. For example, a buyer might ask a seller, "Could you tell me the price of the item?"

[0709] Step 6: Payment Processing

[0710] The device receives payment information from the user and sends it to the server. The server completes the payment using an external payment API (such as the Stripe API).

[0711] Step 7: Shipping and Tracking

[0712] The seller ships the item and enters the tracking information into the app. The server saves this information to a database and notifies the buyer.

[0713] Step 8: Confirm receipt and review

[0714] The buyer receives the product and confirms receipt through the app. The buyer also rates the other party within the app, and the server stores the rating information.

[0715] Specific example

[0716] User A posts product information along with "used book" using a smartphone app. Similarly, User B posts "I want a used book." Based on this information, a generative AI model matches the two. The server notifies both users of the matching information, and Users A and B confirm the transaction details via message. User B completes the payment, and User A ships the product. Tracking information can be viewed in the app, and finally, User B receives the product, confirms receipt, and leaves a review.

[0717] Example of a prompt

[0718] "User A, who owns a used book, opens a smartphone app and posts product information with the hashtag 'used book'. Similarly, User B posts 'I want a used book'. A generative AI model analyzes these posts, recognizes that the two users match, and sends a notification. Once both users negotiate and payment is completed, User A ships the item and registers the tracking information in the app. Finally, User B receives the item, and the transaction is complete."

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The user opens the smartphone app, enters product information (product name, details), hashtags, and a photo, and posts it. This input data is temporarily stored on the device. Specifically, the user enters data into a form for product information, takes a photo, and uploads it. The input data is converted to JSON format and sent to the server.

[0722] Step 2:

[0723] The device sends the posted data to the server's API endpoint. The server stores this data in the "Posts" table in its database. The post's timestamp is also recorded. Specifically, the device sends a POST request to the server, and the server processes the received data and stores it in the database.

[0724] Step 3:

[0725] A generative AI installed on the server analyzes the posted data in the database. The generative AI analyzes product information from the input hashtags and photos and searches for related past posted data. Specifically, an AI model (e.g., BERT, GPT) analyzes the meaning of the hashtags and uses image recognition technology (e.g., CNN) to extract information from the photos. It lists highly relevant posts as search results and returns that information to the server.

[0726] Step 4:

[0727] Based on the matching candidate data returned by the AI, the server sends notifications to relevant users. Specifically, the server sends push notifications to relevant devices to inform users of the possibility of a transaction. Users receive the notification and confirm that a match has been made in the app.

[0728] Step 5:

[0729] After a user receives a notification, they can send and receive messages in real time using the in-app chat function. Specifically, messages sent by the user are sent to the server and immediately displayed on the recipient's device. This allows for confirmation of transaction details and negotiations to take place.

[0730] Step 6:

[0731] The terminal prompts the buyer for payment information and sends this data to the server. The server uses an external payment API (e.g., Stripe API) to process the payment. Specifically, the server makes an API call and receives confirmation that the payment was successful. This information is then notified to both the buyer and the seller.

[0732] Step 7:

[0733] The seller ships the item and enters the tracking information into the app. The server saves this tracking information to a database and notifies the buyer. Specifically, the tracking number entered by the seller is sent to the server and stored in the database. Once the transmission is complete, the buyer receives a notification and can check the delivery status within the app.

[0734] Step 8:

[0735] Once the buyer receives the product, they confirm receipt through the app. Simultaneously, they rate the seller. The server receives this information and stores it in a database. Specifically, the buyer clicks a "received" button and fills out a rating form. This data is sent to the server and recorded in the database. Once the rating results are saved, they are used as reference data for future transactions.

[0736] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0737] This invention combines a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users with each other, with an emotion engine that recognizes the user's emotions. The program for this system is described below in natural language.

[0738] System Overview

[0739] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[0740] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[0741] 3. The emotion engine analyzes the user's emotions from the posted text and photo data. The analyzed emotion data is an important factor in matching.

[0742] 4. The generation AI analyzes product information based on hashtags and photos, and matches related users by referring to past posting data in the database. In this process, sentiment data provided by the sentiment engine is also taken into consideration.

[0743] 5. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[0744] 6. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[0745] 7. After the product arrives, the user confirms receipt and leaves a review, and the emotion engine is activated again during this review process.

[0746] Program processing

[0747] 1. Enter and post product information.

[0748] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[0749] The device temporarily holds the posted data and sends it to the server's API endpoint.

[0750] 2. Data Storage

[0751] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[0752] 3. Emotion analysis

[0753] An emotion engine located on the server analyzes the submitted data (text and photos). This analysis classifies the user's current emotional state (e.g., joy, sadness, excitement) and generates numerical emotion data.

[0754] 4. AI-based analysis and matching

[0755] The generation AI retrieves new post data and analyzes product information from hashtags and photos. At the same time, it searches the database for relevant users, taking into account sentiment data provided by the sentiment engine.

[0756] As a concrete example, suppose user A posts "used books" and wants to offer used books. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generation AI matches user A's post with user B's post, and the emotion engine evaluates the users' emotional states to perform the optimal match.

[0757] 5. Notification of matching information

[0758] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0759] 6. Sending and receiving messages

[0760] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0761] 7. Payment Processing

[0762] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[0763] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0764] 8. Shipping of the product

[0765] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0766] 9. Confirmation of receipt and evaluation

[0767] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[0768] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0769] Through these detailed steps, the AI ​​reuse matching SNS efficiently manages transactions between users, and by utilizing an emotion engine, it achieves optimal matching and service provision that also takes users' emotions into consideration.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[0773] Step 2:

[0774] The terminal temporarily stores the entered product information in memory and sends a POST request to the server's API endpoint. The posted data includes the product name, hashtags, photos, and user ID.

[0775] Step 3:

[0776] The server analyzes the received post data and saves it to the "Posts" table in the database. During saving, the post's timestamp and user ID are also recorded.

[0777] Step 4:

[0778] The server's emotion engine analyzes the posted text and photo data to evaluate the user's emotional state. As a result of the analysis, it generates numerical data representing emotional states such as joy, sadness, and excitement.

[0779] Step 5:

[0780] The server's AI retrieves newly saved post data and analyzes product information based on hashtags and photos. Simultaneously, it also considers sentiment data provided by the sentiment engine and searches the database for relevant past posts.

[0781] Step 6:

[0782] The generation AI matches relevant users based on the analysis results. For example, if user A's "used books" and user B's "I want used books" match, the emotion engine also evaluates the emotional state of both users and performs the optimal match.

[0783] Step 7:

[0784] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0785] Step 8:

[0786] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0787] Step 9:

[0788] The terminal prompts user B to enter payment information and sends it to the server. This payment information includes credit card information and electronic payment information.

[0789] Step 10:

[0790] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0791] Step 11:

[0792] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[0793] Step 12:

[0794] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[0795] Step 13:

[0796] The server saves the receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[0797] (Example 2)

[0798] Next, we will describe Example 2. 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".

[0799] The problem this invention aims to solve is to facilitate smooth matching and transaction completion in conventional user-to-user product transactions. In particular, matching that does not take emotional factors into consideration can negatively impact transaction satisfaction and success rates. Furthermore, a system is needed to efficiently manage a series of processes such as real-time message sending and receiving, payment processing, product shipping, and receipt confirmation.

[0800] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0801] In this invention, the server includes means for users to input information about products and post hashtags and images; means for storing the posted product information in a data management device; means for analyzing product information from hashtags and images using generated artificial intelligence and searching for relevant past posts; means for notifying users of matching information; means for matching users to send and receive information in real time; means for processing payment of delivery and service fees; means for managing product delivery and tracking information; means for confirming receipt of products and providing user ratings; means for analyzing the user's emotions from posted content and images; and means for considering the analyzed emotional data in matching. This enables optimal matching that takes user emotions into account and efficient management of the entire transaction process.

[0802] "Product information" refers to data including the name, details, and features of the product that the user is providing or wants.

[0803] A hashtag is a keyword used to categorize a post and make it easier for other users to find it.

[0804] "Images" are data that includes visual information about products that a user provides or wants.

[0805] A "data management device" refers to any system used to store and manage submitted data.

[0806] "Artificial intelligence" is a technology that analyzes information about products from hashtags, images, etc., to perform optimal matching.

[0807] "Posted information" refers to data including all product information, hashtags, and images entered by the user.

[0808] "Matching information" refers to information used by AI to notify users of potential trading partners who are likely to be the best match among related users.

[0809] "Sending and receiving information" refers to a means of communication that allows users to check transaction details in real time and conduct negotiations.

[0810] "Shipping and service fees" include all costs, including the costs of shipping the goods and server usage fees.

[0811] "Tracking information" refers to data used to check the current delivery status of a shipped item.

[0812] "Receipt confirmation" is a method by which the user confirms whether the product has arrived safely and notifies the seller accordingly.

[0813] "User ratings" refer to feedback information used to evaluate the trading partner after a transaction is completed.

[0814] "Emotion" refers to the user's emotional state as interpreted from the posted text and images.

[0815] "Analyzed emotional data" refers to data that shows the user's emotional state, which has been analyzed and quantified by the emotional engine.

[0816] This invention is a system for facilitating product transactions between users, and its specific embodiment is shown in the following steps. Users access the application using a smartphone or personal computer, input product information, and post hashtags and images. The terminal temporarily stores the posted data and then sends it to the server. The server stores the received data in a data management device and further analyzes it using an emotion engine and generative AI.

[0817] The generation AI model deployed on the server analyzes the posted hashtags and images to extract information about the product. Furthermore, it searches for relevant users by referencing past posting data and performs optimal matching. In this process, the emotion engine analyzes the user's emotions from the posted content and images, and the analyzed emotional data is also taken into consideration in the matching process.

[0818] For example, if user A posts a picture with the captions "Used books" and "I'm offering used books," and user B posts a picture with the captions "I want a used book" and "I'm looking for that book," this system uses generative AI to analyze the content of both posts. At the same time, the emotion engine detects the emotional state of the users, and if, for example, user A is posting with a joyful emotion, the system prioritizes matching that user.

[0819] The server generates matching results based on this information and sends notifications to the relevant users. Users negotiate transaction details in real time using the in-app messaging function. Payment processing is handled by the server in cooperation with an external payment provider using payment data sent from the device. If successful, the information is recorded in the database and the user is notified.

[0820] Subsequently, User A ships the product and enters the tracking number into the app. This information is stored on the server and notified to User B. Once User B receives the product, they confirm receipt within the app and rate User A. The emotion engine also operates during this rating process, analyzing the user's emotional state. The receipt confirmation and rating data are stored on the server's data management system and used as reference for future transactions.

[0821] Example of a prompt:

[0822] "User A posts 'Used books' and 'I'm offering used books,' while User B posts 'I want a used book' and 'I'm looking for that book.' Explain how the AI ​​engine matches these posts."

[0823] Through the above embodiments, transactions between users can be facilitated, and by considering emotional information, it is possible to increase transaction satisfaction and success rates.

[0824] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0825] Step 1:

[0826] The user launches the app and enters information about the product.

[0827] Input: Product name, details, hashtags, image.

[0828] Specific operation: The user uploads a "used book," its details, the hashtag "used book," and an image of the product to the app's input form.

[0829] Output: Product information data is generated and temporarily stored on the device.

[0830] Step 2:

[0831] The terminal sends the product information entered by the user to the server.

[0832] Input: Product information data entered by the user.

[0833] Specific operation: The terminal sends a POST request to the server's API endpoint with the temporarily stored product information data.

[0834] Output: Product information data is sent to the server.

[0835] Step 3:

[0836] The server stores the received product information in the data management device.

[0837] Input: Product information data.

[0838] Specific operation: The server analyzes the received product information data and saves it to the "Posts" table in the database. Metadata such as timestamps and user IDs are also recorded at the same time.

[0839] Output: Product information data is saved to the database.

[0840] Step 4:

[0841] The server sends product information to the emotion engine for analysis.

[0842] Input: Product information data.

[0843] Specific operation: The server sends product information data (text and images) to the emotion engine, which then analyzes the user's emotions.

[0844] Output: Numerical emotional data is generated and returned to the server.

[0845] Step 5:

[0846] The generation AI analyzes product information, searches for related past posts, and performs matching.

[0847] Input: Product information data, past post data, emotional data.

[0848] Specific operation: The generating AI analyzes hashtags and images from product information data and searches past posting data. Furthermore, it considers emotional data to select the most suitable matching candidates.

[0849] Output: Information on the best matching candidates.

[0850] Step 6:

[0851] The server notifies relevant users of the matching information.

[0852] Input: Matching candidate information.

[0853] Specific operation: Based on the matching information, the server sends in-app notifications and push notifications to related users (for example, user A and user B).

[0854] Output: Matching information is sent to both User A and User B.

[0855] Step 7:

[0856] The user checks the notification and uses the in-app messaging function to view the transaction details.

[0857] Input: Matching notification.

[0858] Specific action: User A and User B discuss the transaction details using the app's messaging function. For example, User B sends User A the message, "Please send me a used book."

[0859] Output: Transaction details message.

[0860] Step 8:

[0861] The terminal prompts the user to enter payment information and sends it to the server.

[0862] Input: Payment information (credit card information, electronic payment information).

[0863] Specific operation: User B enters payment information into the app, and the device sends that data to the server.

[0864] Output: Payment data is sent to the server.

[0865] Step 9:

[0866] The server calls the payment provider's API to process the payment.

[0867] Input: Payment data.

[0868] Specific operation: The server calls an external payment provider API to process the payment, records the information in the database if successful, and notifies the user.

[0869] Output: Payment confirmation notice; payment information is recorded in the database.

[0870] Step 10:

[0871] User A ships the product and enters the tracking number into the app.

[0872] Input: Tracking number.

[0873] Specific operation: User A hands over the product to the delivery company and enters the received tracking number into the app. The server saves this information to the database and notifies User B.

[0874] Output: Tracking information is saved to the database and user B is notified.

[0875] Step 11:

[0876] User B receives the product and confirms receipt.

[0877] Input: Press the "Confirm Receipt" button.

[0878] Specific action: User B receives the product and presses the "Confirm Receipt" button within the app. At the same time, a form for User A to enter their review appears.

[0879] Output: Receipt confirmation data and evaluation data are sent to the server.

[0880] Step 12:

[0881] The server saves receipt confirmation and evaluation data to the database, and the transaction is completed.

[0882] Input: Receipt confirmation data, evaluation data.

[0883] Specific operation: The server saves the receipt confirmation data and evaluation data to the data management device, indicating that the transaction has been completed.

[0884] Output: Transaction completion data is saved to the database.

[0885] Through these steps, the system can efficiently manage the entire transaction process, including optimal matching that takes user emotions into consideration.

[0886] (Application Example 2)

[0887] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0888] Traditional e-commerce sites often resulted in unsatisfactory transactions because users could not fully understand the emotions and needs of the other party when purchasing or listing products. Furthermore, matching based solely on simple product information was problematic because it failed to provide optimal suggestions tailored to the user's emotions and circumstances.

[0889] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0890] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for generating emotion data using an emotion engine that analyzes user emotions; means for the generation AI to suggest the most suitable products and sellers considering the user's emotion data; means for notifying users of matching information; means for matching users to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; and means for confirming receipt of products and performing user evaluations. This makes it possible to perform optimal matching that takes user emotions into consideration and realize highly satisfying transactions.

[0891] A "user" is an individual or legal entity that uses the system to purchase or list goods for sale.

[0892] "Product information" refers to information including the name, details, and related data such as hashtags and photos of the product that the user wishes to list or purchase.

[0893] A "hashtag" is a keyword used to identify the characteristics or category of a product, making it easier to search for information on social media and search engines.

[0894] "Generative AI" refers to an algorithm that uses artificial intelligence technology to analyze product information from posted hashtags and photos, searches for relevant past posting data, and performs optimal matching.

[0895] An "emotion engine" refers to a module or technology that analyzes emotional data from user-submitted text and photos and quantifies emotional states.

[0896] A "database" is a digital storage system used to centrally manage and store posted product information, sentiment data, past transaction data, and other similar information.

[0897] "Matching information" refers to information about potential trading partners between users, derived by a generative AI and an emotion engine.

[0898] "Real-time" means that messages are sent and received and information is exchanged between users instantly.

[0899] "Shipping and service fees" refer to the costs of delivering goods through logistics and various expenses related to the use of the system.

[0900] "Tracking information" refers to information that shows where a product is passing through during delivery and what stage it is currently in.

[0901] "Receipt confirmation" refers to the process of confirming that the product has been delivered to the buyer and finalizing that status within the system.

[0902] "User ratings" refer to feedback information used by other users to evaluate the quality and satisfaction level of a transaction based on its outcome.

[0903] "Notifications" refer to messages or alerts sent to inform users about successful matches or the progress of transactions.

[0904] The system that implements this application example includes means for users to input product information and post hashtags and photos, means for saving the posted product information to a database, means for analyzing product information from hashtags and photos using a generative AI and searching for related past posting data, means for generating sentiment data using an sentiment engine that analyzes the user's emotions, means for the generative AI to suggest the most suitable products and sellers considering the user's sentiment data, and means for notifying the user of matching information.

[0905] The server receives product information entered by users using devices such as smartphones and personal computers, and stores this information in a database. The database includes the product name, details, related hashtags, and photos. Next, the server uses generative AI to analyze the product information from hashtags and photos and searches for related past posts. A generative AI model is used for this process.

[0906] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions from the posted text and photos. The emotion engine quantifies the user's emotional state and stores the generated emotion data in a database. The generating AI also refers to this emotion data to suggest the most suitable products and sellers to the user.

[0907] This system also includes a means for notifying users of matching information and for matching users to send and receive messages in real time. Once a transaction is completed, the server processes payment of shipping and service fees and manages product shipping and tracking information. Finally, after the product arrives, it confirms receipt and provides user feedback, storing this information in a database.

[0908] As a concrete example, consider a case where user A posts a photo from their smartphone with the hashtags "old manga" and "old manga." Suppose user B posts "I want old manga," sending information that they are looking for old manga. The emotion engine analyzes that user A has the emotion of "nostalgia," while user B has the emotion of "excitement." The generative AI model matches the two in the most optimal way and notifies the user of the result.

[0909] Example of a prompt:

[0910] "Enter product information and post a photo with the hashtag #oldcomicbooks. Our emotional AI will analyze your emotional state and suggest the best buyer / seller."

[0911] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0912] Step 1:

[0913] Users input product information via their smartphones or computers. Specifically, they input and post the product name, product details text, and related hashtags and photos. The input data, including the product name "Old Comic Books," the hashtag "Old Comic Books," and the photo file, is sent to the server. The device temporarily stores this data and sends a POST request to the server's API endpoint.

[0914] Step 2:

[0915] The server parses the received post data and stores it in the database. The stored data includes the product name, details, hashtags, the path to the photo file, and the poster's user ID and timestamp. At this stage, the input data is converted to the appropriate format and registered in the "Posts" table of the database.

[0916] Step 3:

[0917] An emotion engine deployed on the server analyzes the submitted data (text and photos). The input data consists of the product description and photos. The emotion engine uses natural language processing (NLP) and image recognition technology to quantify the user's emotional state (e.g., "nostalgic" or "excited"). The analysis results in quantified emotion data.

[0918] Step 4:

[0919] The generative AI model acquires new post data and analyzes product information based on hashtags and photos. Furthermore, it considers sentiment data provided by the sentiment engine to perform optimal user matching. Input data includes user sentiment data, product information, and historical related data. Based on this data, the generative AI identifies the most suitable sellers and buyers and generates matching results.

[0920] Step 5:

[0921] The server notifies the relevant users of the generated matching results. The device is notified of the successful match via in-app notifications or push notifications. The notification includes a brief profile of the other user and the reason for the match.

[0922] Step 6:

[0923] Users receive notifications and review and negotiate transaction details through the in-app messaging function. Specific input data includes message text and conversation history. The server manages this message data, enabling real-time sending and receiving.

[0924] Step 7:

[0925] Once a transaction is completed, shipping and service fees are paid through the buyer's device. The input data includes payment information (credit card or electronic payment information), and the server calls the API of an external payment provider to process the payment. A confirmation message is generated as output if the payment is successful.

[0926] Step 8:

[0927] The seller ships the item via their device, and the tracking number provided by the shipping carrier is entered into the app. The server stores this tracking information in a database and notifies the buyer. The input data includes the tracking number and the shipping timestamp.

[0928] Step 9:

[0929] Once the buyer receives the item and presses the "Received" button, the receipt of the item is confirmed. Furthermore, the buyer provides feedback to the seller. The emotion engine also operates during the feedback process, analyzing the user's emotional state. This process saves the receipt confirmation and feedback information to a database.

[0930] By following these steps, a system is created that optimizes matching while considering user emotions, thereby improving transaction satisfaction.

[0931] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0932] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0933] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0934] [Third Embodiment]

[0935] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0936] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0937] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0938] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0939] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0940] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0941] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0942] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0943] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0944] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0945] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0946] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0947] This invention is a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users. The program for this system is described below in natural language.

[0948] System Overview

[0949] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[0950] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[0951] 3. The generation AI analyzes product information based on hashtags and photos, and matches relevant users in real time by referring to past posting data in the database.

[0952] 4. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[0953] 5. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[0954] 6. After the product arrives, the customer confirms receipt and leaves a review, completing the transaction.

[0955] Program processing

[0956] 1. Enter and post product information.

[0957] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[0958] The device temporarily holds the posted data and sends it to the server's API endpoint.

[0959] 2. Data Storage

[0960] The server saves the received post data to the "Posts" table in the database. It also records information such as the post's timestamp.

[0961] 3. AI-based analysis and matching

[0962] The generation AI deployed on the server analyzes the posted data. It analyzes product information from hashtags and photos, and searches for matching candidates by referring to past posting data in the database.

[0963] For example, if user A posts "used books" and user B posts "I want used books," the AI ​​will match these posts.

[0964] 4. Notification of matching information

[0965] The server notifies the relevant users of the matching information. Notifications are sent to the devices of both User A and User B, informing them that a match has been made.

[0966] 5. Sending and receiving messages

[0967] After the user receives the notification, they use the in-app messaging function to confirm the transaction details. For example, user B sends user A the message, "Please send me the used book."

[0968] 6. Payment Processing

[0969] The terminal prompts user B to enter payment information and sends it to the server.

[0970] The server processes the payment and completes the payment using an external payment API.

[0971] 7. Shipping of the product

[0972] User A ships a product and enters tracking information into the app, sending it to the server. The server saves the tracking information in a database and notifies User B.

[0973] 8. Confirmation of receipt and evaluation

[0974] User B receives the product and confirms receipt through the app. At the same time, they rate the trading partner through the app and send the rating to the server.

[0975] The server stores the evaluation information in a database and uses it as reference data for future transactions.

[0976] Specific example

[0977] User A posts a listing for a used book, offering to provide it. Simultaneously, User B posts a listing for a used book, indicating they are looking for that book. A generation AI matches these posts, and the server sends notifications to both users. User B contacts User A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once User B receives the book, they confirm receipt and rate User A.

[0978] In this way, this system allows users to trade in reused goods efficiently and easily.

[0979] The following describes the processing flow.

[0980] Step 1:

[0981] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[0982] Step 2:

[0983] The terminal temporarily stores the entered product information in memory and sends a POST request containing the submitted data to the server's API endpoint.

[0984] Step 3:

[0985] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[0986] Step 4:

[0987] The generation AI acquires new post data and calls an analysis engine running in a cloud environment. It analyzes product information based on hashtags and photos and searches the database for similar past posts.

[0988] Step 5:

[0989] The generating AI uses the analysis results to refer to past posting data in the database and matches relevant users. For example, if user B posts "I want a used book," the generating AI will detect a match with user A's post.

[0990] Step 6:

[0991] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[0992] Step 7:

[0993] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[0994] Step 8:

[0995] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[0996] Step 9:

[0997] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[0998] Step 10:

[0999] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1000] Step 11:

[1001] User B receives the product and presses the receipt confirmation button in the app to confirm receipt. At the same time, User B fills out and submits a form to rate User A.

[1002] Step 12:

[1003] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1004] Through the steps outlined above, the AI ​​reuse matching SNS efficiently manages and supports transactions between users, ensuring smooth progress.

[1005] (Example 1)

[1006] Next, we will describe Example 1. 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."

[1007] Traditional online trading systems lacked sufficient accuracy in matching products and trading efficiency, making it difficult to find suitable matches between users. Furthermore, tracking transactions and managing evaluation information was cumbersome, highlighting the need to improve user convenience.

[1008] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1009] In this invention, the server includes means for users to input product information and post hashtags and images; means for storing the posted product information in a database; means for analyzing product information from hashtags and images using a generative AI model and searching for relevant past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking data; and means for confirming receipt of products and providing user ratings. This enables users to efficiently and reliably match products and conduct transactions.

[1010] A "user" is an individual or legal entity that uses the system to input product information and conduct transactions.

[1011] "Product information" refers to information including the name, details, hashtags, and images of the product that the user wishes to trade.

[1012] A "hashtag" is an identifier inserted into text data to make product information easier to identify.

[1013] "Images" are visual data used to visually represent products offered or requested by a user.

[1014] A "database" is an information storage system for effectively storing and managing product information.

[1015] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and match product information.

[1016] "Matching information" refers to information that identifies potential transaction partners between related users, based on analysis by a generative AI model.

[1017] "Messages" are text-based communications used by matching users to negotiate transaction details in real time.

[1018] The means of processing "payments" refers to electronic payment systems that allow users to pay fees or service charges for transactions.

[1019] "Shipping a product" refers to the act of a seller delivering the product to the buyer.

[1020] "Tracking data" refers to information that allows you to check the delivery status of a product in real time after it has been shipped.

[1021] "Receipt confirmation" is a procedure that allows the system to verify that the buyer has received the product.

[1022] "User ratings" are feedback information that users use to evaluate each other's transaction status after a transaction is completed.

[1023] This system is configured so that users input product information, post hashtags and images to social media, and a generating AI model analyzes this information to assist in matching users. The specific implementation method of the system is described below.

[1024] System Configuration

[1025] This invention is primarily composed of three components: a server, a terminal, and a user.

[1026] 1. Enter and post product information.

[1027] Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input the product name, details, hashtags, and images and post them. For example, they might post a photo of a book with the hashtags "used book" and "used book".

[1028] The terminal temporarily stores the entered product information and sends the data to the server's API endpoint.

[1029] 2. Saving data

[1030] The server stores the received post data in a database. For example, it records information such as product name, details, hashtags, images, and posting date and time in the "Posts" table of the database.

[1031] 3. AI-based analysis and matching

[1032] A generative AI model deployed on the server analyzes the posted data (hashtags and images). The generative AI model uses image analysis and natural language processing techniques to classify product information and extract features.

[1033] The server searches past posting data in the database based on the analysis results of the generated AI model and selects matching candidates between related users. For example, if user A posts "used books" and user B posts "I want used books," the system will match these posts.

[1034] 4. Notification of matching information

[1035] The server sends notifications to users who have been successfully matched. This allows users to confirm that a match has been made through the app's notification function.

[1036] 5. Sending and receiving messages

[1037] After receiving a notification, users can use the app's messaging function to negotiate the details of the transaction. For example, user B can send user A a message saying, "Please send me a used book."

[1038] 6. Payment Processing

[1039] The terminal prompts user B to enter payment information and sends it to the server. The server processes the payment using an external payment API. Once the payment is complete, user B is notified.

[1040] 7. Shipping the product

[1041] User A ships a product and obtains tracking information. User A sends the tracking information to the server via the app. The server stores this information in a database and notifies User B.

[1042] 8. Confirmation of receipt and evaluation

[1043] User B receives the product and confirms receipt through the app. At the same time, they rate the transaction and send it to the server. The server stores the rate information in a database and uses it as reference data for future transactions.

[1044] Specific example

[1045] For example, suppose user A posts a "used book" and wants to offer it. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generative AI model matches these posts, and the server sends notifications to both users. User B contacts user A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once user B receives the book, they confirm receipt and rate user A. In this way, this system allows users to trade used goods efficiently and easily.

[1046] Example of a prompt

[1047] Please generate a product list that corresponds to "used books".

[1048] Please describe the procedure for analyzing posted hashtags and images to match them with relevant users.

[1049] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1050] Step 1:

[1051] The user launches the app and enters product information.

[1052] Specific operation: The user enters the product name, details, hashtags, and image, and then clicks the post button.

[1053] Input: Product name, details, hashtags, image.

[1054] Output: Product information dataset. Temporarily stored on the device.

[1055] Step 2:

[1056] The device sends the entered data to the server's API endpoint.

[1057] Specific operation: The device creates an HTTP POST request and sends data to the server's API endpoint.

[1058] Input: A dataset of product information.

[1059] Output: Product information sent to the server.

[1060] Step 3:

[1061] The server receives the submitted data.

[1062] Specific operation: The server parses the HTTP request it receives and extracts the data payload.

[1063] Input: Product information sent from the device.

[1064] Output: Product information is loaded into the server's memory.

[1065] Step 4:

[1066] The server saves product information to the database.

[1067] Specific operation: The server inserts the extracted product information into the "Posts" table in the database.

[1068] Input: Data payload.

[1069] Output: A new record in the "Posts" table of the database.

[1070] Step 5:

[1071] The AI ​​model deployed on the server analyzes the submitted data.

[1072] Specific operation: The generative AI model extracts features of product information through image analysis and natural language processing, and determines its attributes.

[1073] Input: Database entry data.

[1074] Output: Analysis results of product information.

[1075] Step 6:

[1076] The server searches for relevant past posts based on the analysis results of the generated AI model.

[1077] Specific operation: The server queries the database for relevant posts that match the analysis results.

[1078] Input: Product information analysis results.

[1079] Output: Dataset of matching candidates.

[1080] Step 7:

[1081] The server selects matching candidates and sends a notification to the user.

[1082] Specific operation: The server sends a push notification to the device of the relevant matching candidate user.

[1083] Input: Dataset of matching candidates.

[1084] Output: Notification to the user.

[1085] Step 8:

[1086] The user receives a notification and begins exchanging messages.

[1087] Specific operation: The user clicks the notification and negotiates the terms of the transaction using the in-app messaging function.

[1088] Input: Notification content.

[1089] Output: The content of the message exchange.

[1090] Step 9:

[1091] The terminal prompts the user to enter payment information and sends it to the server.

[1092] Specific operation: The user enters payment information and clicks the submit button. The device sends that information to the server.

[1093] Input: Payment information.

[1094] Output: Payment information sent to the server.

[1095] Step 10:

[1096] The server processes the payment and completes the payment using an external payment API.

[1097] Specific operation: The server calls an external payment API to confirm the payment. If the transaction is successful, user B is notified.

[1098] Input: Payment information.

[1099] Output: Payment completion notification.

[1100] Step 11:

[1101] User A ships the product and enters the tracking information into the app, then sends it to the server.

[1102] Specific action: User A ships the product, obtains the tracking number, enters it into the app, and clicks the submit button.

[1103] Input: Tracking number.

[1104] Output: Tracking information sent to the server.

[1105] Step 12:

[1106] The server saves the tracking information and notifies user B.

[1107] Specific operation: The server saves the tracking information to the database and notifies user B that "the item has been shipped."

[1108] Input: Tracking information.

[1109] Output: Notification to User B.

[1110] Step 13:

[1111] User B receives the product and confirms receipt via the app.

[1112] Specific action: After the product arrives, user B opens the app and clicks the "Confirm Receipt" button.

[1113] Input: Delivered items.

[1114] Output: Receipt confirmation information.

[1115] Step 14:

[1116] User B evaluates User A and sends the evaluation to the server.

[1117] Specific action: User B enters a rating for the trading partner and clicks the submit button.

[1118] Input: Evaluation information.

[1119] Output: Evaluation information sent to the server.

[1120] Step 15:

[1121] The server saves the evaluation information to the database.

[1122] Specific operation: The server saves evaluation information to a database and uses it as reference data for future transactions.

[1123] Input: Evaluation information.

[1124] Output: A database containing evaluation information.

[1125] (Application Example 1)

[1126] Next, we will explain Application Example 1. In the following explanation, 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."

[1127] Current e-commerce sites make it difficult for users to efficiently buy, sell, and exchange goods, especially matching used items. Furthermore, managing shipping, payment processing, and tracking information is cumbersome, and a system that centralizes these processes is needed. Additionally, mechanisms for user ratings and ensuring transaction security are lacking. Therefore, a platform is needed that enhances user convenience while allowing for secure transactions.

[1128] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1129] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; means for confirming receipt of products and providing user ratings; and means for buying, selling, and exchanging products using a smartphone application. This enables centralized management of efficient and safe transactions of reused goods, improving user convenience and transaction reliability.

[1130] A "user" is an individual or organization that uses the system to input product information or conduct transactions.

[1131] "Product information" refers to information such as product name, details, price, and images that users enter into the system.

[1132] A "hashtag" is an identifying keyword added to product information, making it easier to search and categorize posted data.

[1133] A "photo" is an image file posted to visually supplement product information.

[1134] "Generative AI" refers to artificial intelligence technology that analyzes input data and provides relevant information.

[1135] A "database" is a system for storing and managing posted product information and transaction data.

[1136] "Matching information" refers to information about suitable trading partners presented as a result of analysis by the generating AI.

[1137] "Notifications" refer to the means by which the system communicates matching information and other important information to the user.

[1138] "Real-time" means that data is sent and received between users instantly and without delay.

[1139] A "message" is text information that users send and receive to confirm transaction details.

[1140] "Payment processing" refers to the process by which a user pays for a transaction or service fee.

[1141] "Tracking information" refers to information used to track the delivery status of a product after it has been shipped.

[1142] "Rating" refers to feedback that a user provides to their trading partner after a transaction is completed.

[1143] A "smartphone application" is software that runs on a smartphone and provides the user with the functions of the present invention.

[1144] System Configuration

[1145] To realize this application, this system includes the following main components.

[1146] Smartphone application: Provides an interface for users to enter product information and post hashtags and photos. Works on iOS and Android devices.

[1147] Generative AI Model: Uses Transformers-based AI such as BERT and GPT to analyze and match product information from hashtags and photos.

[1148] Server and Database: Responsible for backend logic. For example, using Python frameworks such as Django or Flask, and storing data in a PostgreSQL database.

[1149] External APIs: Stripe API is used for payment processing, and the shipping carrier's API is used for tracking information.

[1150] Program processing

[1151] Step 1: Enter and post product information.

[1152] Users enter product information through a smartphone app and post photos along with hashtags. This information is temporarily stored on the user's device.

[1153] Step 2: Sending and saving data

[1154] The device sends the posted data to the server. The server saves the received data to the "Posts" table in the database. The post's timestamp is also recorded at this time.

[1155] Step 3: Analysis and matching using generative AI

[1156] A generation AI installed on the server analyzes past post data in the database. The generation AI analyzes product information based on hashtags and photos and searches for related post data.

[1157] Step 4: Notification of matching information

[1158] The server notifies the user of matching information. Once the user receives the notification, they can confirm in the app that a match has been made.

[1159] Step 5: Sending and receiving messages

[1160] After receiving a notification, users use the in-app messaging function to check the transaction details. For example, a buyer might ask a seller, "Could you tell me the price of the item?"

[1161] Step 6: Payment Processing

[1162] The device receives payment information from the user and sends it to the server. The server completes the payment using an external payment API (such as the Stripe API).

[1163] Step 7: Shipping and Tracking

[1164] The seller ships the item and enters the tracking information into the app. The server saves this information to a database and notifies the buyer.

[1165] Step 8: Confirm receipt and review

[1166] The buyer receives the product and confirms receipt through the app. The buyer also rates the other party within the app, and the server stores the rating information.

[1167] Specific example

[1168] User A posts product information along with "used book" using a smartphone app. Similarly, User B posts "I want a used book." Based on this information, a generative AI model matches the two. The server notifies both users of the matching information, and Users A and B confirm the transaction details via message. User B completes the payment, and User A ships the product. Tracking information can be viewed in the app, and finally, User B receives the product, confirms receipt, and leaves a review.

[1169] Example of a prompt

[1170] "User A, who owns a used book, opens a smartphone app and posts product information with the hashtag 'used book'. Similarly, User B posts 'I want a used book'. A generative AI model analyzes these posts, recognizes that the two users match, and sends a notification. Once both users negotiate and payment is completed, User A ships the item and registers the tracking information in the app. Finally, User B receives the item, and the transaction is complete."

[1171] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1172] Step 1:

[1173] The user opens the smartphone app, enters product information (product name, details), hashtags, and a photo, and posts it. This input data is temporarily stored on the device. Specifically, the user enters data into a form for product information, takes a photo, and uploads it. The input data is converted to JSON format and sent to the server.

[1174] Step 2:

[1175] The device sends the posted data to the server's API endpoint. The server stores this data in the "Posts" table in its database. The post's timestamp is also recorded. Specifically, the device sends a POST request to the server, and the server processes the received data and stores it in the database.

[1176] Step 3:

[1177] A generative AI installed on the server analyzes the posted data in the database. The generative AI analyzes product information from the input hashtags and photos and searches for related past posted data. Specifically, an AI model (e.g., BERT, GPT) analyzes the meaning of the hashtags and uses image recognition technology (e.g., CNN) to extract information from the photos. It lists highly relevant posts as search results and returns that information to the server.

[1178] Step 4:

[1179] Based on the matching candidate data returned by the AI, the server sends notifications to relevant users. Specifically, the server sends push notifications to relevant devices to inform users of the possibility of a transaction. Users receive the notification and confirm that a match has been made in the app.

[1180] Step 5:

[1181] After a user receives a notification, they can send and receive messages in real time using the in-app chat function. Specifically, messages sent by the user are sent to the server and immediately displayed on the recipient's device. This allows for confirmation of transaction details and negotiations to take place.

[1182] Step 6:

[1183] The terminal prompts the buyer for payment information and sends this data to the server. The server uses an external payment API (e.g., Stripe API) to process the payment. Specifically, the server makes an API call and receives confirmation that the payment was successful. This information is then notified to both the buyer and the seller.

[1184] Step 7:

[1185] The seller ships the item and enters the tracking information into the app. The server saves this tracking information to a database and notifies the buyer. Specifically, the tracking number entered by the seller is sent to the server and stored in the database. Once the transmission is complete, the buyer receives a notification and can check the delivery status within the app.

[1186] Step 8:

[1187] Once the buyer receives the product, they confirm receipt through the app. Simultaneously, they rate the seller. The server receives this information and stores it in a database. Specifically, the buyer clicks a "received" button and fills out a rating form. This data is sent to the server and recorded in the database. Once the rating results are saved, they are used as reference data for future transactions.

[1188] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1189] This invention combines a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users with each other, with an emotion engine that recognizes the user's emotions. The program for this system is described below in natural language.

[1190] System Overview

[1191] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[1192] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[1193] 3. The emotion engine analyzes the user's emotions from the posted text and photo data. The analyzed emotion data is an important factor in matching.

[1194] 4. The generation AI analyzes product information based on hashtags and photos, and matches related users by referring to past posting data in the database. In this process, sentiment data provided by the sentiment engine is also taken into consideration.

[1195] 5. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[1196] 6. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[1197] 7. After the product arrives, the user confirms receipt and leaves a review, and the emotion engine is activated again during this review process.

[1198] Program processing

[1199] 1. Enter and post product information.

[1200] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[1201] The device temporarily holds the posted data and sends it to the server's API endpoint.

[1202] 2. Data Storage

[1203] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[1204] 3. Emotion analysis

[1205] An emotion engine located on the server analyzes the submitted data (text and photos). This analysis classifies the user's current emotional state (e.g., joy, sadness, excitement) and generates numerical emotion data.

[1206] 4. AI-based analysis and matching

[1207] The generation AI retrieves new post data and analyzes product information from hashtags and photos. At the same time, it searches the database for relevant users, taking into account sentiment data provided by the sentiment engine.

[1208] As a concrete example, suppose user A posts "used books" and wants to offer used books. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generation AI matches user A's post with user B's post, and the emotion engine evaluates the users' emotional states to perform the optimal match.

[1209] 5. Notification of matching information

[1210] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[1211] 6. Sending and receiving messages

[1212] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[1213] 7. Payment Processing

[1214] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[1215] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[1216] 8. Shipping of the product

[1217] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1218] 9. Confirmation of receipt and evaluation

[1219] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[1220] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1221] Through these detailed steps, the AI ​​reuse matching SNS efficiently manages transactions between users, and by utilizing an emotion engine, it achieves optimal matching and service provision that also takes users' emotions into consideration.

[1222] The following describes the processing flow.

[1223] Step 1:

[1224] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[1225] Step 2:

[1226] The terminal temporarily stores the entered product information in memory and sends a POST request to the server's API endpoint. The posted data includes the product name, hashtags, photos, and user ID.

[1227] Step 3:

[1228] The server analyzes the received post data and saves it to the "Posts" table in the database. During saving, the post's timestamp and user ID are also recorded.

[1229] Step 4:

[1230] The server's emotion engine analyzes the posted text and photo data to evaluate the user's emotional state. As a result of the analysis, it generates numerical data representing emotional states such as joy, sadness, and excitement.

[1231] Step 5:

[1232] The server's AI retrieves newly saved post data and analyzes product information based on hashtags and photos. Simultaneously, it also considers sentiment data provided by the sentiment engine and searches the database for relevant past posts.

[1233] Step 6:

[1234] The generation AI matches relevant users based on the analysis results. For example, if user A's "used books" and user B's "I want used books" match, the emotion engine also evaluates the emotional state of both users and performs the optimal match.

[1235] Step 7:

[1236] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[1237] Step 8:

[1238] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[1239] Step 9:

[1240] The terminal prompts user B to enter payment information and sends it to the server. This payment information includes credit card information and electronic payment information.

[1241] Step 10:

[1242] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[1243] Step 11:

[1244] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1245] Step 12:

[1246] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[1247] Step 13:

[1248] The server saves the receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1249] (Example 2)

[1250] Next, we will describe Example 2. 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."

[1251] The problem this invention aims to solve is to facilitate smooth matching and transaction completion in conventional user-to-user product transactions. In particular, matching that does not take emotional factors into consideration can negatively impact transaction satisfaction and success rates. Furthermore, a system is needed to efficiently manage a series of processes such as real-time message sending and receiving, payment processing, product shipping, and receipt confirmation.

[1252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1253] In this invention, the server includes means for users to input information about products and post hashtags and images; means for storing the posted product information in a data management device; means for analyzing product information from hashtags and images using generated artificial intelligence and searching for relevant past posts; means for notifying users of matching information; means for matching users to send and receive information in real time; means for processing payment of delivery and service fees; means for managing product delivery and tracking information; means for confirming receipt of products and providing user ratings; means for analyzing the user's emotions from posted content and images; and means for considering the analyzed emotional data in matching. This enables optimal matching that takes user emotions into account and efficient management of the entire transaction process.

[1254] "Product information" refers to data including the name, details, and features of the product that the user is providing or wants.

[1255] A hashtag is a keyword used to categorize a post and make it easier for other users to find it.

[1256] "Images" are data that includes visual information about products that a user provides or wants.

[1257] A "data management device" refers to any system used to store and manage submitted data.

[1258] "Artificial intelligence" is a technology that analyzes information about products from hashtags, images, etc., to perform optimal matching.

[1259] "Posted information" refers to data including all product information, hashtags, and images entered by the user.

[1260] "Matching information" refers to information used by AI to notify users of potential trading partners who are likely to be the best match among related users.

[1261] "Sending and receiving information" refers to a means of communication that allows users to check transaction details in real time and conduct negotiations.

[1262] "Shipping and service fees" include all costs, including the costs of shipping the goods and server usage fees.

[1263] "Tracking information" refers to data used to check the current delivery status of a shipped item.

[1264] "Receipt confirmation" is a method by which the user confirms whether the product has arrived safely and notifies the seller accordingly.

[1265] "User ratings" refer to feedback information used to evaluate the trading partner after a transaction is completed.

[1266] "Emotion" refers to the user's emotional state as interpreted from the posted text and images.

[1267] "Analyzed emotional data" refers to data that shows the user's emotional state, which has been analyzed and quantified by the emotional engine.

[1268] This invention is a system for facilitating product transactions between users, and its specific embodiment is shown in the following steps. Users access the application using a smartphone or personal computer, input product information, and post hashtags and images. The terminal temporarily stores the posted data and then sends it to the server. The server stores the received data in a data management device and further analyzes it using an emotion engine and generative AI.

[1269] The generation AI model deployed on the server analyzes the posted hashtags and images to extract information about the product. Furthermore, it searches for relevant users by referencing past posting data and performs optimal matching. In this process, the emotion engine analyzes the user's emotions from the posted content and images, and the analyzed emotional data is also taken into consideration in the matching process.

[1270] For example, if user A posts a picture with the captions "Used books" and "I'm offering used books," and user B posts a picture with the captions "I want a used book" and "I'm looking for that book," this system uses generative AI to analyze the content of both posts. At the same time, the emotion engine detects the emotional state of the users, and if, for example, user A is posting with a joyful emotion, the system prioritizes matching that user.

[1271] The server generates matching results based on this information and sends notifications to the relevant users. Users negotiate transaction details in real time using the in-app messaging function. Payment processing is handled by the server in cooperation with an external payment provider using payment data sent from the device. If successful, the information is recorded in the database and the user is notified.

[1272] Subsequently, User A ships the product and enters the tracking number into the app. This information is stored on the server and notified to User B. Once User B receives the product, they confirm receipt within the app and rate User A. The emotion engine also operates during this rating process, analyzing the user's emotional state. The receipt confirmation and rating data are stored on the server's data management system and used as reference for future transactions.

[1273] Example of a prompt:

[1274] "User A posts 'Used books' and 'I'm offering used books,' while User B posts 'I want a used book' and 'I'm looking for that book.' Explain how the AI ​​engine matches these posts."

[1275] Through the above embodiments, transactions between users can be facilitated, and by considering emotional information, it is possible to increase transaction satisfaction and success rates.

[1276] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1277] Step 1:

[1278] The user launches the app and enters information about the product.

[1279] Input: Product name, details, hashtags, image.

[1280] Specific operation: The user uploads a "used book," its details, the hashtag "used book," and an image of the product to the app's input form.

[1281] Output: Product information data is generated and temporarily stored on the device.

[1282] Step 2:

[1283] The terminal sends the product information entered by the user to the server.

[1284] Input: Product information data entered by the user.

[1285] Specific operation: The terminal sends a POST request to the server's API endpoint with the temporarily stored product information data.

[1286] Output: Product information data is sent to the server.

[1287] Step 3:

[1288] The server stores the received product information in the data management device.

[1289] Input: Product information data.

[1290] Specific operation: The server analyzes the received product information data and saves it to the "Posts" table in the database. Metadata such as timestamps and user IDs are also recorded at the same time.

[1291] Output: Product information data is saved to the database.

[1292] Step 4:

[1293] The server sends product information to the emotion engine for analysis.

[1294] Input: Product information data.

[1295] Specific operation: The server sends product information data (text and images) to the emotion engine, which then analyzes the user's emotions.

[1296] Output: Numerical emotional data is generated and returned to the server.

[1297] Step 5:

[1298] The generation AI analyzes product information, searches for related past posts, and performs matching.

[1299] Input: Product information data, past post data, emotional data.

[1300] Specific operation: The generating AI analyzes hashtags and images from product information data and searches past posting data. Furthermore, it considers emotional data to select the most suitable matching candidates.

[1301] Output: Information on the best matching candidates.

[1302] Step 6:

[1303] The server notifies relevant users of the matching information.

[1304] Input: Matching candidate information.

[1305] Specific operation: Based on the matching information, the server sends in-app notifications and push notifications to related users (for example, user A and user B).

[1306] Output: Matching information is sent to both User A and User B.

[1307] Step 7:

[1308] The user checks the notification and uses the in-app messaging function to view the transaction details.

[1309] Input: Matching notification.

[1310] Specific action: User A and User B discuss the transaction details using the app's messaging function. For example, User B sends User A the message, "Please send me a used book."

[1311] Output: Transaction details message.

[1312] Step 8:

[1313] The terminal prompts the user to enter payment information and sends it to the server.

[1314] Input: Payment information (credit card information, electronic payment information).

[1315] Specific operation: User B enters payment information into the app, and the device sends that data to the server.

[1316] Output: Payment data is sent to the server.

[1317] Step 9:

[1318] The server calls the payment provider's API to process the payment.

[1319] Input: Payment data.

[1320] Specific operation: The server calls an external payment provider API to process the payment, records the information in the database if successful, and notifies the user.

[1321] Output: Payment confirmation notice; payment information is recorded in the database.

[1322] Step 10:

[1323] User A ships the product and enters the tracking number into the app.

[1324] Input: Tracking number.

[1325] Specific operation: User A hands over the product to the delivery company and enters the received tracking number into the app. The server saves this information to the database and notifies User B.

[1326] Output: Tracking information is saved to the database and user B is notified.

[1327] Step 11:

[1328] User B receives the product and confirms receipt.

[1329] Input: Press the "Confirm Receipt" button.

[1330] Specific action: User B receives the product and presses the "Confirm Receipt" button within the app. At the same time, a form for User A to enter their review appears.

[1331] Output: Receipt confirmation data and evaluation data are sent to the server.

[1332] Step 12:

[1333] The server saves receipt confirmation and evaluation data to the database, and the transaction is completed.

[1334] Input: Receipt confirmation data, evaluation data.

[1335] Specific operation: The server saves the receipt confirmation data and evaluation data to the data management device, indicating that the transaction has been completed.

[1336] Output: Transaction completion data is saved to the database.

[1337] Through these steps, the system can efficiently manage the entire transaction process, including optimal matching that takes user emotions into consideration.

[1338] (Application Example 2)

[1339] Next, we will explain application example 2. In the following explanation, 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."

[1340] Traditional e-commerce sites often resulted in unsatisfactory transactions because users could not fully understand the emotions and needs of the other party when purchasing or listing products. Furthermore, matching based solely on simple product information was problematic because it failed to provide optimal suggestions tailored to the user's emotions and circumstances.

[1341] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1342] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for generating emotion data using an emotion engine that analyzes user emotions; means for the generation AI to suggest the most suitable products and sellers considering the user's emotion data; means for notifying users of matching information; means for matching users to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; and means for confirming receipt of products and performing user evaluations. This makes it possible to perform optimal matching that takes user emotions into consideration and realize highly satisfying transactions.

[1343] A "user" is an individual or legal entity that uses the system to purchase or list goods for sale.

[1344] "Product information" refers to information including the name, details, and related data such as hashtags and photos of the product that the user wishes to list or purchase.

[1345] A "hashtag" is a keyword used to identify the characteristics or category of a product, making it easier to search for information on social media and search engines.

[1346] "Generative AI" refers to an algorithm that uses artificial intelligence technology to analyze product information from posted hashtags and photos, searches for relevant past posting data, and performs optimal matching.

[1347] An "emotion engine" refers to a module or technology that analyzes emotional data from user-submitted text and photos and quantifies emotional states.

[1348] A "database" is a digital storage system used to centrally manage and store posted product information, sentiment data, past transaction data, and other similar information.

[1349] "Matching information" refers to information about potential trading partners between users, derived by a generative AI and an emotion engine.

[1350] "Real-time" means that messages are sent and received and information is exchanged between users instantly.

[1351] "Shipping and service fees" refer to the costs of delivering goods through logistics and various expenses related to the use of the system.

[1352] "Tracking information" refers to information that shows where a product is passing through during delivery and what stage it is currently in.

[1353] "Receipt confirmation" refers to the process of confirming that the product has been delivered to the buyer and finalizing that status within the system.

[1354] "User ratings" refer to feedback information used by other users to evaluate the quality and satisfaction level of a transaction based on its outcome.

[1355] "Notifications" refer to messages or alerts sent to inform users about successful matches or the progress of transactions.

[1356] The system that implements this application example includes means for users to input product information and post hashtags and photos, means for saving the posted product information to a database, means for analyzing product information from hashtags and photos using a generative AI and searching for related past posting data, means for generating sentiment data using an sentiment engine that analyzes the user's emotions, means for the generative AI to suggest the most suitable products and sellers considering the user's sentiment data, and means for notifying the user of matching information.

[1357] The server receives product information entered by users using devices such as smartphones and personal computers, and stores this information in a database. The database includes the product name, details, related hashtags, and photos. Next, the server uses generative AI to analyze the product information from hashtags and photos and searches for related past posts. A generative AI model is used for this process.

[1358] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions from the posted text and photos. The emotion engine quantifies the user's emotional state and stores the generated emotion data in a database. The generating AI also refers to this emotion data to suggest the most suitable products and sellers to the user.

[1359] This system also includes a means for notifying users of matching information and for matching users to send and receive messages in real time. Once a transaction is completed, the server processes payment of shipping and service fees and manages product shipping and tracking information. Finally, after the product arrives, it confirms receipt and provides user feedback, storing this information in a database.

[1360] As a concrete example, consider a case where user A posts a photo from their smartphone with the hashtags "old manga" and "old manga." Suppose user B posts "I want old manga," sending information that they are looking for old manga. The emotion engine analyzes that user A has the emotion of "nostalgia," while user B has the emotion of "excitement." The generative AI model matches the two in the most optimal way and notifies the user of the result.

[1361] Example of a prompt:

[1362] "Enter product information and post a photo with the hashtag #oldcomicbooks. Our emotional AI will analyze your emotional state and suggest the best buyer / seller."

[1363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1364] Step 1:

[1365] Users input product information via their smartphones or computers. Specifically, they input and post the product name, product details text, and related hashtags and photos. The input data, including the product name "Old Comic Books," the hashtag "Old Comic Books," and the photo file, is sent to the server. The device temporarily stores this data and sends a POST request to the server's API endpoint.

[1366] Step 2:

[1367] The server parses the received post data and stores it in the database. The stored data includes the product name, details, hashtags, the path to the photo file, and the poster's user ID and timestamp. At this stage, the input data is converted to the appropriate format and registered in the "Posts" table of the database.

[1368] Step 3:

[1369] An emotion engine deployed on the server analyzes the submitted data (text and photos). The input data consists of the product description and photos. The emotion engine uses natural language processing (NLP) and image recognition technology to quantify the user's emotional state (e.g., "nostalgic" or "excited"). The analysis results in quantified emotion data.

[1370] Step 4:

[1371] The generative AI model acquires new post data and analyzes product information based on hashtags and photos. Furthermore, it considers sentiment data provided by the sentiment engine to perform optimal user matching. Input data includes user sentiment data, product information, and historical related data. Based on this data, the generative AI identifies the most suitable sellers and buyers and generates matching results.

[1372] Step 5:

[1373] The server notifies the relevant users of the generated matching results. The device is notified of the successful match via in-app notifications or push notifications. The notification includes a brief profile of the other user and the reason for the match.

[1374] Step 6:

[1375] Users receive notifications and review and negotiate transaction details through the in-app messaging function. Specific input data includes message text and conversation history. The server manages this message data, enabling real-time sending and receiving.

[1376] Step 7:

[1377] Once a transaction is completed, shipping and service fees are paid through the buyer's device. The input data includes payment information (credit card or electronic payment information), and the server calls the API of an external payment provider to process the payment. A confirmation message is generated as output if the payment is successful.

[1378] Step 8:

[1379] The seller ships the item via their device, and the tracking number provided by the shipping carrier is entered into the app. The server stores this tracking information in a database and notifies the buyer. The input data includes the tracking number and the shipping timestamp.

[1380] Step 9:

[1381] Once the buyer receives the item and presses the "Received" button, the receipt of the item is confirmed. Furthermore, the buyer provides feedback to the seller. The emotion engine also operates during the feedback process, analyzing the user's emotional state. This process saves the receipt confirmation and feedback information to a database.

[1382] By following these steps, a system is created that optimizes matching while considering user emotions, thereby improving transaction satisfaction.

[1383] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1384] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1386] [Fourth Embodiment]

[1387] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1388] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1390] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1394] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1395] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1398] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1400] This invention is a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users. The program for this system is described below in natural language.

[1401] System Overview

[1402] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[1403] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[1404] 3. The generation AI analyzes product information based on hashtags and photos, and matches relevant users in real time by referring to past posting data in the database.

[1405] 4. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[1406] 5. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[1407] 6. After the product arrives, the customer confirms receipt and leaves a review, completing the transaction.

[1408] Program processing

[1409] 1. Enter and post product information.

[1410] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[1411] The device temporarily holds the posted data and sends it to the server's API endpoint.

[1412] 2. Data Storage

[1413] The server saves the received post data to the "Posts" table in the database. It also records information such as the post's timestamp.

[1414] 3. AI-based analysis and matching

[1415] The generation AI deployed on the server analyzes the posted data. It analyzes product information from hashtags and photos, and searches for matching candidates by referring to past posting data in the database.

[1416] For example, if user A posts "used books" and user B posts "I want used books," the AI ​​will match these posts.

[1417] 4. Notification of matching information

[1418] The server notifies the relevant users of the matching information. Notifications are sent to the devices of both User A and User B, informing them that a match has been made.

[1419] 5. Sending and receiving messages

[1420] After the user receives the notification, they use the in-app messaging function to confirm the transaction details. For example, user B sends user A the message, "Please send me the used book."

[1421] 6. Payment Processing

[1422] The terminal prompts user B to enter payment information and sends it to the server.

[1423] The server processes the payment and completes the payment using an external payment API.

[1424] 7. Shipping of the product

[1425] User A ships a product and enters tracking information into the app, sending it to the server. The server saves the tracking information in a database and notifies User B.

[1426] 8. Confirmation of receipt and evaluation

[1427] User B receives the product and confirms receipt through the app. At the same time, they rate the trading partner through the app and send the rating to the server.

[1428] The server stores the evaluation information in a database and uses it as reference data for future transactions.

[1429] Specific example

[1430] User A posts a listing for a used book, offering to provide it. Simultaneously, User B posts a listing for a used book, indicating they are looking for that book. A generation AI matches these posts, and the server sends notifications to both users. User B contacts User A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once User B receives the book, they confirm receipt and rate User A.

[1431] In this way, this system allows users to trade in reused goods efficiently and easily.

[1432] The following describes the processing flow.

[1433] Step 1:

[1434] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[1435] Step 2:

[1436] The terminal temporarily stores the entered product information in memory and sends a POST request containing the submitted data to the server's API endpoint.

[1437] Step 3:

[1438] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[1439] Step 4:

[1440] The generation AI acquires new post data and calls an analysis engine running in a cloud environment. It analyzes product information based on hashtags and photos and searches the database for similar past posts.

[1441] Step 5:

[1442] The generating AI uses the analysis results to refer to past posting data in the database and matches relevant users. For example, if user B posts "I want a used book," the generating AI will detect a match with user A's post.

[1443] Step 6:

[1444] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[1445] Step 7:

[1446] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[1447] Step 8:

[1448] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[1449] Step 9:

[1450] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[1451] Step 10:

[1452] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1453] Step 11:

[1454] User B receives the product and presses the receipt confirmation button in the app to confirm receipt. At the same time, User B fills out and submits a form to rate User A.

[1455] Step 12:

[1456] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1457] Through the steps outlined above, the AI ​​reuse matching SNS efficiently manages and supports transactions between users, ensuring smooth progress.

[1458] (Example 1)

[1459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1460] Traditional online trading systems lacked sufficient accuracy in matching products and trading efficiency, making it difficult to find suitable matches between users. Furthermore, tracking transactions and managing evaluation information was cumbersome, highlighting the need to improve user convenience.

[1461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1462] In this invention, the server includes means for users to input product information and post hashtags and images; means for storing the posted product information in a database; means for analyzing product information from hashtags and images using a generative AI model and searching for relevant past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking data; and means for confirming receipt of products and providing user ratings. This enables users to efficiently and reliably match products and conduct transactions.

[1463] A "user" is an individual or legal entity that uses the system to input product information and conduct transactions.

[1464] "Product information" refers to information including the name, details, hashtags, and images of the product that the user wishes to trade.

[1465] A "hashtag" is an identifier inserted into text data to make product information easier to identify.

[1466] "Images" are visual data used to visually represent products offered or requested by a user.

[1467] A "database" is an information storage system for effectively storing and managing product information.

[1468] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and match product information.

[1469] "Matching information" refers to information that identifies potential transaction partners between related users, based on analysis by a generative AI model.

[1470] "Messages" are text-based communications used by matching users to negotiate transaction details in real time.

[1471] The means of processing "payments" refers to electronic payment systems that allow users to pay fees or service charges for transactions.

[1472] "Shipping a product" refers to the act of a seller delivering the product to the buyer.

[1473] "Tracking data" refers to information that allows you to check the delivery status of a product in real time after it has been shipped.

[1474] "Receipt confirmation" is a procedure that allows the system to verify that the buyer has received the product.

[1475] "User ratings" are feedback information that users use to evaluate each other's transaction status after a transaction is completed.

[1476] This system is configured so that users input product information, post hashtags and images to social media, and a generating AI model analyzes this information to assist in matching users. The specific implementation method of the system is described below.

[1477] System Configuration

[1478] This invention is primarily composed of three components: a server, a terminal, and a user.

[1479] 1. Enter and post product information.

[1480] Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input the product name, details, hashtags, and images and post them. For example, they might post a photo of a book with the hashtags "used book" and "used book".

[1481] The terminal temporarily stores the entered product information and sends the data to the server's API endpoint.

[1482] 2. Saving data

[1483] The server stores the received post data in a database. For example, it records information such as product name, details, hashtags, images, and posting date and time in the "Posts" table of the database.

[1484] 3. AI-based analysis and matching

[1485] A generative AI model deployed on the server analyzes the posted data (hashtags and images). The generative AI model uses image analysis and natural language processing techniques to classify product information and extract features.

[1486] The server searches past posting data in the database based on the analysis results of the generated AI model and selects matching candidates between related users. For example, if user A posts "used books" and user B posts "I want used books," the system will match these posts.

[1487] 4. Notification of matching information

[1488] The server sends notifications to users who have been successfully matched. This allows users to confirm that a match has been made through the app's notification function.

[1489] 5. Sending and receiving messages

[1490] After receiving a notification, users can use the app's messaging function to negotiate the details of the transaction. For example, user B can send user A a message saying, "Please send me a used book."

[1491] 6. Payment Processing

[1492] The terminal prompts user B to enter payment information and sends it to the server. The server processes the payment using an external payment API. Once the payment is complete, user B is notified.

[1493] 7. Shipping the product

[1494] User A ships a product and obtains tracking information. User A sends the tracking information to the server via the app. The server stores this information in a database and notifies User B.

[1495] 8. Confirmation of receipt and evaluation

[1496] User B receives the product and confirms receipt through the app. At the same time, they rate the transaction and send it to the server. The server stores the rate information in a database and uses it as reference data for future transactions.

[1497] Specific example

[1498] For example, suppose user A posts a "used book" and wants to offer it. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generative AI model matches these posts, and the server sends notifications to both users. User B contacts user A and completes the payment of shipping and service fees. User A ships the book and provides tracking information. Once user B receives the book, they confirm receipt and rate user A. In this way, this system allows users to trade used goods efficiently and easily.

[1499] Example of a prompt

[1500] Please generate a product list that corresponds to "used books".

[1501] Please describe the procedure for analyzing posted hashtags and images to match them with relevant users.

[1502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1503] Step 1:

[1504] The user launches the app and enters product information.

[1505] Specific operation: The user enters the product name, details, hashtags, and image, and then clicks the post button.

[1506] Input: Product name, details, hashtags, image.

[1507] Output: Product information dataset. Temporarily stored on the device.

[1508] Step 2:

[1509] The device sends the entered data to the server's API endpoint.

[1510] Specific operation: The device creates an HTTP POST request and sends data to the server's API endpoint.

[1511] Input: A dataset of product information.

[1512] Output: Product information sent to the server.

[1513] Step 3:

[1514] The server receives the submitted data.

[1515] Specific operation: The server parses the HTTP request it receives and extracts the data payload.

[1516] Input: Product information sent from the device.

[1517] Output: Product information is loaded into the server's memory.

[1518] Step 4:

[1519] The server saves product information to the database.

[1520] Specific operation: The server inserts the extracted product information into the "Posts" table in the database.

[1521] Input: Data payload.

[1522] Output: A new record in the "Posts" table of the database.

[1523] Step 5:

[1524] The AI ​​model deployed on the server analyzes the submitted data.

[1525] Specific operation: The generative AI model extracts features of product information through image analysis and natural language processing, and determines its attributes.

[1526] Input: Database entry data.

[1527] Output: Analysis results of product information.

[1528] Step 6:

[1529] The server searches for relevant past posts based on the analysis results of the generated AI model.

[1530] Specific operation: The server queries the database for relevant posts that match the analysis results.

[1531] Input: Product information analysis results.

[1532] Output: Dataset of matching candidates.

[1533] Step 7:

[1534] The server selects matching candidates and sends a notification to the user.

[1535] Specific operation: The server sends a push notification to the device of the relevant matching candidate user.

[1536] Input: Dataset of matching candidates.

[1537] Output: Notification to the user.

[1538] Step 8:

[1539] The user receives a notification and begins exchanging messages.

[1540] Specific operation: The user clicks the notification and negotiates the terms of the transaction using the in-app messaging function.

[1541] Input: Notification content.

[1542] Output: The content of the message exchange.

[1543] Step 9:

[1544] The terminal prompts the user to enter payment information and sends it to the server.

[1545] Specific operation: The user enters payment information and clicks the submit button. The device sends that information to the server.

[1546] Input: Payment information.

[1547] Output: Payment information sent to the server.

[1548] Step 10:

[1549] The server processes the payment and completes the payment using an external payment API.

[1550] Specific operation: The server calls an external payment API to confirm the payment. If the transaction is successful, user B is notified.

[1551] Input: Payment information.

[1552] Output: Payment completion notification.

[1553] Step 11:

[1554] User A ships the product and enters the tracking information into the app, then sends it to the server.

[1555] Specific action: User A ships the product, obtains the tracking number, enters it into the app, and clicks the submit button.

[1556] Input: Tracking number.

[1557] Output: Tracking information sent to the server.

[1558] Step 12:

[1559] The server saves the tracking information and notifies user B.

[1560] Specific operation: The server saves the tracking information to the database and notifies user B that "the item has been shipped."

[1561] Input: Tracking information.

[1562] Output: Notification to User B.

[1563] Step 13:

[1564] User B receives the product and confirms receipt via the app.

[1565] Specific action: After the product arrives, user B opens the app and clicks the "Confirm Receipt" button.

[1566] Input: Delivered items.

[1567] Output: Receipt confirmation information.

[1568] Step 14:

[1569] User B evaluates User A and sends the evaluation to the server.

[1570] Specific action: User B enters a rating for the trading partner and clicks the submit button.

[1571] Input: Evaluation information.

[1572] Output: Evaluation information sent to the server.

[1573] Step 15:

[1574] The server saves the evaluation information to the database.

[1575] Specific operation: The server saves evaluation information to a database and uses it as reference data for future transactions.

[1576] Input: Evaluation information.

[1577] Output: A database containing evaluation information.

[1578] (Application Example 1)

[1579] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1580] Current e-commerce sites make it difficult for users to efficiently buy, sell, and exchange goods, especially matching used items. Furthermore, managing shipping, payment processing, and tracking information is cumbersome, and a system that centralizes these processes is needed. Additionally, mechanisms for user ratings and ensuring transaction security are lacking. Therefore, a platform is needed that enhances user convenience while allowing for secure transactions.

[1581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1582] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for notifying users of matching information; means for users who have been matched to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; means for confirming receipt of products and providing user ratings; and means for buying, selling, and exchanging products using a smartphone application. This enables centralized management of efficient and safe transactions of reused goods, improving user convenience and transaction reliability.

[1583] A "user" is an individual or organization that uses the system to input product information or conduct transactions.

[1584] "Product information" refers to information such as product name, details, price, and images that users enter into the system.

[1585] A "hashtag" is an identifying keyword added to product information, making it easier to search and categorize posted data.

[1586] A "photo" is an image file posted to visually supplement product information.

[1587] "Generative AI" refers to artificial intelligence technology that analyzes input data and provides relevant information.

[1588] A "database" is a system for storing and managing posted product information and transaction data.

[1589] "Matching information" refers to information about suitable trading partners presented as a result of analysis by the generating AI.

[1590] "Notifications" refer to the means by which the system communicates matching information and other important information to the user.

[1591] "Real-time" means that data is sent and received between users instantly and without delay.

[1592] A "message" is text information that users send and receive to confirm transaction details.

[1593] "Payment processing" refers to the process by which a user pays for a transaction or service fee.

[1594] "Tracking information" refers to information used to track the delivery status of a product after it has been shipped.

[1595] "Rating" refers to feedback that a user provides to their trading partner after a transaction is completed.

[1596] A "smartphone application" is software that runs on a smartphone and provides the user with the functions of the present invention.

[1597] System Configuration

[1598] To realize this application, this system includes the following main components.

[1599] Smartphone application: Provides an interface for users to enter product information and post hashtags and photos. Works on iOS and Android devices.

[1600] Generative AI Model: Uses Transformers-based AI such as BERT and GPT to analyze and match product information from hashtags and photos.

[1601] Server and Database: Responsible for backend logic. For example, using Python frameworks such as Django or Flask, and storing data in a PostgreSQL database.

[1602] External APIs: Stripe API is used for payment processing, and the shipping carrier's API is used for tracking information.

[1603] Program processing

[1604] Step 1: Enter and post product information.

[1605] Users enter product information through a smartphone app and post photos along with hashtags. This information is temporarily stored on the user's device.

[1606] Step 2: Sending and saving data

[1607] The device sends the posted data to the server. The server saves the received data to the "Posts" table in the database. The post's timestamp is also recorded at this time.

[1608] Step 3: Analysis and matching using generative AI

[1609] A generation AI installed on the server analyzes past post data in the database. The generation AI analyzes product information based on hashtags and photos and searches for related post data.

[1610] Step 4: Notification of matching information

[1611] The server notifies the user of matching information. Once the user receives the notification, they can confirm in the app that a match has been made.

[1612] Step 5: Sending and receiving messages

[1613] After receiving a notification, users use the in-app messaging function to check the transaction details. For example, a buyer might ask a seller, "Could you tell me the price of the item?"

[1614] Step 6: Payment Processing

[1615] The device receives payment information from the user and sends it to the server. The server completes the payment using an external payment API (such as the Stripe API).

[1616] Step 7: Shipping and Tracking

[1617] The seller ships the item and enters the tracking information into the app. The server saves this information to a database and notifies the buyer.

[1618] Step 8: Confirm receipt and review

[1619] The buyer receives the product and confirms receipt through the app. The buyer also rates the other party within the app, and the server stores the rating information.

[1620] Specific example

[1621] User A posts product information along with "used book" using a smartphone app. Similarly, User B posts "I want a used book." Based on this information, a generative AI model matches the two. The server notifies both users of the matching information, and Users A and B confirm the transaction details via message. User B completes the payment, and User A ships the product. Tracking information can be viewed in the app, and finally, User B receives the product, confirms receipt, and leaves a review.

[1622] Example of a prompt

[1623] "User A, who owns a used book, opens a smartphone app and posts product information with the hashtag 'used book'. Similarly, User B posts 'I want a used book'. A generative AI model analyzes these posts, recognizes that the two users match, and sends a notification. Once both users negotiate and payment is completed, User A ships the item and registers the tracking information in the app. Finally, User B receives the item, and the transaction is complete."

[1624] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1625] Step 1:

[1626] The user opens the smartphone app, enters product information (product name, details), hashtags, and a photo, and posts it. This input data is temporarily stored on the device. Specifically, the user enters data into a form for product information, takes a photo, and uploads it. The input data is converted to JSON format and sent to the server.

[1627] Step 2:

[1628] The device sends the posted data to the server's API endpoint. The server stores this data in the "Posts" table in its database. The post's timestamp is also recorded. Specifically, the device sends a POST request to the server, and the server processes the received data and stores it in the database.

[1629] Step 3:

[1630] A generative AI installed on the server analyzes the posted data in the database. The generative AI analyzes product information from the input hashtags and photos and searches for related past posted data. Specifically, an AI model (e.g., BERT, GPT) analyzes the meaning of the hashtags and uses image recognition technology (e.g., CNN) to extract information from the photos. It lists highly relevant posts as search results and returns that information to the server.

[1631] Step 4:

[1632] Based on the matching candidate data returned by the AI, the server sends notifications to relevant users. Specifically, the server sends push notifications to relevant devices to inform users of the possibility of a transaction. Users receive the notification and confirm that a match has been made in the app.

[1633] Step 5:

[1634] After a user receives a notification, they can send and receive messages in real time using the in-app chat function. Specifically, messages sent by the user are sent to the server and immediately displayed on the recipient's device. This allows for confirmation of transaction details and negotiations to take place.

[1635] Step 6:

[1636] The terminal prompts the buyer for payment information and sends this data to the server. The server uses an external payment API (e.g., Stripe API) to process the payment. Specifically, the server makes an API call and receives confirmation that the payment was successful. This information is then notified to both the buyer and the seller.

[1637] Step 7:

[1638] The seller ships the item and enters the tracking information into the app. The server saves this tracking information to a database and notifies the buyer. Specifically, the tracking number entered by the seller is sent to the server and stored in the database. Once the transmission is complete, the buyer receives a notification and can check the delivery status within the app.

[1639] Step 8:

[1640] Once the buyer receives the product, they confirm receipt through the app. Simultaneously, they rate the seller. The server receives this information and stores it in a database. Specifically, the buyer clicks a "received" button and fills out a rating form. This data is sent to the server and recorded in the database. Once the rating results are saved, they are used as reference data for future transactions.

[1641] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1642] This invention combines a system in which users input product information, post hashtags and photos to social media, and AI analyzes this information to assist in matching users with each other, with an emotion engine that recognizes the user's emotions. The program for this system is described below in natural language.

[1643] System Overview

[1644] 1. Users use devices such as smartphones and computers to input information about products they want or can offer through the app. Specifically, they input product names and details, and post product photos along with the corresponding hashtags.

[1645] 2. The terminal sends the entered product information to the server. The server stores the submitted data in a database and analyzes the information using a generation AI.

[1646] 3. The emotion engine analyzes the user's emotions from the posted text and photo data. The analyzed emotion data is an important factor in matching.

[1647] 4. The generation AI analyzes product information based on hashtags and photos, and matches related users by referring to past posting data in the database. In this process, sentiment data provided by the sentiment engine is also taken into consideration.

[1648] 5. Once a match is made, the server sends a notification to the relevant users. Users receive the notification and negotiate the transaction details via message.

[1649] 6. If the user agrees to the transaction, they will pay shipping and service fees through the app. Once payment is complete, the item will be shipped and tracking information will be managed.

[1650] 7. After the product arrives, the user confirms receipt and leaves a review, and the emotion engine is activated again during this review process.

[1651] Program processing

[1652] 1. Enter and post product information.

[1653] The user opens the app and enters product information. For example, they might post a photo with the hashtags "used books" and "used books".

[1654] The device temporarily holds the posted data and sends it to the server's API endpoint.

[1655] 2. Data Storage

[1656] The server analyzes the received post data and saves it to the "Posts" table in the database. Metadata such as the timestamp and user ID are also recorded at the same time.

[1657] 3. Emotion analysis

[1658] An emotion engine located on the server analyzes the submitted data (text and photos). This analysis classifies the user's current emotional state (e.g., joy, sadness, excitement) and generates numerical emotion data.

[1659] 4. AI-based analysis and matching

[1660] The generation AI retrieves new post data and analyzes product information from hashtags and photos. At the same time, it searches the database for relevant users, taking into account sentiment data provided by the sentiment engine.

[1661] As a concrete example, suppose user A posts "used books" and wants to offer used books. At the same time, user B posts "I want a used book" and is looking for that book. In this case, the generation AI matches user A's post with user B's post, and the emotion engine evaluates the users' emotional states to perform the optimal match.

[1662] 5. Notification of matching information

[1663] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[1664] 6. Sending and receiving messages

[1665] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[1666] 7. Payment Processing

[1667] The terminal prompts user B for payment information and sends the payment data to the server. This payment information includes credit card details and electronic payment information.

[1668] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[1669] 8. Shipping of the product

[1670] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1671] 9. Confirmation of receipt and evaluation

[1672] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[1673] The server saves receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1674] Through these detailed steps, the AI ​​reuse matching SNS efficiently manages transactions between users, and by utilizing an emotion engine, it achieves optimal matching and service provision that also takes users' emotions into consideration.

[1675] The following describes the processing flow.

[1676] Step 1:

[1677] The user opens the app and enters product information. Specifically, they enter the product name and details, and post the corresponding hashtags and product photos. For example, user A posts a photo of a book with the hashtags "used book" and "used book".

[1678] Step 2:

[1679] The terminal temporarily stores the entered product information in memory and sends a POST request to the server's API endpoint. The posted data includes the product name, hashtags, photos, and user ID.

[1680] Step 3:

[1681] The server analyzes the received post data and saves it to the "Posts" table in the database. During saving, the post's timestamp and user ID are also recorded.

[1682] Step 4:

[1683] The server's emotion engine analyzes the posted text and photo data to evaluate the user's emotional state. As a result of the analysis, it generates numerical data representing emotional states such as joy, sadness, and excitement.

[1684] Step 5:

[1685] The server's AI retrieves newly saved post data and analyzes product information based on hashtags and photos. Simultaneously, it also considers sentiment data provided by the sentiment engine and searches the database for relevant past posts.

[1686] Step 6:

[1687] The generation AI matches relevant users based on the analysis results. For example, if user A's "used books" and user B's "I want used books" match, the emotion engine also evaluates the emotional state of both users and performs the optimal match.

[1688] Step 7:

[1689] The server notifies the relevant users of the matching information. Specifically, it sends in-app notifications and push notifications to the devices of user A and user B to inform them that a match has been made.

[1690] Step 8:

[1691] The user receives a notification and uses the in-app messaging function to check the transaction details. For example, user B sends a message to user A saying, "Please send me a used book." The message is sent to the recipient's device via the server.

[1692] Step 9:

[1693] The terminal prompts user B to enter payment information and sends it to the server. This payment information includes credit card information and electronic payment information.

[1694] Step 10:

[1695] The server calls the external payment provider's API to process the payment. If the payment is successful, it records this in the database and notifies both User A and User B.

[1696] Step 11:

[1697] User A ships an item and enters the tracking number provided by the shipping company into the app. The server saves this tracking information to a database and notifies User B.

[1698] Step 12:

[1699] User B receives the product and presses the receipt confirmation button within the app to acknowledge receipt. At the same time, User B fills out and submits a form to rate User A. The emotion engine also operates during this rating process, analyzing User B's emotional state.

[1700] Step 13:

[1701] The server saves the receipt confirmation and user rating data to the "Transactions" and "Ratings" tables in the database. This completes the transaction, and the rating information serves as a reference for future user-to-user transactions.

[1702] (Example 2)

[1703] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1704] The problem this invention aims to solve is to facilitate smooth matching and transaction completion in conventional user-to-user product transactions. In particular, matching that does not take emotional factors into consideration can negatively impact transaction satisfaction and success rates. Furthermore, a system is needed to efficiently manage a series of processes such as real-time message sending and receiving, payment processing, product shipping, and receipt confirmation.

[1705] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1706] In this invention, the server includes means for users to input information about products and post hashtags and images; means for storing the posted product information in a data management device; means for analyzing product information from hashtags and images using generated artificial intelligence and searching for relevant past posts; means for notifying users of matching information; means for matching users to send and receive information in real time; means for processing payment of delivery and service fees; means for managing product delivery and tracking information; means for confirming receipt of products and providing user ratings; means for analyzing the user's emotions from posted content and images; and means for considering the analyzed emotional data in matching. This enables optimal matching that takes user emotions into account and efficient management of the entire transaction process.

[1707] "Product information" refers to data including the name, details, and features of the product that the user is providing or wants.

[1708] A hashtag is a keyword used to categorize a post and make it easier for other users to find it.

[1709] "Images" are data that includes visual information about products that a user provides or wants.

[1710] A "data management device" refers to any system used to store and manage submitted data.

[1711] "Artificial intelligence" is a technology that analyzes information about products from hashtags, images, etc., to perform optimal matching.

[1712] "Posted information" refers to data including all product information, hashtags, and images entered by the user.

[1713] "Matching information" refers to information used by AI to notify users of potential trading partners who are likely to be the best match among related users.

[1714] "Sending and receiving information" refers to a means of communication that allows users to check transaction details in real time and conduct negotiations.

[1715] "Shipping and service fees" include all costs, including the costs of shipping the goods and server usage fees.

[1716] "Tracking information" refers to data used to check the current delivery status of a shipped item.

[1717] "Receipt confirmation" is a method by which the user confirms whether the product has arrived safely and notifies the seller accordingly.

[1718] "User ratings" refer to feedback information used to evaluate the trading partner after a transaction is completed.

[1719] "Emotion" refers to the user's emotional state as interpreted from the posted text and images.

[1720] "Analyzed emotional data" refers to data that shows the user's emotional state, which has been analyzed and quantified by the emotional engine.

[1721] This invention is a system for facilitating product transactions between users, and its specific embodiment is shown in the following steps. Users access the application using a smartphone or personal computer, input product information, and post hashtags and images. The terminal temporarily stores the posted data and then sends it to the server. The server stores the received data in a data management device and further analyzes it using an emotion engine and generative AI.

[1722] The generation AI model deployed on the server analyzes the posted hashtags and images to extract information about the product. Furthermore, it searches for relevant users by referencing past posting data and performs optimal matching. In this process, the emotion engine analyzes the user's emotions from the posted content and images, and the analyzed emotional data is also taken into consideration in the matching process.

[1723] For example, if user A posts a picture with the captions "Used books" and "I'm offering used books," and user B posts a picture with the captions "I want a used book" and "I'm looking for that book," this system uses generative AI to analyze the content of both posts. At the same time, the emotion engine detects the emotional state of the users, and if, for example, user A is posting with a joyful emotion, the system prioritizes matching that user.

[1724] The server generates matching results based on this information and sends notifications to the relevant users. Users negotiate transaction details in real time using the in-app messaging function. Payment processing is handled by the server in cooperation with an external payment provider using payment data sent from the device. If successful, the information is recorded in the database and the user is notified.

[1725] Subsequently, User A ships the product and enters the tracking number into the app. This information is stored on the server and notified to User B. Once User B receives the product, they confirm receipt within the app and rate User A. The emotion engine also operates during this rating process, analyzing the user's emotional state. The receipt confirmation and rating data are stored on the server's data management system and used as reference for future transactions.

[1726] Example of a prompt:

[1727] "User A posts 'Used books' and 'I'm offering used books,' while User B posts 'I want a used book' and 'I'm looking for that book.' Explain how the AI ​​engine matches these posts."

[1728] Through the above embodiments, transactions between users can be facilitated, and by considering emotional information, it is possible to increase transaction satisfaction and success rates.

[1729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1730] Step 1:

[1731] The user launches the app and enters information about the product.

[1732] Input: Product name, details, hashtags, image.

[1733] Specific operation: The user uploads a "used book," its details, the hashtag "used book," and an image of the product to the app's input form.

[1734] Output: Product information data is generated and temporarily stored on the device.

[1735] Step 2:

[1736] The terminal sends the product information entered by the user to the server.

[1737] Input: Product information data entered by the user.

[1738] Specific operation: The terminal sends a POST request to the server's API endpoint with the temporarily stored product information data.

[1739] Output: Product information data is sent to the server.

[1740] Step 3:

[1741] The server stores the received product information in the data management device.

[1742] Input: Product information data.

[1743] Specific operation: The server analyzes the received product information data and saves it to the "Posts" table in the database. Metadata such as timestamps and user IDs are also recorded at the same time.

[1744] Output: Product information data is saved to the database.

[1745] Step 4:

[1746] The server sends product information to the emotion engine for analysis.

[1747] Input: Product information data.

[1748] Specific operation: The server sends product information data (text and images) to the emotion engine, which then analyzes the user's emotions.

[1749] Output: Numerical emotional data is generated and returned to the server.

[1750] Step 5:

[1751] The generation AI analyzes product information, searches for related past posts, and performs matching.

[1752] Input: Product information data, past post data, emotional data.

[1753] Specific operation: The generating AI analyzes hashtags and images from product information data and searches past posting data. Furthermore, it considers emotional data to select the most suitable matching candidates.

[1754] Output: Information on the best matching candidates.

[1755] Step 6:

[1756] The server notifies relevant users of the matching information.

[1757] Input: Matching candidate information.

[1758] Specific operation: Based on the matching information, the server sends in-app notifications and push notifications to related users (for example, user A and user B).

[1759] Output: Matching information is sent to both User A and User B.

[1760] Step 7:

[1761] The user checks the notification and uses the in-app messaging function to view the transaction details.

[1762] Input: Matching notification.

[1763] Specific action: User A and User B discuss the transaction details using the app's messaging function. For example, User B sends User A the message, "Please send me a used book."

[1764] Output: Transaction details message.

[1765] Step 8:

[1766] The terminal prompts the user to enter payment information and sends it to the server.

[1767] Input: Payment information (credit card information, electronic payment information).

[1768] Specific operation: User B enters payment information into the app, and the device sends that data to the server.

[1769] Output: Payment data is sent to the server.

[1770] Step 9:

[1771] The server calls the payment provider's API to process the payment.

[1772] Input: Payment data.

[1773] Specific operation: The server calls an external payment provider API to process the payment, records the information in the database if successful, and notifies the user.

[1774] Output: Payment confirmation notice; payment information is recorded in the database.

[1775] Step 10:

[1776] User A ships the product and enters the tracking number into the app.

[1777] Input: Tracking number.

[1778] Specific operation: User A hands over the product to the delivery company and enters the received tracking number into the app. The server saves this information to the database and notifies User B.

[1779] Output: Tracking information is saved to the database and user B is notified.

[1780] Step 11:

[1781] User B receives the product and confirms receipt.

[1782] Input: Press the "Confirm Receipt" button.

[1783] Specific action: User B receives the product and presses the "Confirm Receipt" button within the app. At the same time, a form for User A to enter their review appears.

[1784] Output: Receipt confirmation data and evaluation data are sent to the server.

[1785] Step 12:

[1786] The server saves receipt confirmation and evaluation data to the database, and the transaction is completed.

[1787] Input: Receipt confirmation data, evaluation data.

[1788] Specific operation: The server saves the receipt confirmation data and evaluation data to the data management device, indicating that the transaction has been completed.

[1789] Output: Transaction completion data is saved to the database.

[1790] Through these steps, the system can efficiently manage the entire transaction process, including optimal matching that takes user emotions into consideration.

[1791] (Application Example 2)

[1792] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1793] Traditional e-commerce sites often resulted in unsatisfactory transactions because users could not fully understand the emotions and needs of the other party when purchasing or listing products. Furthermore, matching based solely on simple product information was problematic because it failed to provide optimal suggestions tailored to the user's emotions and circumstances.

[1794] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1795] In this invention, the server includes means for users to input product information and post hashtags and photos; means for storing the posted product information in a database; means for analyzing product information from hashtags and photos using a generation AI and searching for related past posting data; means for generating emotion data using an emotion engine that analyzes user emotions; means for the generation AI to suggest the most suitable products and sellers considering the user's emotion data; means for notifying users of matching information; means for matching users to send and receive messages in real time; means for processing payment of shipping and service fees; means for managing product shipping and tracking information; and means for confirming receipt of products and performing user evaluations. This makes it possible to perform optimal matching that takes user emotions into consideration and realize highly satisfying transactions.

[1796] A "user" is an individual or legal entity that uses the system to purchase or list goods for sale.

[1797] "Product information" refers to information including the name, details, and related data such as hashtags and photos of the product that the user wishes to list or purchase.

[1798] A "hashtag" is a keyword used to identify the characteristics or category of a product, making it easier to search for information on social media and search engines.

[1799] "Generative AI" refers to an algorithm that uses artificial intelligence technology to analyze product information from posted hashtags and photos, searches for relevant past posting data, and performs optimal matching.

[1800] An "emotion engine" refers to a module or technology that analyzes emotional data from user-submitted text and photos and quantifies emotional states.

[1801] A "database" is a digital storage system used to centrally manage and store posted product information, sentiment data, past transaction data, and other similar information.

[1802] "Matching information" refers to information about potential trading partners between users, derived by a generative AI and an emotion engine.

[1803] "Real-time" means that messages are sent and received and information is exchanged between users instantly.

[1804] "Shipping and service fees" refer to the costs of delivering goods through logistics and various expenses related to the use of the system.

[1805] "Tracking information" refers to information that shows where a product is passing through during delivery and what stage it is currently in.

[1806] "Receipt confirmation" refers to the process of confirming that the product has been delivered to the buyer and finalizing that status within the system.

[1807] "User ratings" refer to feedback information used by other users to evaluate the quality and satisfaction level of a transaction based on its outcome.

[1808] "Notifications" refer to messages or alerts sent to inform users about successful matches or the progress of transactions.

[1809] The system that implements this application example includes means for users to input product information and post hashtags and photos, means for saving the posted product information to a database, means for analyzing product information from hashtags and photos using a generative AI and searching for related past posting data, means for generating sentiment data using an sentiment engine that analyzes the user's emotions, means for the generative AI to suggest the most suitable products and sellers considering the user's sentiment data, and means for notifying the user of matching information.

[1810] The server receives product information entered by users using devices such as smartphones and personal computers, and stores this information in a database. The database includes the product name, details, related hashtags, and photos. Next, the server uses generative AI to analyze the product information from hashtags and photos and searches for related past posts. A generative AI model is used for this process.

[1811] Furthermore, the server is equipped with an emotion engine that analyzes the user's emotions from the posted text and photos. The emotion engine quantifies the user's emotional state and stores the generated emotion data in a database. The generating AI also refers to this emotion data to suggest the most suitable products and sellers to the user.

[1812] This system also includes a means for notifying users of matching information and for matching users to send and receive messages in real time. Once a transaction is completed, the server processes payment of shipping and service fees and manages product shipping and tracking information. Finally, after the product arrives, it confirms receipt and provides user feedback, storing this information in a database.

[1813] As a concrete example, consider a case where user A posts a photo from their smartphone with the hashtags "old manga" and "old manga." Suppose user B posts "I want old manga," sending information that they are looking for old manga. The emotion engine analyzes that user A has the emotion of "nostalgia," while user B has the emotion of "excitement." The generative AI model matches the two in the most optimal way and notifies the user of the result.

[1814] Example of a prompt:

[1815] "Enter product information and post a photo with the hashtag #oldcomicbooks. Our emotional AI will analyze your emotional state and suggest the best buyer / seller."

[1816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1817] Step 1:

[1818] Users input product information via their smartphones or computers. Specifically, they input and post the product name, product details text, and related hashtags and photos. The input data, including the product name "Old Comic Books," the hashtag "Old Comic Books," and the photo file, is sent to the server. The device temporarily stores this data and sends a POST request to the server's API endpoint.

[1819] Step 2:

[1820] The server parses the received post data and stores it in the database. The stored data includes the product name, details, hashtags, the path to the photo file, and the poster's user ID and timestamp. At this stage, the input data is converted to the appropriate format and registered in the "Posts" table of the database.

[1821] Step 3:

[1822] An emotion engine deployed on the server analyzes the submitted data (text and photos). The input data consists of the product description and photos. The emotion engine uses natural language processing (NLP) and image recognition technology to quantify the user's emotional state (e.g., "nostalgic" or "excited"). The analysis results in quantified emotion data.

[1823] Step 4:

[1824] The generative AI model acquires new post data and analyzes product information based on hashtags and photos. Furthermore, it considers sentiment data provided by the sentiment engine to perform optimal user matching. Input data includes user sentiment data, product information, and historical related data. Based on this data, the generative AI identifies the most suitable sellers and buyers and generates matching results.

[1825] Step 5:

[1826] The server notifies the relevant users of the generated matching results. The device is notified of the successful match via in-app notifications or push notifications. The notification includes a brief profile of the other user and the reason for the match.

[1827] Step 6:

[1828] Users receive notifications and review and negotiate transaction details through the in-app messaging function. Specific input data includes message text and conversation history. The server manages this message data, enabling real-time sending and receiving.

[1829] Step 7:

[1830] Once a transaction is completed, shipping and service fees are paid through the buyer's device. The input data includes payment information (credit card or electronic payment information), and the server calls the API of an external payment provider to process the payment. A confirmation message is generated as output if the payment is successful.

[1831] Step 8:

[1832] The seller ships the item via their device, and the tracking number provided by the shipping carrier is entered into the app. The server stores this tracking information in a database and notifies the buyer. The input data includes the tracking number and the shipping timestamp.

[1833] Step 9:

[1834] Once the buyer receives the item and presses the "Received" button, the receipt of the item is confirmed. Furthermore, the buyer provides feedback to the seller. The emotion engine also operates during the feedback process, analyzing the user's emotional state. This process saves the receipt confirmation and feedback information to a database.

[1835] By following these steps, a system is created that optimizes matching while considering user emotions, thereby improving transaction satisfaction.

[1836] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1837] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1838] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1839] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1840] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1841] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1842] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1843] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1844] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1845] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1846] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1847] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1848] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1849] 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.

[1850] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1851] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1852] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1853] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1854] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1855] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1856] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1857] The following is further disclosed regarding the embodiments described above.

[1858] (Claim 1)

[1859] A means for users to enter product information and post hashtags and photos,

[1860] A means of saving posted product information to a database,

[1861] A method for analyzing product information from hashtags and photos using generation AI and searching for related past posting data,

[1862] A means of notifying users of matching information,

[1863] A means for matching users to send and receive messages in real time,

[1864] Means for processing payment of shipping and service fees,

[1865] A means of managing product shipping and tracking information,

[1866] A means of confirming receipt of the product and providing user feedback,

[1867] A system that includes this.

[1868] (Claim 2)

[1869] The system according to claim 1, which analyzes product information using a generation AI and also refers to past posting data.

[1870] (Claim 3)

[1871] The system according to claim 1, which provides a function for users to exchange messages with each other in real time.

[1872] "Example 1"

[1873] (Claim 1)

[1874] A means for users to enter product information and post hashtags and images,

[1875] A means of saving posted product information to a database,

[1876] A method for analyzing product information from hashtags and images using a generative AI model, and for searching related past posting data,

[1877] A means of notifying users of matching information,

[1878] A means for matching users to send and receive messages in real time,

[1879] Means for processing payment of shipping and service fees,

[1880] A means of managing product shipping and tracking data,

[1881] A means of confirming receipt of the product and providing user feedback,

[1882] A system that includes this.

[1883] (Claim 2)

[1884] The system according to claim 1, which analyzes product information using a generative AI model and also refers to past posting data.

[1885] (Claim 3)

[1886] The system according to claim 1, which provides a function for users to exchange messages with each other in real time.

[1887] "Application Example 1"

[1888] (Claim 1)

[1889] A means for users to enter product information and post hashtags and photos,

[1890] A means of saving posted product information to a database,

[1891] A method for analyzing product information from hashtags and photos using generation AI and searching for related past posting data,

[1892] A means of notifying users of matching information,

[1893] A means for matching users to send and receive messages in real time,

[1894] Means for processing payment of shipping and service fees,

[1895] A means of managing product shipping and tracking information,

[1896] A means of confirming receipt of the product and providing user feedback,

[1897] A means of buying, selling, and exchanging goods using a smartphone application,

[1898] A system that includes this.

[1899] (Claim 2)

[1900] The system according to claim 1, which analyzes product information using a generation AI and also refers to past posting data.

[1901] (Claim 3)

[1902] The system according to claim 1, which provides a function for users to exchange messages with each other in real time.

[1903] "Example 2 of combining an emotion engine"

[1904] (Claim 1)

[1905] A means for users to enter information about products and post hashtags and images,

[1906] A means for storing information about posted products in a data management device,

[1907] A means of analyzing product information from hashtags and images using generated artificial intelligence, and searching for related past posts,

[1908] A means of notifying users of matching information,

[1909] A means for matching users to send and receive information in real time,

[1910] Means for processing payment of delivery and service fees,

[1911] A means of managing product delivery and tracking information,

[1912] A means of confirming receipt of goods and providing user feedback,

[1913] A means of analyzing the user's emotions from the posted content and images,

[1914] A means of considering the analyzed emotional data in the matching process,

[1915] A system that includes this.

[1916] (Claim 2)

[1917] The system according to claim 1, which uses generated artificial intelligence to analyze information about products and also refers to past posting information.

[1918] (Claim 3)

[1919] The system according to claim 1, which provides a function for users to exchange information with each other in real time.

[1920] "Application example 2 when combining with an emotional engine"

[1921] (Claim 1)

[1922] A means for users to enter product information and post hashtags and photos,

[1923] A means of saving posted product information to a database,

[1924] A method for analyzing product information from hashtags and photos using generation AI and searching for related past posting data,

[1925] A means of generating emotional data using an emotion engine that analyzes user emotions,

[1926] A method in which a generating AI suggests the most suitable products and sellers by taking into account user sentiment data,

[1927] A means of notifying users of matching information,

[1928] A means for matching users to send and receive messages in real time,

[1929] Means for processing payment of shipping and service fees,

[1930] A means of managing product shipping and tracking information,

[1931] A means of confirming receipt of the product and providing user feedback,

[1932] A system that includes this.

[1933] (Claim 2)

[1934] The system according to claim 1, which analyzes product information using a generation AI, refers to past posting data, and proposes the most suitable product while considering user sentiment data.

[1935] (Claim 3)

[1936] The system according to claim 1, which provides a function for users to exchange messages in real time and performs optimal matching based on sentiment data. [Explanation of Symbols]

[1937] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to enter product information and post hashtags and photos, A means of saving posted product information to a database, A method for analyzing product information from hashtags and photos using generation AI and searching for related past posting data, A means of notifying users of matching information, A means for matching users to send and receive messages in real time, Means for processing payment of shipping and service fees, A means of managing product shipping and tracking information, A means of confirming receipt of the product and providing user feedback, A system that includes this.

2. The system according to claim 1, which uses a generation AI to analyze product information and also refers to past posting data.

3. The system according to claim 1, which provides a function for users to exchange messages with each other in real time.

Images